diff --git a/.Rbuildignore b/.Rbuildignore deleted file mode 100644 index 2999478..0000000 --- a/.Rbuildignore +++ /dev/null @@ -1,9 +0,0 @@ -^.*\.Rproj$ -^\.Rproj\.user$ -^LICENSE\.md$ -^\.git* -^tests/testthat/mplusResults* -sketches -^_pkgdown\.yml$ -^docs$ -^pkgdown$ diff --git a/404.html b/404.html new file mode 100644 index 0000000..b7cd858 --- /dev/null +++ b/404.html @@ -0,0 +1,88 @@ + + + + + + + +Page not found (404) • modsem + + + + + + + + Skip to contents + + +
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+ + + + + + + diff --git a/DESCRIPTION b/DESCRIPTION deleted file mode 100644 index 106287c..0000000 --- a/DESCRIPTION +++ /dev/null @@ -1,62 +0,0 @@ -Package: modsem -Type: Package -Title: Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM) -Version: 1.0.2 -Authors@R: - person(given = "Kjell", family = "Solem Slupphaug", - email = "slupphaugkjell@gmail.com", role = c("aut", "cre"), - comment = c(ORCID = "0009-0005-8324-2834")) -Maintainer: Kjell Solem Slupphaug -Description: - Estimation of interaction (i.e., moderation) effects between latent variables - in structural equation models (SEM). - The supported methods are: - The constrained approach (Algina & Moulder, 2001). - The unconstrained approach (Marsh et al., 2004). - The residual centering approach (Little et al., 2006). - The double centering approach (Lin et al., 2010). - The latent moderated structural equations (LMS) approach (Klein & Moosbrugger, 2000). - The quasi-maximum likelihood (QML) approach (Klein & Muthén, 2007) (temporarily unavailable) - The constrained- unconstrained, residual- and double centering- approaches - are estimated via 'lavaan' (Rosseel, 2012), whilst the LMS- and QML- approaches - are estimated via by modsem it self. Alternatively model can be - estimated via 'Mplus' (Muthén & Muthén, 1998-2017). - References: - Algina, J., & Moulder, B. C. (2001). - . - "A note on estimating the Jöreskog-Yang model for latent variable interaction using 'LISREL' 8.3." - Klein, A., & Moosbrugger, H. (2000). - . - "Maximum likelihood estimation of latent interaction effects with the LMS method." - Klein, A. G., & Muthén, B. O. (2007). - . - "Quasi-maximum likelihood estimation of structural equation models with multiple interaction and quadratic effects." - Lin, G. C., Wen, Z., Marsh, H. W., & Lin, H. S. (2010). - . - "Structural equation models of latent interactions: Clarification of orthogonalizing and double-mean-centering strategies." - Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). - . - "On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables." - Marsh, H. W., Wen, Z., & Hau, K. T. (2004). - . - "Structural equation models of latent interactions: evaluation of alternative estimation strategies and indicator construction." - Muthén, L.K. and Muthén, B.O. (1998-2017). - "'Mplus' User’s Guide. Eighth Edition." - . - Rosseel Y (2012). - . - "'lavaan': An R Package for Structural Equation Modeling." -License: MIT + file LICENSE -Encoding: UTF-8 -LazyData: true -RoxygenNote: 7.3.2 -LinkingTo: Rcpp, RcppArmadillo -Imports: Rcpp, purrr, stringr, lavaan, rlang, MplusAutomation, nlme, dplyr, - mvnfast, stats, fastGHQuad, mvtnorm, ggplot2, parallel -Depends: - R (>= 3.50) -URL: https://github.com/Kss2k/modsem -Suggests: - knitr, - rmarkdown -VignetteBuilder: knitr diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 6527f18..0000000 --- a/LICENSE +++ /dev/null @@ -1,2 +0,0 @@ -YEAR: 2024 -COPYRIGHT HOLDER: modsem authors diff --git a/LICENSE-text.html b/LICENSE-text.html new file mode 100644 index 0000000..713eb32 --- /dev/null +++ b/LICENSE-text.html @@ -0,0 +1,68 @@ + +License • modsem + Skip to contents + + +
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License

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YEAR: 2024
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Developed by Kjell Solem Slupphaug.

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Site built with pkgdown 2.1.0.

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+ + + + + + + diff --git a/LICENSE.html b/LICENSE.html new file mode 100644 index 0000000..78d9392 --- /dev/null +++ b/LICENSE.html @@ -0,0 +1,72 @@ + +MIT License • modsem + Skip to contents + + +
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MIT License

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Copyright (c) 2024 modsem authors

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Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

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THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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Developed by Kjell Solem Slupphaug.

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Site built with pkgdown 2.1.0.

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+ + + + + + + diff --git a/LICENSE.md b/LICENSE.md deleted file mode 100644 index ad37b1d..0000000 --- a/LICENSE.md +++ /dev/null @@ -1,21 +0,0 @@ -# MIT License - -Copyright (c) 2024 modsem authors - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/NAMESPACE b/NAMESPACE deleted file mode 100644 index b09b0c7..0000000 --- a/NAMESPACE +++ /dev/null @@ -1,81 +0,0 @@ -# Generated by roxygen2: do not edit by hand - -S3method(as.character,LavToken) -S3method(as.character,matrix) -S3method(as.logical,matrix) -S3method(assignSubClass,LavClosure) -S3method(assignSubClass,LavMathExpr) -S3method(assignSubClass,LavName) -S3method(assignSubClass,LavNumeric) -S3method(assignSubClass,LavOperator) -S3method(assignSubClass,LavToken) -S3method(coef,modsem_da) -S3method(coef,modsem_pi) -S3method(coefficients,modsem_da) -S3method(coefficients,modsem_pi) -S3method(evalToken,LavAdd) -S3method(evalToken,LavBlank) -S3method(evalToken,LavComment) -S3method(evalToken,LavFunction) -S3method(evalToken,LavInteraction) -S3method(evalToken,LavModify) -S3method(evalToken,LavOperator) -S3method(evalToken,LavSeperator) -S3method(evalToken,LavToken) -S3method(evalToken,LeftBracket) -S3method(evalToken,RightBracket) -S3method(fitsToken,LavBlank) -S3method(fitsToken,LavClosure) -S3method(fitsToken,LavComment) -S3method(fitsToken,LavName) -S3method(fitsToken,LavNumeric) -S3method(fitsToken,LavOperator) -S3method(fitsToken,LavString) -S3method(modsem_inspect,modsem_da) -S3method(modsem_inspect,modsem_pi) -S3method(parameter_estimates,lavaan) -S3method(parameter_estimates,modsem_da) -S3method(parameter_estimates,modsem_mplus) -S3method(parameter_estimates,modsem_pi) -S3method(print,modsem_da) -S3method(print,modsem_mplus) -S3method(print,summary_da) -S3method(print,summary_mplus) -S3method(standardized_estimates,data.frame) -S3method(standardized_estimates,modsem_da) -S3method(standardized_estimates,modsem_mplus) -S3method(standardized_estimates,modsem_pi) -S3method(summary,modsem_da) -S3method(summary,modsem_mplus) -S3method(summary,modsem_pi) -S3method(var_interactions,data.frame) -S3method(var_interactions,modsem_da) -S3method(var_interactions,modsem_mplus) -S3method(vcov,modsem_da) -S3method(vcov,modsem_pi) -export(coef_modsem_da) -export(compare_fit) -export(default_settings_da) -export(default_settings_pi) -export(extract_lavaan) -export(fit_modsem_da) -export(get_pi_data) -export(get_pi_syntax) -export(modsem) -export(modsem_da) -export(modsem_inspect) -export(modsem_mplus) -export(modsem_pi) -export(modsemify) -export(multiplyIndicatorsCpp) -export(parameter_estimates) -export(plot_interaction) -export(standardized_estimates) -export(trace_path) -export(var_interactions) -export(vcov_modsem_da) -importFrom(Rcpp,sourceCpp) -importFrom(stats,coef) -importFrom(stats,coefficients) -importFrom(stats,vcov) -useDynLib(modsem, .registration = TRUE) diff --git a/R/RcppExports.R b/R/RcppExports.R deleted file mode 100644 index 64d353c..0000000 --- a/R/RcppExports.R +++ /dev/null @@ -1,51 +0,0 @@ -# Generated by using Rcpp::compileAttributes() -> do not edit by hand -# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 - -muLmsCpp <- function(model, z) { - .Call(`_modsem_muLmsCpp`, model, z) -} - -sigmaLmsCpp <- function(model, z) { - .Call(`_modsem_sigmaLmsCpp`, model, z) -} - -muQmlCpp <- function(m, t) { - .Call(`_modsem_muQmlCpp`, m, t) -} - -sigmaQmlCpp <- function(m, t) { - .Call(`_modsem_sigmaQmlCpp`, m, t) -} - -calcKronXi <- function(m, t) { - .Call(`_modsem_calcKronXi`, m, t) -} - -calcBinvCpp <- function(m, t) { - .Call(`_modsem_calcBinvCpp`, m, t) -} - -dnormCpp <- function(x, mu, sigma) { - .Call(`_modsem_dnormCpp`, x, mu, sigma) -} - -varZCpp <- function(Omega, Sigma1, numEta) { - .Call(`_modsem_varZCpp`, Omega, Sigma1, numEta) -} - -#' Multiply indicators -#' @param df A data DataFrame -#' @return A NumericVector -#' @export -multiplyIndicatorsCpp <- function(df) { - .Call(`_modsem_multiplyIndicatorsCpp`, df) -} - -rep_dmvnorm <- function(x, expected, sigma, t) { - .Call(`_modsem_rep_dmvnorm`, x, expected, sigma, t) -} - -dmvnrm_arma_mc <- function(x, mean, sigma, logd = TRUE) { - .Call(`_modsem_dmvnrm_arma_mc`, x, mean, sigma, logd) -} - diff --git a/R/calc_se_da.R b/R/calc_se_da.R deleted file mode 100644 index 8afd5d1..0000000 --- a/R/calc_se_da.R +++ /dev/null @@ -1,203 +0,0 @@ -calcFIM_da <- function(model, - finalModel, - theta, - data = NULL, - method = "lms", - calc.se = TRUE, - FIM = "observed", - robust.se = FALSE, - P = NULL, - hessian = FALSE, - EFIM.parametric = TRUE, - NA__ = -999, - EFIM.S = 3e4, - epsilon = 1e-8, - verbose = FALSE) { - if (!calc.se) return(list(FIM = NULL, vcov = NULL, type = "none")) - if (verbose) cat("Calculating standard errors\n") - - I <- switch(method, - lms = - switch(FIM, - observed = calcOFIM_LMS(model, theta = theta, data = data, - epsilon = epsilon, hessian = hessian), - expected = calcEFIM_LMS(model, finalModel = finalModel, - theta = theta, data = data, epsilon = epsilon, - S = EFIM.S, parametric = EFIM.parametric), - stop2("FIM must be either expected or observed")), - qml = - switch(FIM, - observed = calcOFIM_QML(model, theta = theta, data = data, - hessian = hessian, epsilon = epsilon), - expected = calcEFIM_QML(model, finalModel = finalModel, - theta = theta, data = data, epsilon = epsilon, - S = EFIM.S, parametric = EFIM.parametric), - stop2("FIM must be either expected or observed")), - stop2("Unrecognized method: ", method) - ) - - - if (robust.se) { - if (hessian && FIM == "observed") - warning("'robust.se = TRUE' should not be paired with 'EFIM.hessian = TRUE' && 'FIM = \"observed\"'") - H <- calcHessian(model, theta = theta, data = data, method = method, - epsilon = epsilon) - invH <- solveFIM(H, NA__ = NA__) - vcov <- invH %*% I %*% invH - - } else { - vcov <- solveFIM(I, NA__ = NA__) - } - - lavLabels <- model$lavLabels - dimnames(I) <- dimnames(vcov) <- list(lavLabels, lavLabels) - - list(FIM = I, vcov = vcov, type = FIM) -} - -calcHessian <- function(model, theta, data, method = "lms", - epsilon = 1e-8) { - if (method == "lms") { - P <- estepLms(model, theta = theta, data = data) - # negative hessian (sign = -1) - H <- nlme::fdHess(pars = theta, fun = logLikLms, model = model, - data = data, P = P, sign = -1, - .relStep = .Machine$double.eps^(1/5))$Hessian - - } else if (method == "qml") { - # negative hessian (sign = -1) - H <- nlme::fdHess(pars = theta, fun = logLikQml, model = model, sign = -1, - .relStep = .Machine$double.eps^(1/5))$Hessian - } - - H -} - - -solveFIM <- function(H, NA__ = -999) { - tryCatch(solve(H), - error = function(e) { - H[TRUE] <- NA__ - H - }, - warning = function(w) - if (grepl("NaN", conditionMessage(w))) suppressWarnings(solve(H)) else solve(H) - ) -} - - -calcSE_da <- function(calc.se = TRUE, vcov, theta, NA__ = -999) { - if (!calc.se) return(rep(NA__, length(theta))) - if (is.null(vcov)) { - warning2("Fisher Information Matrix (FIM) was not calculated, ", - "unable to compute standard errors") - return(rep(NA__, length(theta))) - } - - se <- suppressWarnings(sqrt(diag(vcov))) - - if (all(is.na(se))) - warning2("SE's could not be computed, negative Hessian is singular.") - if (any(is.nan(se))) - warning2("SE's for some coefficients could not be computed.") - - if (!is.null(names(se))) names(se) <- names(theta) - se[is.na(se)] <- NA__ - se -} - - -calcOFIM_LMS <- function(model, theta, data, hessian = FALSE, - epsilon = 1e-6) { - N <- nrow(data) - P <- estepLms(model, theta = theta, data = data) - if (hessian) { - # negative hessian (sign = -1) - I <- nlme::fdHess(pars = theta, fun = logLikLms, model = model, - data = data, P = P, sign = -1, - .relStep = .Machine$double.eps^(1/5))$Hessian - return(I) - } - J <- gradientLogLikLms_i(theta, model = model, data = data, - P = P, sign = 1, epsilon = epsilon) - I <- matrix(0, nrow = length(theta), ncol = length(theta)) - for (i in seq_len(N)) I <- I + J[i, ] %*% t(J[i, ]) - - I -} - - -calcEFIM_LMS <- function(model, finalModel = NULL, theta, data, S = 3e4, - parametric = TRUE, epsilon = 1e-6) { - N <- nrow(data) - - if (parametric) { - if (is.null(finalModel)) stop2("finalModel must be included in calcEFIM_LMS") - parTable <- modelToParTable(finalModel, method = "lms") - population <- tryCatch( - simulateDataParTable(parTable, N = S, colsOVs = colnames(data))$oV, - error = function(e) { - warning2("Unable to simulate data for EFIM, using stochastic sampling instead") - data[sample(N, S, replace = TRUE), ] - }) - - } else { - population <- data[sample(N, S, replace = TRUE), ] - } - - P <- estepLms(model, theta, data = population) - J <- gradientLogLikLms_i(theta, model = model, data = population, - P = P, sign = 1, epsilon = epsilon) - - I <- matrix(0, nrow = length(theta), ncol = length(theta)) - for (i in seq_len(S)) I <- I + J[i, ] %*% t(J[i, ]) - - I / (S / N) -} - - -calcOFIM_QML <- function(model, theta, data, hessian = FALSE, - epsilon = 1e-8) { - N <- nrow(model$data) - if (hessian) { - # negative hessian (sign = -1) - I <- nlme::fdHess(pars = theta, fun = logLikQml, model = model, - sign = -1, .relStep = .Machine$double.eps^(1/5))$Hessian - return(I) - } - - J <- gradientLogLikQml_i(theta, model = model, sign = 1, - epsilon = epsilon) - I <- matrix(0, nrow = length(theta), ncol = length(theta)) - for (i in seq_len(N)) I <- I + J[i, ] %*% t(J[i, ]) - - I -} - - -calcEFIM_QML <- function(model, finalModel = NULL, theta, data, S = 3e4, - parametric = TRUE, epsilon = 1e-8) { - N <- nrow(model$data) - - if (parametric) { - if (is.null(finalModel)) stop2("finalModel must be included in calcEFIM_QML") - parTable <- modelToParTable(finalModel, method = "qml") - population <- tryCatch( - simulateDataParTable(parTable, N = S, colsOVs = colnames(data))$oV, - error = function(e) { - warning2("Unable to simulate data for EFIM, using stochastic sampling instead") - data[sample(N, S, replace = TRUE), ] - }) - model$data <- population - } else { - model$data <- data[sample(N, S, replace = TRUE), ] - } - - J <- gradientLogLikQml_i(theta, model = model, sign = 1, - epsilon = epsilon) - - I <- matrix(0, nrow = length(theta), ncol = length(theta)) - for (i in seq_len(S)) I <- I + J[i, ] %*% t(J[i, ]) - - I / (S / N) -} diff --git a/R/constraints_pi_ca.R b/R/constraints_pi_ca.R deleted file mode 100644 index aa96c5b..0000000 --- a/R/constraints_pi_ca.R +++ /dev/null @@ -1,212 +0,0 @@ -# Functitions for specifying constraints in the constrained approach -# modsem(method = "ca"). Last updated: 29.05.2024 - - -labelFactorLoadings <- function(parTable) { - # Firstly Label factor loadings in Partable - loadingLabels <- apply(parTable[parTable$op == "=~", c("rhs", "lhs")], - MARGIN = 1, - FUN = function(x) createLabelLambda(x[1], x[2])) - parTable[parTable$op == "=~", "mod"] <- loadingLabels - parTable -} - - -specifyFactorLoadingsSingle <- function(parTable, relDf) { - latentProdName <- stringr::str_c(rownames(relDf), collapse = "") - for (indProd in colnames(relDf)) { - indProdLabel <- createLabelLambda(indProd, latentProdName) - indsInProdLabels <- createLabelLambda(relDf[[indProd]], rownames(relDf)) - vecLhsRhs <- c(indProdLabel, stringr::str_c(indsInProdLabels, - collapse = " * ")) - newRow <- createParTableRow(vecLhsRhs, op = "==") - parTable <- rbind(parTable, newRow) - } - parTable -} - - -specifyFactorLoadings <- function(parTable, relDfs) { - parTable <- specifyFactorLoadingsSingle(parTable, relDfs[[1]]) - if (length(relDfs) <= 1) return(parTable) - specifyFactorLoadings(parTable, relDfs[-1]) -} - - -addVariances <- function(pt) { - # Add variance-labels if missing for latents - latents <- unique(pt[pt$op == "=~", "lhs"]) - if (length(latents) == 0) return(pt) - - specifiedLvs <- pt[pt$lhs %in% latents & - pt$op == "~~" & - pt$rhs %in% latents & - pt$lhs == pt$rhs, "lhs"] |> unique() - toBeSpecifiedLvs <- latents[!latents %in% specifiedLvs] - - newRows <- lapply(toBeSpecifiedLvs, FUN = function(x) - createParTableRow(c(x, x), op = "~~")) |> - purrr::list_rbind() - pt <- rbind(pt, newRows) - - # Add variance for observed variables if missing - observed <- unique(pt[pt$op == "=~", "rhs"]) - - specifiedOvs <- pt[pt$lhs %in% observed & - pt$op == "~~" & - pt$rhs %in% observed & - pt$lhs == pt$rhs, "lhs"] |> unique() - toBeSpecifiedOvs <- observed[!observed %in% specifiedOvs] - - newRows <- lapply(toBeSpecifiedOvs, - FUN = function(x) createParTableRow(c(x, x), op = "~~")) |> - purrr::list_rbind() - rbind(pt, newRows) -} - - -addCovariances <- function(pt) { - # Add covariances for exogenous variables if missing - latents <- unique(pt[pt$op == "=~", "lhs"]) - if (length(latents) == 0) return(pt) - - combos <- getUniqueCombos(latents) - combos$connected <- !is.na(apply(combos, MARGIN = 1, function(xy) - trace_path(pt, xy[[1]], xy[[2]]))) - toBeSpecified <- combos[!combos$connected, c("V1", "V2")] - newRows <- apply(toBeSpecified[c("V1", "V2")], - MARGIN = 1, - FUN = function(x) createParTableRow(x, op = "~~")) |> - purrr::list_rbind() - rbind(pt, newRows) -} - - -labelParameters <- function(pt) { - latents <- unique(pt[pt$op == "=~", "lhs"]) - endogenous <- latents[latents %in% pt[pt$op == "~", "lhs"]] - exogenous <- latents[!latents %in% endogenous] - observed <- unique(pt[pt$op == "=~", "rhs"]) - - # Gamma - pt[pt$op == "~" & pt$rhs != "1", "mod"] <- - apply(pt[pt$op == "~" & pt$rhs != "1", c("rhs", "lhs")], - MARGIN = 1, FUN = function(x) - createLabelGamma(x[[1]], x[[2]])) - - # Variances of exogenous - pt[pt$op == "~~" & pt$lhs == pt$rhs & pt$lhs %in% exogenous, "mod"] <- - vapply(pt[pt$op == "~~" & pt$lhs == pt$rhs & - pt$lhs %in% exogenous, "lhs"], - FUN.VALUE = vector("character", length = 1L), - FUN = createLabelVar) - - # Variances of endogenous - pt[pt$op == "~~" & pt$lhs == pt$rhs & pt$lhs %in% endogenous, "mod"] <- - vapply(pt[pt$op == "~~" & pt$lhs == pt$rhs & - pt$lhs %in% endogenous, "lhs"], - FUN.VALUE = vector("character", length = 1L), - FUN = createLabelZeta) - - # Variance of Observed Variables - pt[pt$op == "~~" & pt$rhs %in% observed & pt$lhs == pt$rhs, "mod"] <- - vapply(pt[pt$op == "~~" & pt$rhs %in% observed & - pt$lhs == pt$rhs, "rhs"], - FUN.VALUE = vector("character", length = 1L), - FUN = createLabelVar) - - # Covariances - pt[pt$op == "~~" & pt$lhs != pt$rhs, "mod"] <- - apply(pt[pt$op == "~~" & pt$lhs != pt$rhs, c("lhs", "rhs")], - MARGIN = 1, FUN = function(x) - createLabelCov(x[[1]], x[[2]])) - - pt -} - - -specifyVarCovSingle <- function(parTable, relDf) { - # This function specifies variances for latents, indicators, - # and indicator products. It will also specifies covariances for latent - # products, and elements int those products. - if (nrow(relDf) > 2) { - stop2("Constraints for products with more than two ", - " elements are not supported for this method") - } - # General info - elemsInProdTerm <- rownames(relDf) - latentProd <- stringr::str_c(rownames(relDf), collapse = "") - - # Variance of latent product - labelLatentProd <- createLabelVar(latentProd) - labelsElemsInProd <- vapply(elemsInProdTerm, - FUN.VALUE = vector("character", length = 1L), - FUN = function(x) trace_path(parTable, x, x)) - - labelCovElems <- trace_path(parTable, elemsInProdTerm[[1]], - elemsInProdTerm[[2]]) |> paste0(" ^ 2") - - lhs <- labelLatentProd - rhs <- paste(stringr::str_c(labelsElemsInProd, collapse = " * "), - labelCovElems, - sep = " + ") - varLatentProd <- createParTableRow(c(lhs, rhs), op = "==") - - # covariances between elems and latents - labelsCovElemProd <- - vapply(elemsInProdTerm, - FUN.VALUE = vector("character", length = 1L), - FUN = function(elem) createLabelCov(elem, latentProd)) # wrap in anonymous fun - # scope latentProd - - covsElemsProd <- lapply(labelsCovElemProd, FUN = function(x) - createParTableRow(c(x, "0"), op = "==")) |> - purrr::list_rbind() - - # Variances of product indicators - constrained.varProdInds <- vector("list", length = ncol(relDf)) - - for (indProd in colnames(relDf)) { - labelVarIndProd <- createLabelVar(indProd) - - labelsFactorLoadings <- vector("character", length = nrow(relDf)) - labelsVarLatents <- vector("character", length = nrow(relDf)) - labelsVarInds <- vector("character", length = nrow(relDf)) - - for (latent in 1:nrow(relDf)) { - labelsFactorLoadings[[latent]] <- - createLabelLambdaSquared(relDf[latent, indProd], - rownames(relDf)[[latent]]) - labelsVarLatents[[latent]] <- - trace_path(parTable, rownames(relDf)[[latent]], - rownames(relDf)[[latent]]) - labelsVarInds[[latent]] <- createLabelVar(relDf[latent, indProd]) - } - - lhs <- labelVarIndProd - rhs1 <- paste(labelsFactorLoadings[[1]], - labelsVarLatents[[1]], - labelsVarInds[[2]], - sep = " * ") - rhs2 <- paste(labelsFactorLoadings[[2]], - labelsVarLatents[[2]], - labelsVarInds[[1]], - sep = " * ") - rhs3 <- paste(labelsVarInds[[1]], labelsVarInds[[2]], sep = " * ") - rhs <- paste(rhs1, rhs2, rhs3, sep = " + ") - - constrained.varProdInds[[indProd]] <- createParTableRow(c(lhs, rhs), op = "==") - } - - constrained.varProdInds <- purrr::list_rbind(constrained.varProdInds) - rbindParTable(parTable, rbind(varLatentProd, - covsElemsProd, - constrained.varProdInds)) -} - - -specifyVarCov <- function(parTable, relDfs) { - parTable <- specifyVarCovSingle(parTable, relDfs[[1]]) - if (length(relDfs) <= 1) return(parTable) - specifyVarCov(parTable, relDfs[-1]) -} diff --git a/R/construct_matrices_da.R b/R/construct_matrices_da.R deleted file mode 100644 index 666a073..0000000 --- a/R/construct_matrices_da.R +++ /dev/null @@ -1,524 +0,0 @@ -# Functions for constructing matrices for LMS and QML. -# Last updated: 06.06.2024 -setMatrixConstraints <- function(X, parTable, op, RHS, LHS, type, nonFreeParams) { - fillConstExprs(X, parTable = parTable, op = op, RHS = RHS, LHS = LHS, - type = type, nonFreeParams = nonFreeParams) |> - fillDynExprs(parTable = parTable, op = op, RHS = RHS, LHS = LHS, type = type) -} - - -fillConstExprs <- function(X, parTable, op, RHS, LHS, type, nonFreeParams = TRUE) { - constExprs <- parTable[parTable$op == op & - parTable$rhs %in% RHS & - parTable$lhs %in% LHS & - canBeNumeric(parTable$mod, includeNA = !nonFreeParams), ] - - setVal <- getSetValFunc(type) - for (i in seq_len(NROW(constExprs))) { - lhs <- constExprs[i, "lhs"] - rhs <- constExprs[i, "rhs"] - val <- as.numeric(constExprs[i, "mod"]) - X <- setVal(X = X, rhs = rhs, lhs = lhs, val = val) - } - - if (type == "symmetric") X[upper.tri(X)] <- 0 - X -} - - -fillDynExprs <- function(X, parTable, op, RHS, LHS, type) { - # dynamic exprs need a corresponding matrix of labels - labelX <- as.character.matrix(X, empty = TRUE) - dynamicExprs <- parTable[parTable$op == op & - parTable$rhs %in% RHS & - parTable$lhs %in% LHS & - !canBeNumeric(parTable$mod, - includeNA = TRUE), ] - - setVal <- getSetValFunc(type) - for (i in seq_len(NROW(dynamicExprs))) { - lhs <- dynamicExprs[i, "lhs"] - rhs <- dynamicExprs[i, "rhs"] - mod <- dynamicExprs[i, "mod"] - - X <- setVal(X = X, rhs = rhs, lhs = lhs, val = 0) - labelX <- setVal(X = labelX, rhs = rhs, lhs = lhs, val = mod) - } - - list(numeric = X, label = labelX) -} - - -getSetValFunc <- function(type) { - switch(type, - rhs = setValRhsFirst, - lhs = setValLhsFirst, - symmetric = setValSymmetric, - stop("Unrecognized type, this is probably a bug!")) -} - - -setValRhsFirst <- function(X, rhs, lhs, val) { - X[rhs, lhs] <- val - X -} - - -setValLhsFirst <- function(X, rhs, lhs, val) { - X[lhs, rhs] <- val - X -} - - -setValSymmetric <- function(X, rhs, lhs, val) { - X[lhs, rhs] <- X[rhs, lhs] <- val - X -} - - -constructLambda <- function(lVs, indsLVs, parTable, auto.constraints = TRUE) { - numLVs <- length(lVs) - indsLVs <- indsLVs[lVs] # make sure it is sorted - numIndsLVs <- lapply(indsLVs, FUN = length) - allIndsLVs <- unlist(indsLVs) - numAllIndsLVs <- length(allIndsLVs) - firstVal <- ifelse(auto.constraints, 1, NA) - - lastRowPreviousLV <- 0 - lambda <- matrix(0, nrow = numAllIndsLVs, ncol = numLVs, - dimnames = list(allIndsLVs, lVs)) - - for (i in seq_along(lVs)) { - rowIndices <- seq_len(numIndsLVs[[i]]) + lastRowPreviousLV - lambda[rowIndices, i] <- c(firstVal, rep(NA, numIndsLVs[[i]] - 1)) - lastRowPreviousLV <- lastRowPreviousLV + numIndsLVs[[i]] - } - - setMatrixConstraints(X = lambda, parTable = parTable, op = "=~", - RHS = allIndsLVs, LHS = lVs, type = "rhs", - nonFreeParams = TRUE) # first params are by default set to 1 -} - - -constructTau <- function(lVs, indsLVs, parTable, mean.observed = TRUE) { - indsLVs <- indsLVs[lVs] # make sure it is sorted - numIndsLVs <- lapply(indsLVs, FUN = length) - allIndsLVs <- unlist(indsLVs) - numAllIndsLVs <- length(allIndsLVs) - default <- ifelse(mean.observed, NA, 0) - lavOptimizerSyntaxAdditions <- "" - - tau <- matrix(default, nrow = numAllIndsLVs, ncol = 1, - dimnames = list(allIndsLVs, "1")) - for (lV in lVs) { # set first ind to 0, if lV has meanstructure - subPT <- parTable[parTable$lhs == lV & parTable$op == "~" & - parTable$rhs == "1", ] - if (NROW(subPT)) { - firstInd <- indsLVs[[lV]][[1]] - tau[firstInd, 1] <- 0 - lavOptimizerSyntaxAdditions <- - getFixedInterceptSyntax(indicator = firstInd, parTable = parTable, - syntax = lavOptimizerSyntaxAdditions) - } - } - - c(setMatrixConstraints(X = tau, parTable = parTable, op = "~", - RHS = "1", LHS = allIndsLVs, type = "lhs", - nonFreeParams = FALSE), - list(syntaxAdditions = lavOptimizerSyntaxAdditions)) -} - - -constructTheta <- function(lVs, indsLVs, parTable, auto.constraints = TRUE) { - numLVs <- length(lVs) - indsLVs <- indsLVs[lVs] # make sure it is sorted - numIndsLVs <- lapply(indsLVs, FUN = length) - allIndsLVs <- unlist(indsLVs) - numAllIndsLVs <- length(allIndsLVs) - - theta <- matrix(0, nrow = numAllIndsLVs, ncol = numAllIndsLVs, - dimnames = list(allIndsLVs, allIndsLVs)) - diag(theta) <- NA - - if (auto.constraints) { - for (lV in lVs) { # set to 0 if there is only a single indicator - if (numIndsLVs[[lV]] != 1) next - theta[indsLVs[[lV]], indsLVs[[lV]]] <- 0 - } - } - - setMatrixConstraints(X = theta, parTable = parTable, op = "~~", - RHS = allIndsLVs, LHS = allIndsLVs, type = "symmetric", - nonFreeParams = FALSE) -} - - -constructGamma <- function(DVs, IVs, parTable) { - exprsGamma <- parTable[parTable$op == "~" & !grepl(":", parTable$rhs) & - parTable$rhs != "1", ] - numDVs <- length(DVs) - numIVs <- length(IVs) - gamma <- matrix(0, nrow = numDVs, ncol = numIVs, dimnames = list(DVs, IVs)) - - setMatrixConstraints(X = gamma, parTable = exprsGamma, op = "~", RHS = IVs, - LHS = DVs, type = "lhs", nonFreeParams = FALSE) -} - - -constructPsi <- function(etas, parTable) { - numEtas <- length(etas) - psi <- matrix(0, nrow = numEtas, ncol = numEtas, - dimnames = list(etas, etas)) - diag(psi) <- NA - - setMatrixConstraints(X = psi, parTable = parTable, op = "~~", - RHS = etas, LHS = etas, type = "symmetric", - nonFreeParams = FALSE) -} - - -constructPhi <- function(xis, method = "lms", cov.syntax = NULL, - parTable) { - numXis <- length(xis) - phi <- matrix(0, nrow = numXis, ncol = numXis, - dimnames = list(xis, xis)) - if (method != "lms" && is.null(cov.syntax)) { - phi[lower.tri(phi, diag = TRUE)] <- NA - } - setMatrixConstraints(X = phi, parTable = parTable, op = "~~", - RHS = xis, LHS = xis, type = "symmetric", - nonFreeParams = FALSE) -} - - -constructA <- function(xis, method = "lms", cov.syntax = NULL, - parTable) { - numXis <- length(xis) - A <- matrix(0, nrow = numXis, ncol = numXis, - dimnames = list(xis, xis)) - if (method == "lms" && is.null(cov.syntax)) { - A[lower.tri(A, diag = TRUE)] <- NA - } - - setMatrixConstraints(X = A, parTable = parTable, op = "~~", - RHS = xis, LHS = xis, type = "symmetric", - nonFreeParams = FALSE) -} - - -constructAlpha <- function(etas, parTable, auto.constraints = TRUE, - mean.observed = TRUE) { - numEtas <- length(etas) - if (auto.constraints && mean.observed) default <- 0 else default <- NA - alpha <- matrix(default, nrow = numEtas, ncol = 1, - dimnames = list(etas, "1")) - - setMatrixConstraints(X = alpha, parTable = parTable, op = "~", - RHS = "1", LHS = etas, type = "lhs", - nonFreeParams = FALSE) -} - - -selectScalingY <- function(lambdaY, method = "qml") { - if (method != "qml") return(NULL) - matrix(apply(lambdaY, MARGIN = 2, FUN = isScalingY), - nrow = nrow(lambdaY), ncol = ncol(lambdaY), - dimnames = dimnames(lambdaY)) -} - - -selectBetaRows <- function(lambdaY, method = "qml") { - if (method != "qml") return(NULL) - scalingYs <- selectScalingY(lambdaY, method = "qml") - matrix(apply(scalingYs, MARGIN = 1, FUN = function(x )!all(x)), - nrow = nrow(lambdaY), ncol = 1, - dimnames = list(rownames(lambdaY), "1")) -} - - -constructR <- function(etas, indsEtas, lambdaY, method = "qml") { - if (method != "qml") return(NULL) - hasMultipleInds <- vapply(indsEtas, FUN = function(x) length(x) > 1, - FUN.VALUE = logical(1L)) - etas <- names(indsEtas)[hasMultipleInds] - indsEtas <- indsEtas[etas] - if (length(etas) == 0) return(NULL) - - numEtas <- length(etas) - numIndsEtas <- lapply(indsEtas, FUN = length) - allIndsEtas <- unlist(indsEtas) - numAllIndsEtas <- length(allIndsEtas) - selectBetaRows <- selectBetaRows(lambdaY, method = method) - rowNamesR <- rownames(lambdaY)[selectBetaRows] - - R <- matrix(0, nrow = numAllIndsEtas - numEtas, - ncol = numAllIndsEtas, - dimnames = list(rowNamesR[seq_len(numAllIndsEtas - numEtas)], - allIndsEtas)) - - lastRow <- lastCol <- 0 - for (i in seq_len(numEtas)) { - nInds <- numIndsEtas[[etas[[i]]]] - 1 - if (nInds == 0) stop2("Etas in QML must have at least two indicators") - # free params - R[seq_len(nInds) + lastRow, lastCol + 1] <- NA - R[seq_len(nInds) + lastRow, - seq_len(nInds) + lastCol + 1] <- diag(nInds) - lastRow <- lastRow + nInds - lastCol <- lastCol + nInds + 1 - } - R -} - - -getLatentEtasQml <- function(indsEtas, method = "qml") { - if (method != "qml") return(NULL) - hasMultipleInds <- vapply(indsEtas, FUN = function(x) length(x) > 1, - FUN.VALUE = logical(1L)) - etas <- names(indsEtas)[hasMultipleInds] - if (length(etas) == 0) return(NULL) - etas -} - - -getColsU <- function(etas, indsEtas, lambdaY, method = "qml") { - numEtas <- length(etas) - numIndsEtas <- lapply(indsEtas, FUN = length) - allIndsEtas <- unlist(indsEtas) - numAllIndsEtas <- length(allIndsEtas) - selectBetaRows <- selectBetaRows(lambdaY, method = "qml") - rowNamesR <- rownames(lambdaY)[selectBetaRows] - hasMultipleInds <- vapply(indsEtas, FUN = function(x) length(x) > 1, - FUN.VALUE = logical(1L)) - if (sum(hasMultipleInds) == 0) return(NULL) - colsU <- rowNamesR[seq_len(numAllIndsEtas - sum(hasMultipleInds))] - colsU[is.na(colsU)] <- paste0("__FILL__ZERO__", seq_len(sum(is.na(colsU)))) - colsU -} - - -constructFullU <- function(fullL2, N, etas, method = "qml") { - if (method != "qml" || N == 0) return(NULL) - - if (is.null(fullL2)) nCols <- length(etas) - else nCols <- NCOL(fullL2) - - matrix(0, nrow = N, ncol = nCols, dimnames = list(NULL, colnames(fullL2))) -} - - -constructFullR <- function(etas, indsEtas, lambdaY, method = "qml") { - if (method != "qml") return(NULL) - - numEtas <- length(etas) - numIndsEtas <- lapply(indsEtas, FUN = length) - allIndsEtas <- unlist(indsEtas) - numAllIndsEtas <- length(allIndsEtas) - selectBetaRows <- selectBetaRows(lambdaY, method = "qml") - rowNamesR <- rownames(lambdaY)[selectBetaRows] - hasMultipleInds <- vapply(indsEtas, FUN = function(x) length(x) > 1, - FUN.VALUE = logical(1L)) - if (sum(hasMultipleInds) == 0) return(NULL) - - matrix(0, nrow = numAllIndsEtas - sum(hasMultipleInds), - ncol = numAllIndsEtas, - dimnames = list(rowNamesR[seq_len(numAllIndsEtas - sum(hasMultipleInds))], - allIndsEtas)) - -} - - -constructFullSigma2ThetaEpsilon <- function(psi, method = "qml") { - if (method != "qml") return(NULL) - matrix(0, nrow = nrow(psi), ncol = ncol(psi), - dimnames = dimnames(psi)) -} - - -getSelectSubSigma2ThetaEpsilon <- function(fullSigma2ThetaEpsilon, - latentEtas, method = "qml") { - if (method != "qml") return(NULL) - select <- as.logical.matrix(fullSigma2ThetaEpsilon) - select[TRUE] <- FALSE - select[latentEtas, latentEtas] <- TRUE - select -} - - -constructFullL2 <- function(colsU, etas, method = "qml") { - if (method != "qml") return(NULL) - - if (is.null(colsU)) nCols <- length(etas) - else nCols <- length(colsU) - matrix(0, nrow = length(etas), ncol = nCols, - dimnames = list(etas, colsU)) -} - - -getSelectSubL2 <- function(fullL2, colsU, latentEtas, method = "qml") { - if (method != "qml") return(NULL) - select <- as.logical.matrix(fullL2) - select[TRUE] <- FALSE - select[latentEtas, !grepl("__FILL__ZERO__", colnames(select))] <- TRUE - select -} - - -getScalingInds <- function(indsEtas, R, latentEtas, method = "qml") { - if (method != "qml") return(NULL) - allIndsEtas <- unlist(indsEtas[latentEtas]) - scalingInds <- allIndsEtas[!allIndsEtas %in% rownames(R)] - scalingInds -} - - -selectThetaEpsilon <- function(indsEtas, thetaEpsilon, scalingInds, - method = "qml") { - if (method != "qml") return(NULL) - selectThetaEpsilon <- as.logical.matrix(thetaEpsilon) - selectThetaEpsilon[TRUE] <- FALSE - diag(selectThetaEpsilon)[scalingInds] <- TRUE - selectThetaEpsilon -} - - -constructSubThetaEpsilon <- function(indsEtas, thetaEpsilon, scalingInds, - method = "qml") { - if (method != "qml") return(NULL) - subThetaEpsilon <- matrix(0, nrow = length(scalingInds), - ncol = length(scalingInds), - dimnames = list(scalingInds, - scalingInds)) - diag(subThetaEpsilon) <- NA - subThetaEpsilon -} - - -getScalingLambdaY <- function(lambdaY, indsEtas, etas, method = "qml") { - if (method != "qml") return(NULL) - hasMultipleInds <- vapply(indsEtas, FUN = function(x) length(x) > 1, - FUN.VALUE = logical(1L)) - latentEtas <- names(indsEtas)[hasMultipleInds] - indsLatentEtas <- unlist(indsEtas[latentEtas]) - lambdaY[indsLatentEtas, latentEtas] -} - - -sortXisConstructOmega <- function(xis, varsInts, etas, intTerms, - method = "lms", double = FALSE) { - listSortedXis <- sortXis(xis = xis, varsInts = varsInts, etas = etas, - intTerms = intTerms, double = double) - sortedXis <- listSortedXis$sortedXis - nonLinearXis <- listSortedXis$nonLinearXis - - omegaXiXi <- constructOmegaXiXi(xis = xis, etas = etas, - sortedXis = sortedXis, - nonLinearXis = nonLinearXis, - varsInts = varsInts, - intTerms = intTerms) - omegaEtaXi <- constructOmegaEtaXi(xis = xis, etas = etas, - sortedXis = sortedXis, - nonLinearXis = nonLinearXis, - varsInts = varsInts, - intTerms = intTerms) - - list(sortedXis = sortedXis, omegaXiXi = omegaXiXi, - omegaEtaXi = omegaEtaXi, k = length(nonLinearXis)) -} - - -sortXis <- function(xis, varsInts, etas, intTerms, double) { - # allVarsInInts should be sorted according to which variables - # occur in the most interaction terms (makes it more efficient) - allVarsInInts <- unique(unlist(varsInts)) - freqInIntTerms <- lapply(varsInts, FUN = unique) |> unlist() |> - table() |> oneWayTableToDataFrame() - - sortedXis <- c(allVarsInInts, xis[!xis %in% allVarsInInts]) - nonLinearXis <- character(0L) - for (interaction in varsInts) { - if (any(interaction %in% nonLinearXis) && !double || - all(interaction %in% nonLinearXis) && double) next # no need to add it again - - stopif(length(interaction) > 2, "Only interactions between two variables are allowed") - stopif(all(interaction %in% etas), "Interactions between two endogenous ", - "variables are not allowed, see \nvignette(\"interaction_two_etas\", \"modsem\")") - - choice <- unique(interaction[which(!interaction %in% etas)]) - if (length(choice) > 1 && !double) { - freq <- freqInIntTerms[choice, "freq"] - choice <- choice[whichIsMax(freq)][[1]] # pick first if both are equal - } - - nonLinearXis <- c(nonLinearXis, choice) - } - - linearXis <- xis[!xis %in% nonLinearXis] - - list(linearXis = linearXis, sortedXis = c(nonLinearXis, linearXis), - nonLinearXis = nonLinearXis) -} - - -constructOmegaEtaXi <- function(xis, etas, sortedXis, nonLinearXis, - varsInts, intTerms) { - omega <- NULL - labelOmega <- NULL - - for (eta in etas) { - subOmega <- matrix(0, nrow = length(xis), ncol = length(etas), - dimnames = list(sortedXis, etas)) - subLabelOmega <- as.character.matrix(subOmega, empty = TRUE) - - for (row in varsInts[intTerms$lhs == eta]) { - if (!any(row %in% etas) || all(row %in% etas)) next - - whichXi <- which(!row %in% etas) - whichEta <- which(row %in% etas) - - subOmega[row[[whichXi]], row[[whichEta]]] <- - getFreeOrConstIntTerms(row, eta, intTerms) - subLabelOmega[row[[whichXi]], row[[whichEta]]] <- - getLabelIntTerms(row, eta, intTerms) - } - - omega <- rbind(omega, labelRowsOmega(subOmega, eta = eta)) - labelOmega <- rbind(labelOmega, labelRowsOmega(subLabelOmega, eta = eta)) - } - list(numeric = omega, label = labelOmega) -} - - -constructOmegaXiXi <- function(xis, etas, sortedXis, nonLinearXis, - varsInts, intTerms) { - omega <- NULL - labelOmega <- NULL - for (eta in etas) { - subOmega <- matrix(0, nrow = length(sortedXis), ncol = length(sortedXis), - dimnames = list(sortedXis, sortedXis)) - subLabelOmega <- as.character.matrix(subOmega, empty = TRUE) - - for (row in varsInts[intTerms$lhs == eta]) { - if (!all(row %in% sortedXis)) next - - whichRow <- which(row %in% nonLinearXis)[[1]] # if quadratic term pick first - whichCol <- ifelse(whichRow == 1, 2, 1) - - subOmega[row[[whichRow]], row[[whichCol]]] <- - getFreeOrConstIntTerms(row, eta, intTerms) - subLabelOmega[row[[whichRow]], row[[whichCol]]] <- - getLabelIntTerms(row, eta, intTerms) - } - - omega <- rbind(omega, labelRowsOmega(subOmega, eta = eta)) - labelOmega <- rbind(labelOmega, labelRowsOmega(subLabelOmega, eta = eta)) - } - - list(numeric = omega, label = labelOmega) -} - - -labelRowsOmega <- function(X, eta) { - rownames(X) <- paste0(eta, "~", rownames(X)) - X -} diff --git a/R/cov_model.R b/R/cov_model.R deleted file mode 100644 index dbb8b9c..0000000 --- a/R/cov_model.R +++ /dev/null @@ -1,165 +0,0 @@ -# functions for computing the constrained covariance matrix, -# based on causal relationships. This can make the lms method more flexible, -# as you can split the model into a non-linear, and linear part. allowing -# you to use (normally distributed) endogenous variables as non-normal -# as of now the mean-structure is excluded -covModel <- function(syntax = NULL, method = "lms", parTable = NULL) { - if (is.null(parTable) && !is.null(syntax)) parTable <- modsemify(syntax) - if (is.null(parTable)) { - return(list(matrices = NULL, freeParams = 0, info = NULL, - theta = NULL, syntax = NULL, parTable = NULL)) - } - - etas <- getSortedEtas(parTable, isLV = FALSE, checkAny = TRUE) - numEtas <- length(etas) - xis <- getXis(parTable, checkAny = TRUE, isLV = FALSE) - numXis <- length(xis) - - # Gamma - listGammaXi <- constructGamma(etas, xis, parTable = parTable) - gammaXi <- listGammaXi$numeric - labelGammaXi <- listGammaXi$label - - listGammaEta <- constructGamma(etas, etas, parTable = parTable) - gammaEta <- listGammaEta$numeric - labelGammaEta <- listGammaEta$label - - # covariance matrices - listPsi <- constructPsi(etas, parTable = parTable) - psi <- listPsi$numeric - labelPsi <- listPsi$label - - listPhi <- constructPhi(xis, method = method, parTable = parTable) - phi <- listPhi$numeric - labelPhi <- listPhi$label - - listA <- constructA(xis, method = method, parTable = parTable) - A <- listA$numeric - labelA <- listA$label - - matrices <- list( - gammaXi = gammaXi, - gammaEta = gammaEta, - A = A, - psi = psi, - phi = phi) - - labelMatrices <- list( - gammaXi = labelGammaXi, - gammaEta = labelGammaEta, - A = labelA, - psi = labelPsi, - phi = labelPhi) - - model <- list(info = - list(etas = etas, - numEtas = numEtas, - xis = xis, - numXis = numXis), - matrices = matrices, - labelMatrices = labelMatrices, - syntax = syntax, - parTable = parTable) - - model -} - - -countFreeCovModel <- function(matrices) { - vapply(matrices, FUN.VALUE = integer(1L), - FUN = function(x) sum(is.na(x))) |> sum() -} - - -expectedCovModel <- function(model, method = "lms", sortedXis) { - gammaXi <- model$matrices$gammaXi - gammaEta <- model$matrices$gammaEta - - if (method == "lms") { - A <- model$matrices$A - phi <- A %*% t(A) - } else if (method == "qml") { - phi <- model$matrices$phi - } - psi <- model$matrices$psi - - Binv <- solve(diag(nrow(gammaEta)) - gammaEta) - covEtaEta <- Binv %*% (gammaXi %*% phi %*% t(gammaXi) + psi) %*% t(Binv) - covEtaXi <- Binv %*% gammaXi %*% phi - sigma <- rbind(cbind(covEtaEta, covEtaXi), - cbind(t(covEtaXi), phi)) - sigma <- sigma[sortedXis, sortedXis] - - if (method == "lms") { - sigma <- tryCatch(t(chol(sigma)), - error = function(e) { - sigma[TRUE] <- NaN - sigma - }) - } - sigma -} - - -covModelToParTable <- function(model, method = "lms") { - matricesEst <- model$covModel$matrices - matricesSE <- model$covModelSE$matrices - matricesNA <- model$covModelNA$matrices - matricesLabel <- model$covModel$labelMatrices - - if (is.null(matricesEst) || is.null(matricesNA)) return(NULL) - if (is.null(matricesSE)) matricesSE <- matricesNA - - etas <- model$info$etas - numXis <- model$info$numXis - parTable <- NULL - - # coefficients Structural Model - newRows <- matrixToParTable(matricesNA$gammaXi, - matricesEst$gammaXi, - matricesSE$gammaXi, - matricesLabel$gammaXi, - op = "~", - rowsLhs = TRUE) - parTable <- rbind(parTable, newRows) - - newRows <- matrixToParTable(matricesNA$gammaEta, - matricesEst$gammaEta, - matricesSE$gammaEta, - matricesLabel$gammaEta, - op = "~", - rowsLhs = TRUE) - parTable <- rbind(parTable, newRows) - - if (method == "lms") { - phiNA <- matricesNA$A - phiEst <- matricesEst$phi - phiSE <- matricesSE$A - phiLabel <- matricesLabel$A - } else if (method == "qml") { - phiNA <- matricesNA$phi - phiEst <- matricesEst$phi - phiSE <- matricesSE$phi - phiLabel <- matricesLabel$phi - } - - newRows <- matrixToParTable(phiNA, - phiEst, - phiSE, - phiLabel, - op = "~~", - rowsLhs = FALSE) - parTable <- rbind(parTable, newRows) - - newRows <- matrixToParTable(matricesNA$psi, - matricesEst$psi, - matricesSE$psi, - matricesLabel$psi, - op = "~~", - rowsLhs = FALSE) - parTable <- rbind(parTable, newRows) - - parTable <- lapplyDf(parTable, FUN = function(x) replace(x, x == -999, NA)) - # return - parTable -} diff --git a/R/create_labels_pi_ca.R b/R/create_labels_pi_ca.R deleted file mode 100644 index afb75b5..0000000 --- a/R/create_labels_pi_ca.R +++ /dev/null @@ -1,28 +0,0 @@ - - -createLabelCov <- function(x, y) - paste("Cov", x, y, sep = "_") - - -createLabelVar <- function(x) - paste("Var", x, sep = "_") - - -createLabelLambda <- function(ind, latent) - paste("lambda", ind, latent, sep = "_") - - -createLabelLambdaSquared <- function(ind, latent) - paste0(createLabelLambda(ind, latent), " ^ 2") - - -createLabelGamma <- function(x, y) - paste("Gamma", x, y, sep = "_") - - -createLabelMean <- function(x) - paste("Mean", x, sep = "_") - - -createLabelZeta <- function(x) - paste("Zeta", x, sep = "_") diff --git a/R/datasets.R b/R/datasets.R deleted file mode 100644 index ea87be3..0000000 --- a/R/datasets.R +++ /dev/null @@ -1,145 +0,0 @@ -#' oneInt -#' -#' @name oneInt -#' @docType data -#' @description A simulated dataset with one interaction effect -NULL - - -#' TPB -#' -#' @name TPB -#' @docType data -#' @description A simulated dataset based on the Theory of Planned Behaviour -#' @examples -#' -#' tpb <- ' -#' # Outer Model (Based on Hagger et al., 2007) -#' ATT =~ att1 + att2 + att3 + att4 + att5 -#' SN =~ sn1 + sn2 -#' PBC =~ pbc1 + pbc2 + pbc3 -#' INT =~ int1 + int2 + int3 -#' BEH =~ b1 + b2 -#' -#' # Inner Model (Based on Steinmetz et al., 2011) -#' INT ~ ATT + SN + PBC -#' BEH ~ INT + PBC + INT:PBC -#' ' -#' -#' est <- modsem(tpb, data = TPB) -NULL - - -#' TPB_UK -#' -#' @name TPB_UK -#' @docType data -#' @description A dataset based on the Theory of Planned Behaviour from a -#' UK sample. 4 variables with high communality were selected for each -#' latent variable (ATT, SN, PBC, INT, BEH), from two time points (t1 and t2). -#' -#' @source -#' Gathered from a replciation study of the original by Hagger et al. (2023). -#' Obtained from https://doi.org/10.23668/psycharchives.12187 -#' @examples -#' -#' tpb_uk <- ' -#' # Outer Model (Based on Hagger et al., 2007) -#' ATT =~ att3 + att2 + att1 + att4 -#' SN =~ sn4 + sn2 + sn3 + sn1 -#' PBC =~ pbc2 + pbc1 + pbc3 + pbc4 -#' INT =~ int2 + int1 + int3 + int4 -#' BEH =~ beh3 + beh2 + beh1 + beh4 -#' -#' # Inner Model (Based on Steinmetz et al., 2011) -#' # Causal Relationsships -#' INT ~ ATT + SN + PBC -#' BEH ~ INT + PBC -#' BEH ~ INT:PBC -#' ' -#' -#' est <- modsem(tpb_uk, data = TPB_UK) -NULL - - -#' Jordan subset of PISA 2006 data -#' -#' @name jordan -#' @docType data -#' @description The data stem from the large-scale assessment study PISA 2006 -#' (Organisation for Economic Co-Operation and Development, 2009) where -#' competencies of 15-year-old students in reading, mathematics, and science -#' are assessed using nationally representative samples in 3-year cycles. -#' In this eacademicample, data from the student background questionnaire from the -#' Jordan sample of PISA 2006 were used. Only data of students with complete -#' responses to all 15 items (N = 6,038) were considered. -#' -#' @format -#' A data frame of fifteen variables and 6,038 observations: -#' -#' enjoy1 -#' indicator for enjoyment of science, item ST16Q01: I generally have fun when I am learning topics. -#' -#' enjoy2 -#' indicator for enjoyment of science, item ST16Q02: I like reading about . -#' -#' enjoy3 -#' indicator for enjoyment of science, item ST16Q03: I am happy doing problems. -#' -#' enjoy4 -#' indicator for enjoyment of science, item ST16Q04: I enjoy acquiring new knowledge in . -#' -#' enjoy5 -#' indicator for enjoyment of science, item ST16Q05: I am interested in learning about . -#' -#' academic1 -#' indicator for academic self-concept in science, item ST37Q01: I can easily understand new ideas in . -#' -#' academic2 -#' indicator for academic self-concept in science, item ST37Q02: Learning advanced topics would be easy for me. -#' -#' academic3 -#' indicator for academic self-concept in science, item ST37Q03: I can usually give good answers to on topics. -#' -#' academic4 -#' indicator for academic self-concept in science, item ST37Q04: I learn topics quickly. -#' -#' academic5 -#' indicator for academic self-concept in science, item ST37Q05: topics are easy for me. -#' -#' academic6 -#' indicator for academic self-concept in science, item ST37Q06: When I am being taught , I can understand the concepts very well. -#' -#' career1 -#' indicator for career aspirations in science, item ST29Q01: I would like to work in a career involving . -#' -#' career2 -#' indicator for career aspirations in science, item ST29Q02: I would like to study after . -#' -#' career3 -#' indicator for career aspirations in science, item ST29Q03: I would like to spend my life doing advanced . -#' -#' career4 -#' indicator for career aspirations in science, item ST29Q04: I would like to work on projects as an adult. -#' -#' @source -#' This version of the dataset, as well as the description was gathered from the -#' documentation of the 'nlsem' package (https://cran.r-project.org/package=nlsem), -#' where the only difference is that the names of the variables were changed -#' -#' Originally the dataset was gathered by the Organisation for Economic Co-Operation and Development (2009). -#' Pisa 2006: Science competencies for tomorrow's world (Tech. Rep.). -#' Paris, France. Obtained from: https://www.oecd.org/pisa/pisaproducts/database-pisa2006.htm -#' -#' @examples -#' \dontrun{ -#' m1 <- ' -#' ENJ =~ enjoy1 + enjoy2 + enjoy3 + enjoy4 + enjoy5 -#' CAREER =~ career1 + career2 + career3 + career4 -#' SC =~ academic1 + academic2 + academic3 + academic4 + academic5 + academic6 -#' CAREER ~ ENJ + SC + ENJ:ENJ + SC:SC + ENJ:SC -#' ' -#' -#' est <- modsem(m1, data = jordan) -#' } -NULL diff --git a/R/equations_lms.R b/R/equations_lms.R deleted file mode 100644 index 1d30d99..0000000 --- a/R/equations_lms.R +++ /dev/null @@ -1,185 +0,0 @@ -muLms <- function(model, z1) { # for testing purposes - matrices <- model$matrices - A <- matrices$A - Oxx <- matrices$omegaXiXi - Oex <- matrices$omegaEtaXi - Ie <- matrices$Ieta - lY <- matrices$lambdaY - lX <- matrices$lambdaX - tY <- matrices$tauY - tX <- matrices$tauX - Gx <- matrices$gammaXi - Ge <- matrices$gammaEta - a <- matrices$alpha - psi <- matrices$psi - - k <- model$quad$k - zVec <- c(z1[0:k], rep(0, model$info$numXis - k)) - kronZ <- kronecker(Ie, A %*% zVec) - if (ncol(Ie) == 1) Binv <- Ie else Binv <- solve(Ie - Ge - t(kronZ) %*% Oex) - - muX <- tX + lX %*% A %*% zVec - muY <- tY + - lY %*% (Binv %*% (a + - Gx %*% A %*% zVec + - t(kronZ) %*% Oxx %*% A %*% zVec)) - rbind(muX, muY) -} - - -sigmaLms <- function(model, z1) { # for testing purposes - matrices <- model$matrices - Oxx <- matrices$omegaXiXi - Oex <- matrices$omegaEtaXi - Ie <- matrices$Ieta - A <- matrices$A - lY <- matrices$lambdaY - lX <- matrices$lambdaX - Gx <- matrices$gammaXi - Ge <- matrices$gammaEta - dX <- matrices$thetaDelta - dY <- matrices$thetaEpsilon - psi <- matrices$psi - k <- model$quad$k - zVec <- c(z1[0:k], rep(0, model$info$numXis - k)) - kronZ <- kronecker(Ie, A %*% zVec) - if (ncol(Ie) == 1) Binv <- Ie else Binv <- solve(Ie - Ge - t(kronZ) %*% Oex) - - OI <- diag(1, model$info$numXis) - diag(OI) <- c(rep(0, k), rep(1, model$info$numXis - k)) - - Sxx <- lX %*% A %*% OI %*% - t(A) %*% t(lX) + dX - Sxy <- lX %*% A %*% OI %*% - t(Binv %*% (Gx %*% A + t(kronZ) %*% Oxx %*% A)) %*% t(lY) - Syy <- lY %*% - (Binv %*% (Gx %*% A + t(kronZ) %*% Oxx %*% A)) %*% - OI %*% - t(Binv %*% (Gx %*% A + t(kronZ) %*% Oxx %*% A)) %*% t(lY) + - lY %*% (Binv %*% psi %*% t(Binv)) %*% t(lY) + dY - rbind( - cbind(Sxx, Sxy), - cbind(t(Sxy), Syy) - ) -} - - -estepLms <- function(model, theta, data, ...) { - modFilled <- fillModel(model = model, theta = theta, method = "lms") - V <- modFilled$quad$n # matrix of node vectors m x k - w <- modFilled$quad$w # weights - # the probability of each observation is derived as the sum of the probabilites - # of observing the data given the parameters at each point in the k dimensional - # space of the distribution of the non-linear latent variables. To approximate - # this we use individual nodes sampled from the k-dimensional space with a - # corresponding probability (weight w) for each node appearing. I.e., whats the - # probability of observing the data given the nodes, and what is the probability - # of observing the given nodes (i.e., w). Sum the row probabilities = 1 - P <- matrix(0, nrow = nrow(data), ncol = length(w)) - sapply(seq_along(w), FUN = function(i) { - P[, i] <<- w[[i]] * dmvn(data, - mean = muLmsCpp(model = modFilled, z = V[i, ]), - sigma = sigmaLmsCpp(model = modFilled, z = V[i, ]), - log = FALSE - ) - }) - P / rowSums(P) -} - - -logLikLms <- function(theta, model, data, P, sign = -1, ...) { - modFilled <- fillModel(model = model, theta = theta, method = "lms") - k <- model$quad$k - V <- modFilled$quad$n - # summed log probability of observing the data given the parameters - # weighted my the posterior probability calculated in the E-step - r <- vapply(seq_len(nrow(V)), FUN.VALUE = numeric(1L), FUN = function(i) { - lls <- sum(dmvn(data, - mean = muLmsCpp(model = modFilled, z = V[i, ]), - sigma = sigmaLmsCpp(model = modFilled, z = V[i, ]), - log = TRUE - ) * P[, i]) - lls - }) |> sum() - sign * r -} - - -gradientLogLikLms <- function(theta, model, data, P, sign = -1, epsilon = 1e-4) { - baseLL <- logLikLms(theta, model = model, data = data, P = P, sign = sign) - vapply(seq_along(theta), FUN.VALUE = numeric(1L), FUN = function(i) { - theta[[i]] <- theta[[i]] + epsilon - (logLikLms(theta, model = model, data = data, P = P, sign = sign) - baseLL) / epsilon - }) -} - - -# log likelihood for each observation -- not all -logLikLms_i <- function(theta, model, data, P, sign = -1, ...) { - modFilled <- fillModel(model = model, theta = theta, method = "lms") - k <- model$quad$k - V <- modFilled$quad$n - # summed log probability of observing the data given the parameters - # weighted my the posterior probability calculated in the E-step - r <- lapplyMatrix(seq_len(nrow(V)), FUN = function(i) { - lls <- dmvn(data, - mean = muLmsCpp(model = modFilled, z = V[i, ]), - sigma = sigmaLmsCpp(model = modFilled, z = V[i, ]), - log = TRUE - ) * P[, i] - lls - }, FUN.VALUE = numeric(nrow(data))) - - sign * apply(r, MARGIN = 1, FUN = sum) -} - - -# gradient function of logLikLms_i -gradientLogLikLms_i <- function(theta, model, data, P, sign = -1, epsilon = 1e-4) { - baseLL <- logLikLms_i(theta, model, data = data, P = P, sign = sign) - lapplyMatrix(seq_along(theta), FUN = function(i) { - theta[[i]] <- theta[[i]] + epsilon - (logLikLms_i(theta, model, data = data, P = P, sign = sign) - baseLL) / epsilon - }, FUN.VALUE = numeric(nrow(data))) -} - - -# Maximization step of EM-algorithm (see Klein & Moosbrugger, 2000) -mstepLms <- function(theta, model, data, P, - max.step, - verbose = FALSE, - control = list(), - optimizer = "nlminb", - optim.method = "L-BFGS-B", - epsilon = 1e-6, - ...) { - gradient <- function(theta, model, data, P, sign) { - gradientLogLikLms(theta = theta, model = model, P = P, sign = sign, - data = data, epsilon = epsilon) - } - - if (optimizer == "nlminb") { - if (is.null(control$iter.max)) control$iter.max <- max.step - est <- stats::nlminb(start = theta, objective = logLikLms, data = data, - model = model, P = P, gradient = gradient, - sign = -1, - upper = model$info$bounds$upper, - lower = model$info$bounds$lower, control = control, - ...) |> suppressWarnings() - - } else if (optimizer == "L-BFGS-B") { - if (is.null(control$maxit)) control$maxit <- max.step - est <- stats::optim(par = theta, fn = logLikLms, data = data, - model = model, P = P, gr = gradient, - method = optimizer, control = control, - sign = -1, lower = model$info$bounds$lower, - upper = model$info$bounds$upper, ...) - - est$objective <- est$value - est$iterations <- est$counts[["function"]] - } else { - stop2("Unrecognized optimizer, must be either 'nlminb' or 'L-BFGS-B'") - } - - est -} diff --git a/R/equations_qml.R b/R/equations_qml.R deleted file mode 100644 index 2aa048a..0000000 --- a/R/equations_qml.R +++ /dev/null @@ -1,161 +0,0 @@ -logLikQml <- function(theta, model, sum = TRUE, sign = -1) { - modelFilled <- fillModel(model, theta, method = "qml") - numXi <- model$info$numXis - numEta <- model$info$numEtas - kOmegaEta <- model$info$kOmegaEta - latentEtas <- model$info$latentEtas - - m <- modelFilled$matrices - m$numEta <- numEta - m$numXi <- numXi - m$kOmegaEta <- kOmegaEta - - m$tauX <- m$tauX + m$lambdaX %*% m$beta0 - m$x <- model$data[, model$info$allIndsXis, drop = FALSE] - m$y <- model$data[, model$info$allIndsEtas, drop = FALSE] - m$x <- centerIndicators(m$x, tau = m$tauX) - m$y <- centerIndicators(m$y, tau = m$tauY) - - t <- NROW(m$x) - if (!is.null(m$emptyR)) { - m$R <- m$emptyR - m$R[is.na(m$R)] <- -m$lambdaY[!m$selectScalingY] # fill R with -Beta - m$fullR[m$rowsR, m$colsR] <- m$R - m$u <- m$y %*% t(m$fullR) - m$fullU[, m$colsU] <- m$u - m$Beta <- m$lambdaY[m$selectBetaRows, latentEtas] - m$subThetaEpsilon <- m$subThetaEpsilon - m$subThetaEpsilon[is.na(m$subThetaEpsilon)] <- - m$thetaEpsilon[m$selectThetaEpsilon] - - m$RER <- m$R %*% m$thetaEpsilon[m$colsR, m$colsR] %*% t(m$R) - invRER <- solve(m$RER) - m$L2 <- -m$subThetaEpsilon %*% t(m$Beta) %*% invRER - m$fullL2[m$selectSubL2] <- m$L2 - - m$Sigma2ThetaEpsilon <- m$subThetaEpsilon - m$subThetaEpsilon ^ 2 %*% - t(m$Beta) %*% invRER %*% m$Beta - - m$fullSigma2ThetaEpsilon[m$selectSubSigma2ThetaEpsilon] <- - m$Sigma2ThetaEpsilon - } - - m$Sigma2ThetaEpsilon <- m$fullSigma2ThetaEpsilon - m$L2 <- m$fullL2 - m$u <- m$fullU - m$LXPLX <- m$lambdaX %*% m$phi %*% t(m$lambdaX) + m$thetaDelta - invLXPLX <- solve(m$LXPLX) - m$L1 <- m$phi %*% t(m$lambdaX) %*% invLXPLX - m$Sigma1 <- m$phi - m$phi %*% t(m$lambdaX) %*% invLXPLX %*% m$lambdaX %*% m$phi - m$kronXi <- calcKronXi(m, t) - m$Binv <- calcBinvCpp(m, t) - - Ey <- muQmlCpp(m, t) - sigmaEpsilon <- sigmaQmlCpp(m, t) - sigmaXU <- calcSigmaXU(m) - - normalInds <- colnames(sigmaXU) - indsY <- colnames(m$y) - nonNormalInds <- indsY[!indsY %in% normalInds] - - f2 <- probf2(matrices = m, normalInds = normalInds, sigma = sigmaXU) - f3 <- probf3(matrices = m, nonNormalInds = nonNormalInds, expected = Ey, - sigma = sigmaEpsilon, t = t, numEta = numEta) - - if (sum) return(sign * sum(f2 + f3)) - sign * (f2 + f3) -} - - -calcSigmaXU <- function(matrices) { - if (is.null(matrices$emptyR)) return(matrices$LXPLX) - diagBindSquareMatrices(matrices$LXPLX, matrices$RER) -} - - -probf2 <- function(matrices, normalInds, sigma) { - mu <- rep(0, ncol(sigma)) - X <- cbind(matrices$x, matrices$u)[ , normalInds] - dmvn(X, mean = mu, sigma = sigma, log = TRUE) -} - - -probf3 <- function(matrices, nonNormalInds, expected, sigma, t, numEta) { - if (numEta == 1) { - return(dnormCpp(matrices$y[, 1], mu = expected, sigma = sqrt(sigma))) - } - rep_dmvnorm(matrices$y[, nonNormalInds], expected = expected, - sigma = sigma, t = t) -} - - -centerIndicators <- function(X, tau) { - for (i in seq_len(ncol(X))) X[, i] <- X[, i] - tau[[i]] - X -} - - -gradientLogLikQml <- function(theta, model, epsilon = 1e-8, sign = -1) { - baseLL <- logLikQml(theta, model, sign = sign) - vapply(seq_along(theta), FUN.VALUE = numeric(1L), FUN = function(i) { - theta[[i]] <- theta[[i]] + epsilon - (logLikQml(theta, model, sign = sign) - baseLL) / epsilon - }) -} - - -# log likelihood for each observation -- not all -# wrapper fro logLikQml(sum = FALSE) -logLikQml_i <- function(theta, model, sign = -1) { - logLikQml(theta, model, sum = FALSE, sign = sign) -} - - -# gradient function of logLikQml_i -gradientLogLikQml_i <- function(theta, model, sign = -1, epsilon = 1e-8) { - baseLL <- logLikQml_i(theta, model, sign = sign) - lapplyMatrix(seq_along(theta), FUN = function(i) { - theta[[i]] <- theta[[i]] + epsilon - (logLikQml_i(theta, model, sign = sign) - baseLL) / epsilon - }, FUN.VALUE = numeric(nrow(model$data))) -} - - -mstepQml <- function(model, - theta, - max.iter = 500, - verbose = FALSE, - convergence = 1e-6, - control = list(), - optimizer = "nlminb", - epsilon = 1e-8, - ...) { - gradient <- function(theta, model, sign) - gradientLogLikQml(theta = theta, model = model, epsilon = epsilon, sign = sign) - - if (verbose) cat("Starting M-step\n") - - if (optimizer == "nlminb") { - control$iter.max <- max.iter - control$eval.max <- max.iter * 2 - control$rel.tol <- convergence - - est <- stats::nlminb(start = theta, objective = logLikQml, model = model, - gradient = gradient, sign = -1, - upper = model$info$bounds$upper, - lower = model$info$bounds$lower, control = control, ...) - - } else if (optimizer == "L-BFGS-B") { - control$factr <- convergence - control$maxit <- max.iter - - est <- stats::optim(par = theta, fn = logLikQml, model = model, - gr = gradient, method = optimizer, sign = -1, - control = control, ...) - - est$objective <- est$value - est$iterations <- est$counts[["function"]] - } else stop2("Unrecognized optimizer, must be either 'nlminb' or 'L-BFGS-B'") - - est -} diff --git a/R/est_lms.R b/R/est_lms.R deleted file mode 100644 index 0384f7b..0000000 --- a/R/est_lms.R +++ /dev/null @@ -1,147 +0,0 @@ -emLms <- function(model, - verbose = FALSE, - convergence = 1e-2, - max.iter = 500, - max.step = 1, - control = list(), - calc.se = TRUE, - FIM = "observed", - OFIM.hessian = FALSE, - EFIM.S = 3e4, - EFIM.parametric = TRUE, - robust.se = FALSE, - epsilon = 1e-6, - optimizer = "nlminb", - fix.estep = TRUE, - ...) { - data <- model$data - stopif(anyNA(data), "Remove or replace missing values from data") - - # Initialization - logLikNew <- 0 - logLikOld <- 0 - iterations <- 0 - thetaNew <- model$theta - - bestLogLik <- -Inf - bestP <- NULL - bestTheta <- NULL - logLiks <- NULL - logLikChanges <- NULL - - run <- TRUE - doEstep <- TRUE - - while(run) { - logLikOld <- logLikNew - thetaOld <- thetaNew - - if (doEstep) P <- estepLms(model = model, theta = thetaOld, data = data, ...) - - mstep <- mstepLms(model = model, P = P, data = data, theta = thetaOld, - max.step = max.step, epsilon = epsilon, - optimizer = optimizer, control = control, ...) - - logLikNew <- -mstep$objective - thetaNew <- unlist(mstep$par) - iterations <- iterations + 1 - logLiks <- c(logLiks, logLikNew) - logLikChanges <- c(logLikChanges, logLikNew - logLikOld) - - if (verbose) { - cat(sprintf("EM: Iteration = %5d, LogLik = %11.2f, Change = %10.3f\n", - iterations, logLikNew, logLikNew - logLikOld)) - } - - if (logLikNew > bestLogLik) { - bestLogLik <- logLikNew - bestP <- P - bestTheta <- thetaOld - } - - if (abs(logLikOld - logLikNew) < convergence) run <- FALSE - if (iterations >= max.iter){ - warning2("Maximum number of iterations was reached. ", - "EM algorithm might not have converged.") - run <- FALSE - } - - if (doEstep && fix.estep && runningAverage(logLikChanges, n = 30) < 0 && - nNegativeLast(logLikChanges, n = 30) >= 15 && iterations > 200) { - doEstep <- FALSE - P <- bestP - thetaNew <- bestTheta - - warning2("EM algorithm is not converging. ", - "Attempting to fix prior probabilities from E-step\n", - "you might want to increase the convergence (i.e., less strict) criterion (see 'help(modsem_da)')") - } - } - - final <- mstepLms(model = model, P = P, data = data, - theta = thetaNew, max.step = max.step, - epsilon = epsilon, optimizer = optimizer, - verbose = verbose, control = control, ...) - - coefficients <- final$par - finalModel <- fillModel(model, coefficients, fillPhi = TRUE, - method = "lms") - - emptyModel <- getEmptyModel(parTable = model$parTable, - cov.syntax = model$cov.syntax, - parTableCovModel = model$covModel$parTable, - method = "lms") - - finalModel$matricesNA <- emptyModel$matrices - finalModel$covModelNA <- emptyModel$covModel - - # Caclulate information matrix (I) and standard errors (SE) - typeSE <- ifelse(!calc.se, "none", ifelse(robust.se, "robust", "standard")) - FIM <- calcFIM_da(model = model, finalModel = finalModel, theta = coefficients, - data = data, method = "lms", EFIM.S = EFIM.S, - hessian = OFIM.hessian, calc.se = calc.se, - EFIM.parametric = EFIM.parametric, verbose = verbose, - FIM = FIM, robust.se = robust.se, epsilon = epsilon, - NA__ = -999) - SE <- calcSE_da(calc.se = calc.se, vcov = FIM$vcov, - theta = coefficients, NA__ = -999) - - modelSE <- fillModel(replaceNonNaModelMatrices(model, value = -999), - theta = SE, method = "lms") - finalModel$matricesSE <- modelSE$matrices - finalModel$covModelSE <- modelSE$covModel - - parTable <- modelToParTable(finalModel, method = "lms") - - parTable$z.value <- parTable$est / parTable$std.error - parTable$p.value <- 2 * stats::pnorm(-abs(parTable$z.value)) - parTable$ci.lower <- parTable$est - 1.96 * parTable$std.error - parTable$ci.upper <- parTable$est + 1.96 * parTable$std.error - - # convergence of em - if (iterations == max.iter) convergence <- FALSE else convergence <- TRUE - - out <- list(model = finalModel, - method = "lms", - optimizer = paste0("EM-", optimizer), - data = data, - theta = coefficients, - parTable = parTable, - - originalParTable = model$parTable, - - logLik = -final$objective, - iterations = iterations, - convergence = convergence, - type.se = typeSE, - info.quad = getInfoQuad(model$quad), - - type.estimates = "unstandardized", - - FIM = FIM$FIM, - vcov = FIM$vcov, - - information = FIM$type) - - out -} diff --git a/R/est_qml.R b/R/est_qml.R deleted file mode 100644 index 712d78f..0000000 --- a/R/est_qml.R +++ /dev/null @@ -1,83 +0,0 @@ -estQml <- function(model, - convergence = 1e-2, - verbose = FALSE, - max.iter = 500, - calc.se = TRUE, - FIM = "observed", - OFIM.hessian = FALSE, - EFIM.S = 3e4, - EFIM.parametric = TRUE, - robust.se = FALSE, - epsilon = 1e-6, - optimizer = "nlminb", - ...) { - startTheta <- model$theta - final <- mstepQml(model = model, theta = startTheta, max.iter = max.iter, - convergence = convergence, epsilon = epsilon, - verbose = verbose, optimizer = optimizer, ...) - coefficients <- final$par - finalModel <- fillModel(model, coefficients) - - info <- model$info - - finalModel <- fillModel(model, coefficients, method = "qml") - emptyModel <- getEmptyModel(parTable = model$parTable, - cov.syntax = model$cov.syntax, - parTableCovModel = model$covModel$parTable, - method = "qml") - finalModel$matricesNA <- emptyModel$matrices - finalModel$covModelNA <- emptyModel$covModel - - # Caclulate information matrix (I) and standard errors (SE) - typeSE <- ifelse(!calc.se, "none", ifelse(robust.se, "robust", "standard")) - FIM <- calcFIM_da(model = model, finalModel = finalModel, theta = coefficients, - data = model$data, method = "qml", EFIM.S = EFIM.S, - hessian = OFIM.hessian, calc.se = calc.se, - EFIM.parametric = EFIM.parametric, verbose = verbose, - FIM = FIM, robust.se = robust.se, NA__ = -999, - epsilon = epsilon) - SE <- calcSE_da(calc.se = calc.se, FIM$vcov, theta = coefficients, NA__ = -999) - - modelSE <- fillModel(replaceNonNaModelMatrices(model, value = -999), - theta = SE, method = "lms") - - modelSE <- fillModel(replaceNonNaModelMatrices(model, value = -999), - SE, method = "qml") - finalModel$matricesSE <- modelSE$matrices - finalModel$covModelSE <- modelSE$covModel - - parTable <- modelToParTable(finalModel, method = "qml") - - parTable$z.value <- parTable$est / parTable$std.error - parTable$p.value <- 2 * stats::pnorm(-abs(parTable$z.value)) - parTable$ci.lower <- parTable$est - 1.96 * parTable$std.error - parTable$ci.upper <- parTable$est + 1.96 * parTable$std.error - - if (final$iterations >= max.iter) { - warning2("Maximum number of iterations was reached, ", - "model estimation might not have converged.") - } - - out <- list(model = finalModel, - method = "qml", - optimizer = optimizer, - data = model$data, - theta = coefficients, - parTable = parTable, - - originalParTable = model$parTable, - - logLik = -final$objective, - iterations = final$iterations, - convergence = final$convergence, - type.se = typeSE, - - type.estimates = "unstandardized", - - info.quad = NULL, - FIM = FIM$FIM, - vcov = FIM$vcov, - information = FIM$type) - - out -} diff --git a/R/fit_modsem_da.R b/R/fit_modsem_da.R deleted file mode 100644 index a85d748..0000000 --- a/R/fit_modsem_da.R +++ /dev/null @@ -1,117 +0,0 @@ -#' Fit measures for QML and LMS models -#' -#' @param model model to be assessed -#' @description Calculates chi-sq test and p-value, as well as RMSEA for -#' the LMS and QML models. Note that the Chi-Square based fit measures should be calculated -#' for the baseline model, i.e., the model without the interaction effect -#' @param model fitted model. Thereafter, you can use 'compare_fit()' -#' to assess the comparative fit of the models. If the interaction effect makes -#' the model better, and e.g., the RMSEA is good for the baseline model, -#' the interaction model likely has a good RMSEA as well. -#' @param chisq should Chi-Square based fit-measures be calculated? -#' @export -fit_modsem_da <- function(model, chisq = TRUE) { - parTable <- model$parTable - warnif(any(grepl(":", parTable$rhs)) && chisq, - "Chi-Square based fit-measures for LMS and QML ", - "should be calculated for baseline model ", - "i.e., the model without the interaction effect") - - logLik <- model$logLik - O <- stats::cov(model$data) - mu <- apply(model$data, 2, mean) - N <- NROW(model$data) - p <- NCOL(model$data) - coef <- coef(model) - k <- length(coef) - df <- getDegreesOfFreedom(m = p, coef = coef) - - matrices <- model$model$matrices - gammaXi <- matrices$gammaXi - gammaEta <- matrices$gammaEta - phi <- matrices$phi - psi <- matrices$psi - lambdaX <- matrices$lambdaX - lambdaY <- matrices$lambdaY - thetaY <- matrices$thetaEpsilon - thetaX <- matrices$thetaDelta - tauX <- matrices$tauX - tauY <- matrices$tauY - alpha <- matrices$alpha - Ieta <- matrices$Ieta - Binv <- solve(Ieta - gammaEta) - - if (chisq) { - covX <- lambdaX %*% phi %*% t(lambdaX) + thetaX - covXY <- lambdaY %*% (Binv %*% gammaXi %*% phi) %*% t(lambdaX) - covY <- lambdaY %*% - (Binv %*% (gammaXi %*% phi %*% t(gammaXi) + psi) %*% t(Binv)) %*% - t(lambdaY) + thetaY - - E <- rbind(cbind(covX, t(covXY)), - cbind(covXY, covY)) - - if (any(grepl("tau|alpha", names(coef)))) { - muX <- tauX - muY <- tauY + lambdaY %*% Binv %*% alpha - muHat <- rbind(muX, muY) - } else muHat <- mu - - chisqValue <- calcChiSqr(O = O, E = E, N = N, p = p, mu = mu, muHat = muHat) - chisqP <- stats::pchisq(chisqValue, df, lower.tail = FALSE) - RMSEA <- calcRMSEA(chisqValue, df, N) - - } else { - E <- NULL - chisqValue <- NULL - chisqP <- NULL - df <- NULL - RMSEA <- NULL - muHat <- NULL - } - - AIC <- calcAIC(logLik, k = k) - AICc <- calcAdjAIC(logLik, k = k, N = N) - BIC <- calcBIC(logLik, k = k, N = N) - aBIC <- calcAdjBIC(logLik, k = k, N = N) - - list(sigma.observed = O, sigma.expected = E, - mu.observed = mu, mu.expected = muHat, - chisq.value = chisqValue, chisq.pvalue = chisqP, chisq.df = df, - AIC = AIC, AICc = AICc, BIC = BIC, aBIC = aBIC, RMSEA = RMSEA) -} - - -calcChiSqr <- function(O, E, N, p, mu, muHat) { - diff_mu <- mu - muHat - Einv <- solve(E) - (N - 1) * (t(diff_mu) %*% Einv %*% diff_mu + - tr(O %*% Einv) - log(det(O %*% Einv)) - p) -} - - -calcRMSEA <- function(chi.sq, df, N) { - ncp <- max(0, chi.sq - df) - sqrt(ncp / ((N - df) * (N - df))) -} - - -calcAIC <- function(logLik, k) { - 2 * k - 2 * logLik -} - - -calcAdjAIC <- function(logLik, k, N) { - AIC <- calcAIC(logLik, k) - AIC + (2 * k ^ 2 + 2 * k) / (N - k - 1) -} - - -calcBIC <- function(logLik, k, N) { - log(N) * k - 2 * logLik -} - - -calcAdjBIC <- function(logLik, k, N) { - log((N + 2) / 24) * k - 2 * logLik -} diff --git a/R/generics.R b/R/generics.R deleted file mode 100644 index 3061fae..0000000 --- a/R/generics.R +++ /dev/null @@ -1,218 +0,0 @@ -#' Extract parameterEstimates from an estimated model -#' -#' @param object An object of class `modsem_pi`, `modsem_da`, or `modsem_mplus` -#' @param ... Additional arguments passed to other functions -#' @export -parameter_estimates <- function(object, ...) { - UseMethod("parameter_estimates") -} - - -#' Extract or modify parTable from an estimated model with estimated variances of interaction terms -#' -#' @param object An object of class `modsem_da`, `modsem_mplus`, -#' or a parTable of class `data.frame` -#' @param ... Additional arguments passed to other functions -#' @export -var_interactions <- function(object, ...) { - UseMethod("var_interactions") -} - - -#' @export -var_interactions.data.frame <- function(object, ...) { - parTable <- fillColsParTable(object) - intTermVarRows <- parTable$lhs == parTable$rhs & - grepl(":", parTable$lhs) & parTable$op == "~~" - parTable <- parTable[!intTermVarRows, ] - - intTerms <- unique(parTable[grepl(":", parTable$rhs) & - parTable$op == "~", "rhs"]) - - for (i in seq_len(length(intTerms))) { - # interaction term = XY - XY <- stringr::str_split_fixed(intTerms[[i]], ":", 2) - muX <- getMean(XY[[1]], parTable) - muY <- getMean(XY[[2]], parTable) - varX <- calcCovParTable(XY[[1]], XY[[1]], parTable) - varY <- calcCovParTable(XY[[2]], XY[[2]], parTable) - covXY <- calcCovParTable(XY[[1]], XY[[2]], parTable) - varXZ <- muX ^ 2 * muY ^ 2 + varX * muY ^ 2 + varY * muX ^ 2 + - 2 * muX * muY * covXY + varX * varY + covXY ^ 2 - newRow <- data.frame(lhs = intTerms[[i]], - op = "~~", - rhs = intTerms[[i]], - label = "", - est = varXZ, - std.error = NA, z.value = NA, p.value = NA, - ci.lower = NA, ci.upper = NA) - parTable <- rbind(parTable, newRow) - } - parTable -} - - -#' Get standardized estimates -#' -#' @param object An object of class `modsem_da`, `modsem_mplus`, -#' or a parTable of class `data.frame` -#' @param ... Additional arguments passed to other functions -#' @details for `modsem_da`, and `modsem_mplus` objects, -#' the interaction term is not standardized such that var(xz) = 1. -#' The interaction term is not an actual variable in the model, meaning that it does not -#' have a variance. It must therefore be calculated from the other parameters in the model. -#' Assuming normality and zero-means the variance is calculated as -#' `var(xz) = var(x) * var(z) + cov(x, z)^2`. Thus setting the variance of the interaction -#' term to 1, would only be 'correct' if the correlation between x and z is zero. -#' This means that the standardized estimates for the interaction term will -#' be different from those using lavaan, since there the interaction term is an -#' actual latent variable in the model, with a standardized variance of 1. -#' @export -standardized_estimates <- function(object, ...) { - UseMethod("standardized_estimates") -} - - -#' @export -standardized_estimates.data.frame <- function(object, intercepts = FALSE, ...) { - parTable <- object[c("lhs", "op", "rhs", "label", "est", "std.error")] - if (!intercepts) { # remove intercepts - parTable <- centerInteraction(parTable) - parTable <- parTable[!(parTable$rhs == "1" & parTable$op == "~"), ] - } - parTable <- var_interactions(parTable) - - lVs <- getLVs(parTable) - intTerms <- getIntTerms(parTable) - etas <- getSortedEtas(parTable, isLV = TRUE) - xis <- getXis(parTable, etas = etas, isLV = TRUE) - indsLVs <- getIndsLVs(parTable, lVs) - allInds <- unique(unlist(indsLVs)) - - variances <- vector("list", length = length(allInds) + length(lVs) + - length(intTerms)) - names(variances) <- c(allInds, lVs, intTerms) - - # get variances - for (x in allInds) { - variances[[x]] <- calcCovParTable(x, x, parTable, - measurement.model = TRUE) - } - for (lV in lVs) { - variances[[lV]] <- calcCovParTable(lV, lV, parTable, - measurement.model = FALSE) - } - for (xz in intTerms) { - variances[[xz]] <- parTable[parTable$lhs == xz & - parTable$rhs == xz & - parTable$op == "~~", "est"] - - } - - # factor loadings - lambda <- NULL - selectRows <- NULL - selectCols <- c("est", "std.error") - for (lV in lVs) { - for (ind in indsLVs[[lV]]) { - selectRows <- parTable$lhs == lV & parTable$op == "=~" & - parTable$rhs == ind - lambda <- parTable[selectRows, selectCols] - parTable[selectRows, selectCols] <- - lambda * (sqrt(variances[[lV]]) / sqrt(variances[[ind]])) - } - } - - # structural coefficients - gamma <- NULL - selectStrucExprsEta <- NULL - structExprsEta <- NULL - selectStrucExprs <- parTable$op == "~" & parTable$rhs != "1" & - parTable$lhs %in% etas - for (eta in etas) { - selectStrucExprsEta <- selectStrucExprs & parTable$lhs == eta - structExprsEta <- parTable[selectStrucExprsEta, ] - - for (xi in structExprsEta$rhs) { - selectRows <- selectStrucExprsEta & parTable$rhs == xi - gamma <- parTable[selectRows, selectCols] - parTable[selectRows, selectCols] <- - gamma * (sqrt(variances[[xi]]) / sqrt(variances[[eta]])) - } - } - - # variances / covariances of xis - selectCovXis <- parTable$op == "~~" & parTable$lhs %in% xis - selectRows <- NULL - combosXis <- getUniqueCombos(xis, match = TRUE) - for (i in seq_len(nrow(combosXis))) { - xis <- combosXis[i, , drop = TRUE] - - selectRows <- selectCovXis & parTable$lhs %in% xis & - parTable$rhs %in% xis - if (xis[[1]] != xis[[2]]) { - selectRows <- selectRows & parTable$lhs != parTable$rhs - } - - covXis <- parTable[selectRows, selectCols] - parTable[selectRows, selectCols] <- - covXis / (sqrt(variances[[xis[[1]]]]) * sqrt(variances[[xis[[2]]]])) - } - - # residual variances etas - selectRows <- NULL - residual <- NULL - for (eta in etas) { - selectRows <- parTable$lhs == eta & parTable$op == "~~" & - parTable$rhs == eta - residual <- parTable[selectRows, selectCols] - projected <- calcCovParTable(eta, eta, parTable) - residual - parTable[selectRows, selectCols] <- residual / variances[[eta]] - } - - # residual variances inds - for (ind in allInds) { - selectRows <- parTable$lhs == ind & parTable$op == "~~" & - parTable$rhs == ind - residual <- parTable[selectRows, selectCols] - - parTable[selectRows, selectCols] <- residual / variances[[ind]] - } - - # recalculate variance of interaction terms - # and rescale coefficients for interaction terms - parTable <- var_interactions(parTable) - for (xz in intTerms) { - selectRows <- parTable$rhs == xz & parTable$op == "~" - varXZ <- parTable[parTable$lhs == xz & - parTable$op == "~~" & - parTable$rhs == xz, "est"] - - gamma <- parTable[selectRows, selectCols] - parTable[selectRows, selectCols] <- gamma / sqrt(varXZ) - } - - parTable$z.value <- parTable$est / parTable$std.error - parTable$p.value <- 2 * stats::pnorm(-abs(parTable$z.value)) - parTable$ci.lower <- parTable$est - 1.96 * parTable$std.error - parTable$ci.upper <- parTable$est + 1.96 * parTable$std.error - - parTable -} - - -#' Inspect model information -#' -#' @param object fittet model to inspect -#' @param what what to inspect -#' @param ... Additional arguments passed to other functions -#' @description function used to inspect fittet object. similar to `lavInspect()` -#' argument 'what' decides what to inspect -#' @details for `modsem_da`, and `modsem_lavaan` -#' for `modsem_lavaan`, it is just a wrapper for `lavInspect()` -#' for `modsem_da` and `` what can either be "all", "matrices", "optim", -#' or just the name of what to extract. -#' @export -modsem_inspect <- function(object, what = NULL, ...) { - UseMethod("modsem_inspect") -} diff --git a/R/generics_lavaan.R b/R/generics_lavaan.R deleted file mode 100644 index 4a4cd29..0000000 --- a/R/generics_lavaan.R +++ /dev/null @@ -1,9 +0,0 @@ -#' @export -parameter_estimates.lavaan <- function(object, ...) { - lavaan::parameterEstimates(object, ...) -} - - -isLavaanObject <- function(x) { - inherits(x, "lavaan") -} diff --git a/R/generics_modsem_da.R b/R/generics_modsem_da.R deleted file mode 100644 index 8fd7db8..0000000 --- a/R/generics_modsem_da.R +++ /dev/null @@ -1,464 +0,0 @@ -#' @export -parameter_estimates.modsem_da <- function(object, ...) { - object$parTable -} - - -MODSEM_VERSION <- "1.0.3" - -#' summary for modsem objects -#' -#' @param object modsem object to summarized -#' @param H0 should a null model be estimated (used for comparison) -#' @param verbose print progress for the estimation of null model -#' @param r.squared calculate R-squared -#' @param adjusted.stat should sample size corrected/adjustes AIC and BIC be reported? -#' @param digits number of digits to print -#' @param scientific print p-values in scientific notation -#' @param ci print confidence intervals -#' @param standardized print standardized estimates -#' @param loadings print loadings -#' @param regressions print regressions -#' @param covariances print covariances -#' @param intercepts print intercepts -#' @param variances print variances -#' @param var.interaction if FALSE (default) variances for interaction terms will be removed (if present) -#' @param ... additional arguments -#' @rdname summary -#' @export -#' @examples -#' \dontrun{ -#' m1 <- " -#' # Outer Model -#' X =~ x1 + x2 + x3 -#' Y =~ y1 + y2 + y3 -#' Z =~ z1 + z2 + z3 -#' -#' # Inner model -#' Y ~ X + Z + X:Z -#' " -#' -#' est1 <- modsem(m1, oneInt, "qml") -#' summary(est1, ci = TRUE, scientific = TRUE) -#' } -summary.modsem_da <- function(object, - H0 = TRUE, - verbose = TRUE, - r.squared = TRUE, - adjusted.stat = FALSE, - digits = 3, - scientific = FALSE, - ci = FALSE, - standardized = FALSE, - loadings = TRUE, - regressions = TRUE, - covariances = TRUE, - intercepts = TRUE, - variances = TRUE, - var.interaction = FALSE, - ...) { - method <- object$method - - if (standardized) { - parTable <- standardized_estimates(object) - } else { - parTable <- parameter_estimates(object) - } - - if (!var.interaction) { - parTable <- removeInteractionVariances(parTable) - } - - args <- object$args - out <- list( - parTable = parTable, - data = object$data, - iterations = object$iterations, - logLik = object$logLik, - fit = fit_modsem_da(object, chisq = FALSE), - D = NULL, - N = NROW(object$data), - method = method, - optimizer = object$optimizer, - quad = object$info.quad, - type.se = object$type.se, - type.estimates = ifelse(standardized, "standardized", object$type.estimates), - information = object$information - ) - - if (H0) { - estH0 <- estimateNullModel(object$originalParTable, - data = out$data, - method = method, - cov.syntax = object$model$covModel$syntax, - verbose = verbose, - calc.se = FALSE, - double = args$double, - standardize = args$standardize, - standardize.out = args$standardize.out, - mean.observed = args$mean.observed - ) - - out$nullModel <- estH0 - if (is.null(estH0)) { - warning2("Comparative fit to H0 will not be calculated.") - H0 <- FALSE - out$D <- NULL - out$fitH0 <- NULL - - } else { - out$D <- compare_fit(estH0, object) - out$fitH0 <- fit_modsem_da(estH0) - } - } else { - out$D <- NULL - } - - if (r.squared) { - out$r.squared <- calcRsquared(parTable) - if (H0) out$r.squared$H0 <- calcRsquared(estH0$parTable) - } else { - out$r.squared <- NULL - } - - out$format <- list( - digits = digits, - scientific = scientific, - adjusted.stat = adjusted.stat, - ci = ci, - loadings = loadings, - regressions = regressions, - covariances = covariances, - intercepts = intercepts, - variances = variances - ) - - class(out) <- "summary_da" - out -} - - -#' @export -print.summary_da <- function(x, digits = 3, ...) { - width.out <- getWidthPrintedParTable(x$parTable, - scientific = x$format$scientific, - ci = x$format$ci, - digits = x$format$digits, - loadings = x$format$loadings, - regressions = x$format$regressions, - covariances = x$format$covariances, - intercepts = x$format$intercepts, - variances = x$format$variances) - cat(paste0("\nmodsem (version ", MODSEM_VERSION, "):\n")) - names <- c("Estimator", "Optimization method", "Number of observations", - "Number of iterations", "Loglikelihood", - "Akaike (AIC)", "Bayesian (BIC)") - values <- c(stringr::str_to_upper(c(x$method, x$optimizer)), - x$N, x$iterations, round(x$logLik, 2), round(x$fit$AIC, 2), - round(x$fit$BIC, 2)) - if (x$format$adjusted.stat) { - names <- c(names, "Corrected Akaike (AICc)", "Adjusted Bayesian (aBIC)") - values <- c(values, round(x$fit$AICc, 2), round(x$fit$aBIC, 2)) - } - - cat(allignLhsRhs(lhs = names, rhs = values, pad = " ", - width.out = width.out), "\n") - - if (!is.null(x$quad)) { - cat("Numerical Integration:\n") - names <- c("Points of integration (per dim)", "Dimensions", - "Total points of integration") - values <- c(x$quad$nodes.dim, x$quad$dim, x$quad$nodes.total) - cat(allignLhsRhs(lhs = names, rhs = values, pad = " ", - width.out = width.out), "\n") - - } - - if (!is.null(x$D)) { - cat("Fit Measures for H0:\n") - names <- c("Loglikelihood", "Akaike (AIC)", "Bayesian (BIC)") - values <- c(round(x$nullModel$logLik), round(x$fitH0$AIC, 2), round(x$fitH0$BIC, 2)) - - if (x$format$adjusted.stat) { - names <- c(names, "Corrected Akaike (AICc)", "Adjusted Bayesian (aBIC)") - values <- c(values, round(x$fitH0$AICc, 2), round(x$fitH0$aBIC, 2)) - } - - names <- c(names, "Chi-square", "Degrees of Freedom (Chi-square)", - "P-value (Chi-square)", "RMSEA") - values <- c(values, formatNumeric(x$fitH0$chisq.value, digits = 2), - x$fitH0$chisq.df, - formatPval(x$fitH0$chisq.pvalue, scientific = x$format$scientific), - formatNumeric(x$fitH0$RMSEA, digits = 3)) - cat(allignLhsRhs(lhs = names, rhs = values, pad = " ", - width.out = width.out), "\n") - - cat("Comparative fit to H0 (no interaction effect)\n") - names <- c("Loglikelihood change", - "Difference test (D)", - "Degrees of freedom (D)", "P-value (D)") - values <- c(formatNumeric(x$D$llChange, digits = 2), - formatNumeric(x$D$D, digits = 2), - x$D$df, - formatPval(x$D$p, scientific = x$format$scientific)) - cat(allignLhsRhs(lhs = names, rhs = values, pad = " ", - width.out = width.out), "\n") - - } - - if (!is.null(x$r.squared)) { - r.squared <- x$r.squared - r.squared$Rsqr <- formatNumeric(r.squared$Rsqr, digits = 3) - maxWidth <- max(vapply(r.squared$Rsqr, FUN.VALUE = numeric(1), FUN = nchar)) - r.squared$Rsqr <- - stringr::str_pad(r.squared$Rsqr, width = maxWidth, side = "left") - - cat("R-Squared:\n") - names <- r.squared$eta - values <- character(length(names)) - for (i in seq_along(r.squared$eta)) { - values[[i]] <- r.squared$Rsqr[[i]] - } - cat(allignLhsRhs(lhs = names, rhs = values, pad = " ", - width.out = width.out)) - - if (!is.null(r.squared$H0)) { - r.squared$H0$Rsqr <- formatNumeric(r.squared$H0$Rsqr, digits = 3) - maxWidth <- - max(vapply(r.squared$H0$Rsqr, FUN.VALUE = numeric(1), FUN = nchar)) - r.squared$H0$Rsqr <- - stringr::str_pad(r.squared$H0$Rsqr, width = maxWidth, side = "left") - - cat("R-Squared Null-Model (H0):\n") - names <- r.squared$H0$eta - for (i in seq_along(names)) { - values[[i]] <- formatNumeric(r.squared$H0$Rsqr[[i]], digits = 3) - } - cat(allignLhsRhs(lhs = names, rhs = values, pad = " ", - width.out = width.out)) - - # Calculate Change (using unformatted Rsquared) - r.squared$H0$diff <- - formatNumeric(x$r.squared$Rsqr - x$r.squared$H0$Rsqr, digits = 3) - cat("R-Squared Change:\n") - for (i in seq_along(names)) { - values[[i]] <- r.squared$H0$diff[[i]] - } - cat(allignLhsRhs(lhs = names, rhs = values, pad = " ", - width.out = width.out)) - } - } - - cat("\nParameter Estimates:\n") - names <- c("Coefficients", "Information", "Standard errors") - values <- c(x$type.estimates, x$information, x$type.se) - cat(allignLhsRhs(lhs = names, rhs = values, pad = " ", - width.out = width.out), "\n") - - printParTable(x$parTable, - scientific = x$format$scientific, - ci = x$format$ci, - digits = x$format$digits, - loadings = x$format$loadings, - regressions = x$format$regressions, - covariances = x$format$covariances, - intercepts = x$format$intercepts, - variances = x$format$variances) -} - - -#' @export -print.modsem_da <- function(x, digits = 3, ...) { - parTable <- x$parTable - parTable$p.value <- format.pval(parTable$p.value, digits = digits) - names(parTable) <- c( - "lhs", "op", "rhs", - "est", "std.error", - "z.value", "p.value", # "P(>|z|)", - "ci.lower", "ci.upper" - ) - est <- lapply(parTable, function(col) { - if (is.numeric(col)) round(col, digits) else col - }) |> - as.data.frame() - print(est) -} - - -estimateNullModel <- function(parTable, - data, - method = "lms", - cov.syntax = NULL, - verbose = FALSE, - calc.se = FALSE, - standardize = NULL, - standardize.out = NULL, - mean.observed = NULL, - double = NULL, - ...) { - tryCatch({ - strippedParTable <- removeUnknownLabels(parTable[!grepl(":", parTable$rhs), ]) - if (NROW(strippedParTable) == NROW(parTable)) { - return(NULL) - } - - syntax <- parTableToSyntax(strippedParTable) - if (verbose) cat("Estimating null model\n") - modsem_da(syntax, data, method, - verbose = verbose, - cov.syntax = cov.syntax, - calc.se = calc.se, - double = double, - standardize = standardize, - standardize.out = standardize.out, - mean.observed = mean.observed, ...) - }, - error = function(e) { - warning2( - "Null model could not be estimated. ", - "Error message: ", e$message - ) - NULL - } - ) -} - - - -#' compare model fit for qml and lms models -#' -#' @param estH0 object of class `modsem_da` representing the -#' null hypothesis model -#' @param estH1 object of class `modsem_da` representing the -#' @description Compare the fit of two models using the likelihood ratio test. -#' `estH0` representing the null -#' hypothesis model, and `estH1` the alternative hypothesis model. Importantly, -#' the function assumes that `estH0` does not have more free parameters -#' (i.e., degrees of freedom) than `estH1`. -#' alternative hypothesis model -#' @rdname compare_fit -#' @export -#' @examples -#' \dontrun{ -#' H0 <- " -#' # Outer Model -#' X =~ x1 + x2 + x3 -#' Y =~ y1 + y2 + y3 -#' Z =~ z1 + z2 + z3 -#' -#' # Inner model -#' Y ~ X + Z -#' " -#' -#' estH0 <- modsem(m1, oneInt, "lms") -#' -#' H1 <- " -#' # Outer Model -#' X =~ x1 + x2 + x3 -#' Y =~ y1 + y2 + y3 -#' Z =~ z1 + z2 + z3 -#' -#' # Inner model -#' Y ~ X + Z + X:Z -#' " -#' -#' estH1 <- modsem(m1, oneInt, "lms") -#' compare_fit(estH0, estH1) -#' } -#' @export -compare_fit <- function(estH0, estH1) { - if (is.null(estH0) || is.null(estH1)) { - return(NULL) - } - df <- length(coef(estH1)) - length(coef(estH0)) - D <- -2 * (estH0$logLik - estH1$logLik) - p <- stats::pchisq(D, df = df, lower.tail = FALSE, log.p = FALSE) - list(D = D, df = df, p = p, llChange = estH1$logLik - estH0$logLik) -} - - -calcRsquared <- function(parTable) { - parTable <- var_interactions.data.frame(parTable) - etas <- unique(parTable$lhs[parTable$op == "~" & - parTable$rhs != "1"]) - - # Calculate Variances/R squared of Etas - variances <- residuals <- Rsqr <- vector("numeric", length(etas)) - for (i in seq_along(etas)) { - variances[[i]] <- calcCovParTable(etas[[i]], etas[[i]], parTable) - residuals[[i]] <- parTable$est[parTable$lhs == etas[[i]] & - parTable$op == "~~" & - parTable$rhs == etas[[i]]] |> - as.numeric() - Rsqr[[i]] <- 1 - residuals[[i]] / variances[[i]] - } - data.frame( - eta = etas, variance = variances, - residual = residuals, Rsqr = Rsqr - ) -} - - -#' @export -var_interactions.modsem_da <- function(object, ...) { - var_interactions.data.frame(parameter_estimates(object)) -} - - -#' @export -standardized_estimates.modsem_da <- function(object, ...) { - standardized_estimates.data.frame(parameter_estimates(object)) -} - - -#' @export -modsem_inspect.modsem_da <- function(object, what = NULL, ...) { - if (is.null(what)) what <- "default" - modsem_inspect_da(object, what = what, ...) -} - - -#' @export -#' @importFrom stats vcov -vcov.modsem_da <- function(object, ...) { - modsem_inspect_da(object, what = "vcov")[[1]] -} - - -#' @export -#' @importFrom stats coefficients -coefficients.modsem_da <- function(object, ...) { - modsem_inspect_da(object, what = "coefficients")[[1]] -} - - -#' @export -#' @importFrom stats coef -coef.modsem_da <- function(object, ...) { - modsem_inspect_da(object, what = "coefficients")[[1]] -} - - - -#' Wrapper for vcov -#' -#' @param object fittet model to inspect -#' @param ... additional arguments -#' @description wrapper for vcov, to be used with modsem::vcov_modsem_da, -#' since vcov is not in the namespace of modsem, but stats -#' @export -vcov_modsem_da <- function(object, ...) { - vcov.modsem_da(object, ...) -} - - -#' Wrapper for coef -#' -#' @param object fittet model to inspect -#' @param ... additional arguments -#' @description wrapper for coef, to be used with modsem::coef_modsem_da, -#' since coef is not in the namespace of modsem, but stats -#' @export -coef_modsem_da <- function(object, ...) { - coef.modsem_da(object, ...) -} diff --git a/R/generics_modsem_mplus.R b/R/generics_modsem_mplus.R deleted file mode 100644 index abaa7ca..0000000 --- a/R/generics_modsem_mplus.R +++ /dev/null @@ -1,77 +0,0 @@ -#' summary for modsem objects -#' -#' @param object modsem object to summarized -#' @param scientific print p-values in scientific notation -#' @param standardize standardize estimates -#' @param ci print confidence intervals -#' @param digits number of digits to print -#' @param loadings print loadings -#' @param regressions print regressions -#' @param covariances print covariances -#' @param intercepts print intercepts -#' @param variances print variances -#' @param ... arguments passed to other functions -#' @rdname summary -#' @export -summary.modsem_mplus <- function(object, - scientific = FALSE, - standardize = FALSE, - ci = FALSE, - digits = 3, - loadings = TRUE, - regressions = TRUE, - covariances = TRUE, - intercepts = TRUE, - variances = TRUE, - ...) { - if (standardize) object$parTable <- standardized_estimates(object) - object$format <- list(digits = digits, - scientific = scientific, - ci = ci, - loadings = loadings, - regressions = regressions, - covariances = covariances, - intercepts = intercepts, - variances = variances) - structure(object, class = "summary_mplus") -} - - -#' @export -print.summary_mplus <- function(x, ...) { - cat("modsem: \nMethod =", attributes(x)$method, "\n") - printParTable(x$parTable, - scientific = x$format$scientific, - ci = x$format$ci, - digits = x$format$digits, - loadings = x$format$loadings, - regressions = x$format$regressions, - covariances = x$format$covariances, - intercepts = x$format$intercepts, - variances = x$format$variances) -} - - -#' @export -print.modsem_mplus <- function(x, ...) { - cat("modsem: \nMethod =", attributes(x)$method, "\n") - print(x$parTable) -} - - -#' @export -parameter_estimates.modsem_mplus <- function(object, ...) { - object$parTable -} - - -#' @export -var_interactions.modsem_mplus <- function(object, ...) { - var_interactions.data.frame(parameter_estimates(object)) -} - - -#' @export -standardized_estimates.modsem_mplus <- function(object, ...) { - standardized_estimates.data.frame(parameter_estimates(object)) -} diff --git a/R/generics_modsem_pi.R b/R/generics_modsem_pi.R deleted file mode 100644 index a844b40..0000000 --- a/R/generics_modsem_pi.R +++ /dev/null @@ -1,50 +0,0 @@ -#' summary for modsem objects -#' -#' @param object modsem object to summarized -#' @param ... arguments passed to lavaan::summary() -#' @rdname summary -#' @export -summary.modsem_pi <- function(object, ...) { - cat("modsem: \nMethod =", attributes(object)$method, "\n") - lavaan::summary(object$lavaan, ...) -} - - -#' @export -parameter_estimates.modsem_pi <- function(object, ...) { - lavaan::parameterEstimates(object$lavaan, ...) -} - - -#' @export -standardized_estimates.modsem_pi <- function(object, ...) { - lavaan::standardizedSolution(object$lavaan, ...) -} - - -#' @export -modsem_inspect.modsem_pi <- function(object, what = NULL, ...) { - if (is.null(what)) what <- "free" - lavaan::lavInspect(object$lavaan, what = what, ...) -} - - -#' @export -#' @importFrom stats vcov -vcov.modsem_pi <- function(object, ...) { - lavaan::vcov(object$lavaan, ...) -} - - -#' @export -#' @importFrom stats coef -coef.modsem_pi <- function(object, ...) { - lavaan::coef(object$lavaan, ...) -} - - -#' @export -#' @importFrom stats coefficients -coefficients.modsem_pi <- function(object, ...) { - lavaan::coef(object$lavaan, ...) -} diff --git a/R/inspect_da.R b/R/inspect_da.R deleted file mode 100644 index 49d076a..0000000 --- a/R/inspect_da.R +++ /dev/null @@ -1,47 +0,0 @@ -inspectDA_Matrices <- c("lambda", "tau", "theta", "gamma_xi", - "gamma_eta", "omega_xi_xi", - "omega_eta_xi", "phi", "psi", "alpha") - -inspectDA_Optim <- c("vcov", "FIM", "data", "coefficients", - "loglik", "iterations", "convergence") - -modsem_inspect_da <- function(model, what = "default") { - matrices <- model$model$matrices - matricesCovModel <- model$model$covModel$matrices - info <- list(vcov = model$vcov, - FIM = model$FIM, - data = model$data, - coefficients = model$theta, - partable = model$parTable, - originalpartable = model$originalParTable , - loglik = model$logLik, - AIC = model$AIC, - iterations = model$iterations, - convergence = model$convergence, - lambda = diagPartitionedMat(matrices$lambdaX, - matrices$lambdaY), - tau = diagPartitionedMat(matrices$tauX, - matrices$tauY), - theta = diagPartitionedMat(matrices$thetaDelta, - matrices$thetaEpsilon), - gamma_xi = diagPartitionedMat(matrices$gammaXi, - matricesCovModel$gammaXi), - gamma_eta = diagPartitionedMat(matrices$gammaEta, - matricesCovModel$gammaEta), - omega_xi_xi = diagPartitionedMat(matrices$omegaXiXi, - matricesCovModel$omegaXiXi), - omega_eta_xi = diagPartitionedMat(matrices$omegaEtaXi, - matricesCovModel$omegaEtaXi), - phi = diagPartitionedMat(matrices$phi, - matricesCovModel$phi), - psi = diagPartitionedMat(matrices$psi, - matricesCovModel$psi), - alpha = matrices$alpha) - - switch(what, - default = info[names(info) != "data"], - all = info, - matrices = info[inspectDA_Matrices], - optim = info[inspectDA_Optim], - info[what]) -} diff --git a/R/labelled_params_da.R b/R/labelled_params_da.R deleted file mode 100644 index 751566c..0000000 --- a/R/labelled_params_da.R +++ /dev/null @@ -1,120 +0,0 @@ -createThetaLabel <- function(labelMatrices, labelMatricesCov, - constrExprs, start = NULL) { - matrices <- c(labelMatrices, labelMatricesCov) - labels <- lapply(matrices, FUN = function(x) { - select <- apply(x, MARGIN = 2, FUN = function(z) - !canBeNumeric(z, includeNA = TRUE)) - as.vector(x[select]) }) |> unlist() |> unique() - - if (!is.null(constrExprs)) { - labels <- labels[!labels %in% constrExprs$fixedParams] - } - - if (is.null(start)) { - start <- vapply(labels, FUN.VALUE = vector("numeric", 1L), - FUN = function(x) stats::runif(1)) - } - - names(start) <- labels - start -} - - -calcThetaLabel <- function(theta, constrExprs) { - if (is.null(constrExprs)) return(theta) - Theta <- c(as.list(theta), constrExprs$thetaFixedParams) - - for (i in seq_along(constrExprs$fixedParams)) { - Theta[constrExprs$fixedParams[[i]]] <- - eval(constrExprs$exprs[[i]], envir = Theta) - } - - unlist(Theta) -} - - -fillMatricesLabels <- function(matrices, labelMatrices, thetaLabel, - constraintExprs = NULL) { - if (is.null(thetaLabel)) return(matrices) - labels <- names(thetaLabel) - purrr::map2(.x = matrices, .y = labelMatrices, .f = function(M, L) { - lapply(labels, FUN = function(label) { - M[L == label] <<- thetaLabel[[label]] - }) - M - }) -} - - -getConstrExprs <- function(parTable, parTableCov) { - parTable <- rbind(parTable, parTableCov) - rows <- sortConstrExprs(parTable) - if (is.null(rows)) return(NULL) - - fixedParams <- unique(rows$lhs) - thetaFixedParams <- vector("list", length(fixedParams)) - names(thetaFixedParams) <- fixedParams - - exprs <- lapply(rows$rhs, function(expr) parse(text = expr)) - list(fixedParams = fixedParams, thetaFixedParams = thetaFixedParams, - exprs = exprs) -} - - -sortConstrExprs <- function(parTable) { - rows <- parTable[parTable$op %in% c("==", ">", "<"), ] - if (NROW(rows) == 0) return(NULL) - - labelled <- unique(parTable$mod[parTable$mod != ""]) - - if (!all(rows$lhs %in% labelled)) { - stop2("Unknown labels in constraints: ", rows$lhs[!rows$lhs %in% labelled]) - - } else if (length(unique(rows$lhs)) != length(rows$lhs)) { - stop2("Duplicated labels in constraints:\n", capturePrint(table(rows$lhs))) - - } else if (any(rows$op %in% c(">", "<"))) { - stop2("Constraints with '>' and '<' are not implemented yet") - } - - definedLabels <- labelled[!labelled %in% rows$lhs] - subRows <- rows - sortedRows <- data.frame(lhs = NULL, op = NULL, lhs = NULL, mod = NULL) - while (NROW(subRows) > 0) { - matchedAny <- FALSE - for (i in seq_len(nrow(subRows))) { - labels_i <- getVarsExpr(subRows[i, "rhs"]) - if (length(labels_i) == 0 || all(labels_i %in% definedLabels)) { - matchedAny <- TRUE - sortedRows <- rbind(sortedRows, subRows[i, ]) - definedLabels <- c(definedLabels, subRows[i, "lhs"]) - subRows <- subRows[-i, ] - break - } - } - - if (!matchedAny) { - stop2("Unkown labels in constraints: ", - labels_i[!labels_i %in% definedLabels]) - } - } - - if (NROW(sortedRows) != NROW(rows)) { - warning2("Something went wrong when sorting and parsing constraint-expressions ", - "attempting to estimate model with unsorted expressions") - return(rows) - } - sortedRows -} - - -getVarsExpr <- function(expr) { - tokens <- createTokensLine(getCharsLine(expr)) - tokens[vapply(tokens, FUN.VALUE = logical(1L), FUN = is.LavName)] |> - lapply(as.character) |> unlist() -} - - -removeConstraintExpressions <- function(parTable) { - parTable[!parTable$op %in% c("==", "<", ">"), ] -} diff --git a/R/lav_syntax_functions.R b/R/lav_syntax_functions.R deleted file mode 100644 index 2a31e07..0000000 --- a/R/lav_syntax_functions.R +++ /dev/null @@ -1,44 +0,0 @@ -# function for finding index for matching ) to a function call -findFunctionEnd <- function(listTokens, i = 1) { - if (i > length(listTokens)) { - stop2("No matching parenthesis for function call") - - } else if (listTokens[[i]] == ")") { - return(i) - - } else { - findFunctionEnd(listTokens, i + 1) - } -} - - -LavEqual <- function(string) { - if (!is.character(string)) { - stop2("Expected argument in equal() to be string, got: ", string) - } else if (length(string) > 1) { - stop2("Expected a single string in equal(), got: ", string) - } - paste0("equal(\"", string, "\")") -} - - -LavStart <- function(number) { - if (!is.numeric(number)) { - stop2("Expected argument in start() to be string, got: ", number) - } else if (length(number) > 1) { - stop2("Expected a single number in start(), got: ", number) - } - paste0("start(", number, ")") -} - - -LavConcat <- function(...) { - as.character(substitute(expression(c(...))))[[2]] -} - - -modEnv <- rlang::env( - equal = LavEqual, - start = LavStart, - c = LavConcat -) diff --git a/R/lavaan_labels.R b/R/lavaan_labels.R deleted file mode 100644 index 25526c8..0000000 --- a/R/lavaan_labels.R +++ /dev/null @@ -1,93 +0,0 @@ -combineLavLabels <- function(lavLabelsCov, lavLabelsMain, currentLabels) { - lavLabels <- c(lavLabelsCov, lavLabelsMain) - finalLabels <- currentLabels - finalLabels[finalLabels %in% names(lavLabels)] <- - lavLabels[names(lavLabels) %in% finalLabels] - finalLabels -} - - -createLavLabels <- function(matrices, subset, etas) { - lambdaX <- createLabelsMatrix(matrices$lambdaX, op = "~") - lambdaY <- createLabelsMatrix(matrices$lambdaY, op = "~") - thetaDelta <- createLabelsMatrix(matrices$thetaDelta, op = "~~") - thetaEpsilon <- createLabelsMatrix(matrices$thetaEpsilon, op = "~~") - phi <- createLabelsMatrix(matrices$phi, op = "~~") - A <- createLabelsMatrix(matrices$A, op = "~~") - psi <- createLabelsMatrix(matrices$psi, op = "~~") - tauX <- createLabelsMatrix(matrices$tauX, op = "~", first = "rows") - tauY <- createLabelsMatrix(matrices$tauY, op = "~", first = "rows") - alpha <- createLabelsMatrix(matrices$alpha, op = "~", first = "rows") - beta0 <- createLabelsMatrix(matrices$beta0, op = "~") - gammaXi <- createLabelsMatrix(matrices$gammaXi, op = "~", first = "rows") - gammaEta <- createLabelsMatrix(matrices$gammaEta, op = "~", first = "rows") - omegaXiXi <- createLabelsOmega(matrices$omegaXiXi) - omegaEtaXi <- createLabelsOmega(matrices$omegaEtaXi) - - labels <- c("lambdaX" = lambdaX, - "lambdaY" = lambdaY, - "tauX" = tauX, - "tauY" = tauY, - "thetaDelta" = thetaDelta, - "thetaEpsilon" = thetaEpsilon, - "phi" = phi, - "A" = A, - "psi" = psi, - "alpha" = alpha, - "beta0" = beta0, - "gammaXi" = gammaXi, - "gammaEta" = gammaEta, - "omegaXiXi" = omegaXiXi, - "omegaEtaXi" = omegaEtaXi) - - labels[subset] -} - - -createLavLabelsCov <- function(matrices, subset) { - if (is.null(matrices)) return(NULL) - - phi <- createLabelsMatrix(matrices$phi, op = "~~") - A <- createLabelsMatrix(matrices$A, op = "~~") - psi <- createLabelsMatrix(matrices$psi, op = "~~") - gammaXi <- createLabelsMatrix(matrices$gammaXi, op = "~", first = "rows") - gammaEta <- createLabelsMatrix(matrices$gammaEta, op = "~", first = "rows") - - labels <- c("phi" = phi, - "A" = A, - "psi" = psi, - "gammaXi" = gammaXi, - "gammaEta" = gammaEta) - - labels[subset] -} - - -createLabelsMatrix <- function(X, op = "~", first = "cols") { - labels <- character(0L) - rows <- rownames(X) - cols <- colnames(X) - - # this is ugly, but... we have to read by cols first - getLabel <- switch(first, cols = function(col, row) paste0(col, op, row), - rows = function(col, row) paste0(row, op, col)) - - for (i in seq_len(ncol(X))) for (j in seq_len(nrow(X))) { - labels <- c(labels, getLabel(col = cols[[i]], row = rows[[j]])) - } - - labels -} - - -createLabelsOmega <- function(X) { - rows <- rownames(X) - cols <- colnames(X) - labels <- character(0L) - - for (i in seq_len(ncol(X))) for (j in seq_len(nrow(X))) { - labels <- c(labels, paste0(rows[[j]], ":", cols[[i]])) - } - - labels -} diff --git a/R/lexer.R b/R/lexer.R deleted file mode 100644 index 130aa59..0000000 --- a/R/lexer.R +++ /dev/null @@ -1,98 +0,0 @@ -createExprNode <- function(node, lhs = NULL, rhs = NULL, - priority = 1) { - structure(list(node = node, - lhs = lhs, - rhs = rhs), - - class = "exprNode", - priority = priority) -} - - -getMinTokenPriority <- function(listTokens, min = NA) { - if (is.null(listTokens) || length(listTokens) == 0) { - stopif(is.na(min), "Unable to find minimum priority for tokens") - return(min) - } - - tokenPriority <- getTokenPriority(listTokens[[1]]) - if (is.na(min) || (tokenPriority < min)) { - min <- tokenPriority - } - getMinTokenPriority(listTokens[-1], min) -} - - -chooseToken <- function(listTokens, i = 1, chosenTokenIdx = NULL, - leftClosures = list()) { - - if (is.null(listTokens) || i > length(listTokens)) { - stopif(is.null(chosenTokenIdx) && length(leftClosures) > 0, - "Unmatched left bracket", last(leftClosures)) - return(chosenTokenIdx) - } - token <- listTokens[[i]] - - if (is.LeftClosure(token)) { - leftClosures <- appendToList(leftClosures, token) - - } else if (is.RightClosure(token)) { - stopif(length(leftClosures) == 0, "Unmatched right bracket", - highlightErrorToken(token)) - - leftClosures <- leftClosures[-1] - if (length(leftClosures) == 0) { - return(i) - } - } - - if (length(leftClosures) == 0) { - if (is.null(chosenTokenIdx)) { - chosenTokenIdx <- i - } - if (getTokenPriority(token) < getTokenPriority(listTokens[[chosenTokenIdx]])) { - chosenTokenIdx <- i - } - } - chooseToken(listTokens, i + 1, chosenTokenIdx, leftClosures) -} - - -createSyntaxTreeLine <- function(listTokens, i = 1) { - minPriority <- getMinTokenPriority(listTokens) - if (i > length(listTokens) || - length(listTokens) == 1 & - getTokenPriority(listTokens[[1]]) < 0) { - return(NULL) - } - - if (getTokenPriority(listTokens[[i]]) == minPriority) { - if (i != 1) { - lhs <- createSyntaxTreeLine(listTokens[1:(i-1)], i = 1) - } else lhs <- NULL - - if (i != length(listTokens)) { - rhs <- createSyntaxTreeLine(listTokens[(i + 1):length(listTokens)], i = 1) - } else rhs <- NULL - - return( - createExprNode(listTokens[[i]], lhs = lhs, rhs = rhs, - priority = getTokenPriority(listTokens[[i]])) - ) - } - createSyntaxTreeLine(listTokens, i + 1) -} - - -createSyntaxTreesSyntax <- function(syntax) { - tokensLines <- tokenizeSyntax(syntax) - lapply(tokensLines, createSyntaxTreeLine) -} - - -last <- function(x) { - if (is.null(x) || length(x) == 0) { - return(NULL) - } - x[[length(x)]] -} diff --git a/R/method_settings_da.R b/R/method_settings_da.R deleted file mode 100644 index ff4de5d..0000000 --- a/R/method_settings_da.R +++ /dev/null @@ -1,91 +0,0 @@ -getMethodSettingsDA <- function(method, args = NULL) { - settings <- list( - lms = list(verbose = FALSE, - optimize = TRUE, - nodes = 24, - convergence = 1e-4, - optimizer = "nlminb", - center.data = FALSE, - standardize.data = FALSE, - standardize.out = FALSE, - standardize = FALSE, - mean.observed = TRUE, - double = FALSE, - calc.se = TRUE, - FIM = "expected", - OFIM.hessian = FALSE, - EFIM.S = 3e4, - EFIM.parametric = TRUE, - robust.se = FALSE, - max.iter = 500, - max.step = 1, - fix.estep = TRUE, - epsilon = 1e-4, - quad.range = Inf, - n.threads = NULL), - qml = list(verbose = FALSE, - optimize = TRUE, - nodes = 0, - convergence = 1e-6, - optimizer = "nlminb", - center.data = FALSE, - standardize = FALSE, - standardize.data = FALSE, - standardize.out = FALSE, - mean.observed = TRUE, - double = FALSE, - calc.se = TRUE, - FIM = "observed", - OFIM.hessian = TRUE, - EFIM.S = 3e4, - EFIM.parametric = TRUE, - robust.se = FALSE, - max.iter = 500, - max.step = NULL, - fix.estep = NULL, - epsilon = 1e-8, - quad.range = Inf, - n.threads = NULL) - ) - - if (is.null(args)) return(settings[method]) - - settingNames <- unique(unlist(lapply(settings, FUN = names))) - args <- args[settingNames] - isMissing <- vapply(args, FUN.VALUE = logical(1L), FUN = is.null) - missingArgs <- settingNames[isMissing] - - if (!method %in% names(settings)) { - stop2("Unrecognized method") - } - - args.out <- c(settings[[method]][missingArgs], args[!isMissing]) - - args.out$standardize.data <- - args.out$standardize || args.out$standardize.data - args.out$standardize.out <- - args.out$standardize || args.out$standardize.out - args.out$mean.observed <- - !args.out$standardize && args.out$mean.observed - args.out$OFIM.hessian <- - args.out$OFIM.hessian && !args.out$robust.se - - args.out$n.threads <- setThreads(args.out$n.threads) - args.out -} - - - -#' default arguments fro LMS and QML approach -#' -#' @param method which method to get the settings for -#' @return list -#' @export -#' @description -#' This function returns the default settings for the LMS and QML approach. -#' @examples -#' library(modsem) -#' default_settings_da() -default_settings_da <- function(method = c("lms", "qml")) { - getMethodSettingsDA(method = method, args = NULL) -} diff --git a/R/method_settings_pi.R b/R/method_settings_pi.R deleted file mode 100644 index 939042b..0000000 --- a/R/method_settings_pi.R +++ /dev/null @@ -1,83 +0,0 @@ -getMethodSettingsPI <- function(method, args) { - defaultResCov <- "simple" - settings <- list( - rca = list( - center.before = FALSE, - center.after = FALSE, - residuals.prods = TRUE, - residual.cov.syntax = TRUE, - constrained.prod.mean = FALSE, - constrained.loadings = FALSE, - constrained.var = FALSE, - constrained.res.cov.method = defaultResCov, - match = FALSE), - uca = list( - center.before = TRUE, - center.after = FALSE, - residuals.prods = FALSE, - residual.cov.syntax = TRUE, - constrained.prod.mean = TRUE, - constrained.loadings = FALSE, - constrained.var = FALSE, - constrained.res.cov.method = defaultResCov, - match = FALSE), - pind = list( - center.before = FALSE, - center.after = FALSE, - residuals.prods = FALSE, - residual.cov.syntax = FALSE, - constrained.prod.mean = FALSE, - constrained.loadings = FALSE, - constrained.var = FALSE, - constrained.res.cov.method = defaultResCov, - match = FALSE), - dblcent = list( - center.before = TRUE, - center.after = TRUE, - residuals.prods = FALSE, - residual.cov.syntax = TRUE, - constrained.prod.mean = FALSE, - constrained.loadings = FALSE, - constrained.var = FALSE, - constrained.res.cov.method = defaultResCov, - match = FALSE), - ca = list( - center.before = TRUE, - center.after = FALSE, - residuals.prods = FALSE, - residual.cov.syntax = TRUE, - constrained.prod.mean = TRUE, - constrained.loadings = TRUE, - constrained.var = TRUE, - constrained.res.cov.method = "ca", - match = TRUE) - ) - - settingNames <- unique(unlist(lapply(settings, FUN = names))) - args <- args[settingNames] - - if (is.null(args)) return(settings[method]) - - isMissing <- vapply(args, FUN.VALUE = logical(1L), FUN = is.null) - missingArgs <- settingNames[isMissing] - if (!method %in% names(settings)) { - stop2("Unrecognized method") - } - c(settings[[method]][missingArgs], args[!isMissing]) -} - - - -#' default arguments for product indicator approaches -#' -#' @param method which method to get the settings for -#' @return list -#' @export -#' @description -#' This function returns the default settings for the product indicator approaches -#' @examples -#' library(modsem) -#' default_settings_pi() -default_settings_pi <- function(method = c("rca", "uca", "pind", "dblcent", "ca")) { - getMethodSettingsPI(method = method, args = NULL) -} diff --git a/R/model_da.R b/R/model_da.R deleted file mode 100644 index 931a42d..0000000 --- a/R/model_da.R +++ /dev/null @@ -1,455 +0,0 @@ -# Functions -specifyModelDA <- function(syntax = NULL, - data = NULL, - method = "lms", - m = 16, - cov.syntax = NULL, - double = FALSE, - parTable = NULL, - parTableCovModel = NULL, - auto.constraints = TRUE, - createTheta = TRUE, - mean.observed = TRUE, - standardize.inp = FALSE, - standardize.out = FALSE, - checkModel = TRUE, - quad.range = Inf) { - if (!is.null(syntax)) parTable <- modsemify(syntax) - stopif(is.null(parTable), "No parTable found") - - # additions to lavaan-syntax for optimizer - lavOptimizerSyntaxAdditions <- "" - - # endogenous variables (etas)model - etas <- getSortedEtas(parTable, isLV = TRUE, checkAny = TRUE) - numEtas <- length(etas) - - indsEtas <- getIndsLVs(parTable, etas) - numIndsEtas <- vapply(indsEtas, FUN.VALUE = vector("integer", 1L), - FUN = length) - allIndsEtas <- unlist(indsEtas) - numAllIndsEtas <- length(allIndsEtas) - - # exogenouts variables (xis) and interaction terms - intTerms <- getIntTermRows(parTable) - varsInts <- getVarsInts(intTerms) - allVarsInInts <- unique(unlist(varsInts)) - xis <- getXis(parTable, checkAny = TRUE) - numXis <- length(xis) - - omegaAndSortedXis <- sortXisConstructOmega(xis, varsInts, etas, intTerms, - method = method, double = double) - xis <- omegaAndSortedXis$sortedXis # get sorted xis according to interaction terms - - indsXis <- getIndsLVs(parTable, xis) - numIndsXis <- vapply(indsXis, FUN.VALUE = vector("integer", 1L), - FUN = length) - allIndsXis <- unlist(indsXis) - numAllIndsXis <- length(allIndsXis) - - # measurement model x - listLambdaX <- constructLambda(xis, indsXis, parTable = parTable, - auto.constraints = auto.constraints) - lambdaX <- listLambdaX$numeric - labelLambdaX <- listLambdaX$label - - listTauX <- constructTau(xis, indsXis, parTable = parTable, - mean.observed = mean.observed) - tauX <- listTauX$numeric - labelTauX <- listTauX$label - lavOptimizerSyntaxAdditions <- paste0(lavOptimizerSyntaxAdditions, - listTauX$syntaxAdditions) - - listThetaDelta <- constructTheta(xis, indsXis, parTable = parTable, - auto.constraints = auto.constraints) - thetaDelta <- listThetaDelta$numeric - thetaLabelDelta <- listThetaDelta$label - - # measurement model y - listLambdaY <- constructLambda(etas, indsEtas, parTable = parTable, - auto.constraints = auto.constraints) - lambdaY <- listLambdaY$numeric - labelLambdaY <- listLambdaY$label - - listTauY <- constructTau(etas, indsEtas, parTable = parTable, - mean.observed = mean.observed) - tauY <- listTauY$numeric - labelTauY <- listTauY$label - lavOptimizerSyntaxAdditions <- paste0(lavOptimizerSyntaxAdditions, - listTauY$syntaxAdditions) - - listThetaEpsilon <- constructTheta(etas, indsEtas, parTable = parTable, - auto.constraints = auto.constraints) - thetaEpsilon <- listThetaEpsilon$numeric - thetaLabelEpsilon <- listThetaEpsilon$label - - # structural model - Ieta <- diag(numEtas) # used for (B^-1 = (Ieta - gammaEta)^-1) - listGammaXi <- constructGamma(etas, xis, parTable = parTable) - gammaXi <- listGammaXi$numeric - labelGammaXi <- listGammaXi$label - - listGammaEta <- constructGamma(etas, etas, parTable = parTable) - gammaEta <- listGammaEta$numeric - labelGammaEta <- listGammaEta$label - - # covariance matrices - listPsi <- constructPsi(etas, parTable = parTable) - psi <- listPsi$numeric - labelPsi <- listPsi$label - - listPhi <- constructPhi(xis, method = method, cov.syntax = cov.syntax, - parTable = parTable) - phi <- listPhi$numeric - labelPhi <- listPhi$label - - listA <- constructA(xis, method = method, cov.syntax = cov.syntax, - parTable = parTable) - A <- listA$numeric - labelA <- listA$label - - # mean etas - listAlpha <- constructAlpha(etas, parTable = parTable, - auto.constraints = auto.constraints, - mean.observed = mean.observed) - alpha <- listAlpha$numeric - labelAlpha <- listAlpha$label - - # mean xis - listBeta0 <- constructAlpha(xis, parTable = parTable, - auto.constraints = auto.constraints, - mean.observed = mean.observed) - beta0 <- listBeta0$numeric - labelBeta0 <- listBeta0$label - - # quadratic terms - listOmegaEtaXi <- omegaAndSortedXis$omegaEtaXi - omegaEtaXi <- listOmegaEtaXi$numeric - labelOmegaEtaXi <- listOmegaEtaXi$label - - listOmegaXiXi <- omegaAndSortedXis$omegaXiXi - omegaXiXi <- listOmegaXiXi$numeric - labelOmegaXiXi <- listOmegaXiXi$label - - # matrices for scaling variables in qml - selectScalingY <- selectScalingY(lambdaY, method = method) - selectBetaRows <- selectBetaRows(lambdaY, method = method) - emptyR <- constructR(etas, indsEtas, lambdaY, method = method) - fullR <- constructFullR(etas, indsEtas, lambdaY, method = method) - - latentEtas <- getLatentEtasQml(indsEtas, method = method) - colsU <- getColsU(etas, indsEtas, lambdaY, method = method) - - fullL2 <- constructFullL2(colsU, etas = etas, method = method) - selectSubL2 <- getSelectSubL2(fullL2, colsU = colsU, latentEtas = latentEtas, - method = method) - fullSigma2ThetaEpsilon <- constructFullSigma2ThetaEpsilon(psi, method = method) - selectSubSigma2ThetaEpsilon <- - getSelectSubSigma2ThetaEpsilon(fullSigma2ThetaEpsilon, latentEtas = latentEtas, - method = method) - fullU <- constructFullU(fullL2 = fullL2, N = NROW(data), etas = etas, method = method) - - scalingInds <- getScalingInds(indsEtas, R = emptyR, latentEtas = latentEtas, - method = method) - selectThetaEpsilon <- selectThetaEpsilon(lambdaY, thetaEpsilon, - scalingInds, method = method) - subThetaEpsilon <- constructSubThetaEpsilon(indsEtas, thetaEpsilon, - scalingInds, method = method) - - covModel <- covModel(cov.syntax, method = method, parTable = parTableCovModel) - - # list of matrices - matrices <- list( - lambdaX = lambdaX, - lambdaY = lambdaY, - gammaXi = gammaXi, - gammaEta = gammaEta, - thetaDelta = thetaDelta, - thetaEpsilon = thetaEpsilon, - phi = phi, - A = A, - Ieta = Ieta, - psi = psi, - tauX = tauX, - tauY = tauY, - alpha = alpha, - beta0 = beta0, - omegaEtaXi = omegaEtaXi, - omegaXiXi = omegaXiXi, - - selectScalingY = selectScalingY, - selectThetaEpsilon = selectThetaEpsilon, - selectBetaRows = selectBetaRows, - - emptyR = emptyR, - fullR = fullR, - - fullSigma2ThetaEpsilon = fullSigma2ThetaEpsilon, - selectSubSigma2ThetaEpsilon = selectSubSigma2ThetaEpsilon, - - fullL2 = fullL2, - selectSubL2 = selectSubL2, - - fullU = fullU, - colsU = colsU, - colsR = colnames(emptyR), - rowsR = rownames(emptyR), - - subThetaEpsilon = subThetaEpsilon) - - labelMatrices <- list( - lambdaX = labelLambdaX, - lambdaY = labelLambdaY, - gammaXi = labelGammaXi, - gammaEta = labelGammaEta, - thetaDelta = thetaLabelDelta, - thetaEpsilon = thetaLabelEpsilon, - - phi = labelPhi, - A = labelA, - psi = labelPsi, - tauX = labelTauX, - tauY = labelTauY, - alpha = labelAlpha, - beta0 = labelBeta0, - - omegaEtaXi = labelOmegaEtaXi, - omegaXiXi = labelOmegaXiXi) - - k <- omegaAndSortedXis$k - quad <- quadrature(m, k, cut = quad.range) - - model <- list(info = - list(xis = xis, - etas = etas, - numXis = numXis, - numEtas = numEtas, - indsXis = indsXis, - indsEtas = indsEtas, - allIndsXis = allIndsXis, - allIndsEtas = allIndsEtas, - varsInts = varsInts, - latentEtas = latentEtas, - scalingInds = scalingInds, - kOmegaEta = getK_NA(omegaEtaXi), - - lavOptimizerSyntaxAdditions = lavOptimizerSyntaxAdditions), - - quad = quad, - matrices = matrices, - labelMatrices = labelMatrices, - syntax = syntax, - cov.syntax = cov.syntax, - parTable = parTable, - covModel = covModel) - - - model$constrExprs <- getConstrExprs(parTable, model$covModel$parTable) - - if (createTheta) { - listTheta <- createTheta(model) - model <- c(model, listTheta) - model$freeParams <- length(listTheta$theta) - model$info$bounds <- getParamBounds(model) - } - - model$data <- cleanAndSortData(data, allIndsXis, allIndsEtas) - model$info$N <- NROW(model$data) - - if (checkModel) checkModel(model = model, covModel = covModel, method = method) - - model -} - - -matrixToParTable <- function(matrixNA, matrixEst, matrixSE, matrixLabel, - op = "=~", rowsLhs = TRUE) { - if (!rowsLhs) { - matrixNA <- t(matrixNA) - matrixEst <- t(matrixEst) - matrixSE <- t(matrixSE) - matrixLabel <- t(matrixLabel) - } - - parTable <- NULL - for (lhs in rownames(matrixEst)) { - for (rhs in colnames(matrixEst)) { - if (!is.na(matrixNA[lhs, rhs]) && matrixLabel[lhs, rhs] == "") next - newRow <- data.frame(lhs = lhs, op = op, rhs = rhs, - label = matrixLabel[lhs, rhs], - est = matrixEst[lhs, rhs], - std.error = matrixSE[lhs, rhs]) - parTable <- rbind(parTable, newRow) - } - } - parTable -} - - -omegaToParTable <- function(omegaNA, omegaEst, omegaSE, omegaLabel) { - rows <- rownames(omegaEst) - cols <- colnames(omegaEst) - - parTable <- NULL - for (row in rows) for (col in cols) { - if (!is.na(omegaNA[row, col]) && omegaLabel[row, col] == "") next - eta <- getEtaRowLabelOmega(row) - x <- getXiRowLabelOmega(row) - intTerm <- paste0(x, ":", col) - - newRow <- data.frame(lhs = eta, op = "~", rhs = intTerm, - label = omegaLabel[row, col], est = omegaEst[row, col], - std.error = omegaSE[row, col]) - parTable <- rbind(parTable, newRow) - } - parTable -} - - -mainModelToParTable <- function(finalModel, method = "lms") { - matricesEst <- finalModel$matrices - matricesSE <- finalModel$matricesSE - matricesNA <- finalModel$matricesNA - matricesLabel <- finalModel$labelMatrices - - if (is.null(matricesSE)) matricesSE <- matricesNA - - etas <- finalModel$info$etas - numXis <- finalModel$info$numXis - parTable <- NULL - - # Coefficients Measurement Model - newRows <- matrixToParTable(matricesNA$lambdaX, - matricesEst$lambdaX, - matricesSE$lambdaX, - matricesLabel$lambdaX, - op = "=~", - rowsLhs = FALSE) - parTable <- rbind(parTable, newRows) - - newRows <- matrixToParTable(matricesNA$lambdaY, - matricesEst$lambdaY, - matricesSE$lambdaY, - matricesLabel$lambdaY, - op = "=~", - rowsLhs = FALSE) - parTable <- rbind(parTable, newRows) - - # coefficients Structural Model - newRows <- matrixToParTable(matricesNA$gammaXi, - matricesEst$gammaXi, - matricesSE$gammaXi, - matricesLabel$gammaXi, - op = "~", - rowsLhs = TRUE) - parTable <- rbind(parTable, newRows) - - newRows <- matrixToParTable(matricesNA$gammaEta, - matricesEst$gammaEta, - matricesSE$gammaEta, - matricesLabel$gammaEta, - op = "~", - rowsLhs = TRUE) - parTable <- rbind(parTable, newRows) - - # interaction effects - newRows <- omegaToParTable(matricesNA$omegaXiXi, - matricesEst$omegaXiXi, - matricesSE$omegaXiXi, - matricesLabel$omegaXiXi) - parTable <- rbind(parTable, newRows) - - newRows <- omegaToParTable(matricesNA$omegaEtaXi, - matricesEst$omegaEtaXi, - matricesSE$omegaEtaXi, - matricesLabel$omegaEtaXi) - parTable <- rbind(parTable, newRows) - - # Intercepts - newRows <- matrixToParTable(matricesNA$tauX, - matricesEst$tauX, - matricesSE$tauX, - matricesLabel$tauX, - op = "~", - rowsLhs = TRUE) - parTable <- rbind(parTable, newRows) - - newRows <- matrixToParTable(matricesNA$tauY, - matricesEst$tauY, - matricesSE$tauY, - matricesLabel$tauY, - op = "~", - rowsLhs = TRUE) - parTable <- rbind(parTable, newRows) - - newRows <- matrixToParTable(matricesNA$alpha, - matricesEst$alpha, - matricesSE$alpha, - matricesLabel$alpha, - op = "~", - rowsLhs = TRUE) - parTable <- rbind(parTable, newRows) - - newRows <- matrixToParTable(matricesNA$beta0, - matricesEst$beta0, - matricesSE$beta0, - matricesLabel$beta0, - op = "~", - rowsLhs = TRUE) - parTable <- rbind(parTable, newRows) - - # Residual (co) variances Measurement Model - newRows <- matrixToParTable(matricesNA$thetaDelta, - matricesEst$thetaDelta, - matricesSE$thetaDelta, - matricesLabel$thetaDelta, - op = "~~", - rowsLhs = TRUE) - parTable <- rbind(parTable, newRows) - - newRows <- matrixToParTable(matricesNA$thetaEpsilon, - matricesEst$thetaEpsilon, - matricesSE$thetaEpsilon, - matricesLabel$thetaEpsilon, - op = "~~", - rowsLhs = TRUE) - parTable <- rbind(parTable, newRows) - - # (Co) variances Structural Model - if (method == "lms") { - phiNA <- matricesNA$A - phiEst <- matricesEst$phi - phiSE <- matricesSE$A - phiLabel <- matricesLabel$A - } else if (method == "qml") { - phiNA <- matricesNA$phi - phiEst <- matricesEst$phi - phiSE <- matricesSE$phi - phiLabel <- matricesLabel$phi - } - - newRows <- matrixToParTable(phiNA, - phiEst, - phiSE, - phiLabel, - op = "~~", - rowsLhs = FALSE) - parTable <- rbind(parTable, newRows) - - newRows <- matrixToParTable(matricesNA$psi, - matricesEst$psi, - matricesSE$psi, - matricesLabel$psi, - op = "~~", - rowsLhs = FALSE) - parTable <- rbind(parTable, newRows) - - parTable <- lapplyDf(parTable, FUN = function(x) replace(x, x == -999, NA)) - parTable -} - - -modelToParTable <- function(model, method = "lms") { - rbind(mainModelToParTable(model, method = method), - covModelToParTable(model, method = method)) -} diff --git a/R/model_parameters_da.R b/R/model_parameters_da.R deleted file mode 100644 index e2e53ba..0000000 --- a/R/model_parameters_da.R +++ /dev/null @@ -1,234 +0,0 @@ -# Global variables -namesParMatrices <- c("lambdaX", "lambdaY", "gammaXi", "gammaEta", - "thetaDelta", "thetaEpsilon", "phi", "A", - "psi", "tauX", "tauY", "alpha", "beta0", "omegaEtaXi", - "omegaXiXi") -namesParMatricesCov <- c("gammaXi", "gammaEta", "A", "psi", "phi") - - -createTheta <- function(model, start = NULL) { - etas <- model$info$etas - - listThetaCov <- createThetaCovModel(model$covModel) - thetaCov <- listThetaCov$theta - lavLabelsCov <- listThetaCov$lavLabels - thetaLabel <- createThetaLabel(model$labelMatrices, - model$covModel$labelMatrices, - model$constrExprs) - totalThetaLabel <- calcThetaLabel(thetaLabel, model$constrExprs) - - M <- model$matrices - lambdaX <- as.vector(M$lambdaX) - lambdaY <- as.vector(M$lambdaY) - thetaDelta <- as.vector(M$thetaDelta) - thetaEpsilon <- as.vector(M$thetaEpsilon) - phi <- as.vector(M$phi) - A <- as.vector(M$A) - psi <- as.vector(M$psi) - tauX <- as.vector(M$tauX) - tauY <- as.vector(M$tauY) - alpha <- as.vector(M$alpha) - beta0 <- as.vector(M$beta0) - gammaXi <- as.vector(M$gammaXi) - gammaEta <- as.vector(M$gammaEta) - omegaXiXi <- as.vector(M$omegaXiXi) - omegaEtaXi <- as.vector(M$omegaEtaXi) - - allModelValues <- c("lambdaX" = lambdaX, - "lambdaY" = lambdaY, - "tauX" = tauX, - "tauY" = tauY, - "thetaDelta" = thetaDelta, - "thetaEpsilon" = thetaEpsilon, - "phi" = phi, - "A" = A, - "psi" = psi, - "alpha" = alpha, - "beta0" = beta0, - "gammaXi" = gammaXi, - "gammaEta" = gammaEta, - "omegaXiXi" = omegaXiXi, - "omegaEtaXi" = omegaEtaXi) - - lavLabelsMain <- createLavLabels(M, subset = is.na(allModelValues), - etas = etas) - - thetaMain <- allModelValues[is.na(allModelValues)] - thetaMain <- fillThetaIfStartNULL(start = start, theta = thetaMain) - theta <- c(thetaLabel, thetaCov, thetaMain) - - lavLabels <- combineLavLabels(lavLabelsMain = lavLabelsMain, - lavLabelsCov = lavLabelsCov, - currentLabels = names(theta)) - - list(theta = theta, lenThetaMain = length(thetaMain), - lenThetaLabel = length(thetaLabel), - totalLenThetaLabel = length(totalThetaLabel), - lenThetaCov = length(thetaCov), lavLabels = lavLabels) -} - - -createThetaCovModel <- function(covModel, start = NULL) { - M <- covModel$matrices - - phi <- as.vector(M$phi) - A <- as.vector(M$A) - psi <- as.vector(M$psi) - alpha <- as.vector(M$alpha) - gammaXi <- as.vector(M$gammaXi) - gammaEta <- as.vector(M$gammaEta) - thetaCov <- c("phi" = phi, - "A" = A, - "psi" = psi, - "gammaXi" = gammaXi, - "gammaEta" = gammaEta) - - lavLabelsCov <- createLavLabelsCov(M, subset = is.na(thetaCov)) - thetaCov <- thetaCov[is.na(thetaCov)] - thetaCov <- fillThetaIfStartNULL(start = start, theta = thetaCov) - - list(theta = thetaCov, lavLabels = lavLabelsCov) -} - - -fillThetaIfStartNULL <- function(start, theta) { - if (!is.null(start)) return(theta) - vapply(theta, FUN = function(x) stats::runif(1), - FUN.VALUE = vector("numeric", 1L)) -} - - -fillModel <- function(model, theta, fillPhi = FALSE, method = "lms") { - if (is.null(names(theta))) names(theta) <- names(model$theta) - - # labeled parameters - thetaLabel <- NULL - if (model$totalLenThetaLabel > 0) { - if (model$lenThetaLabel > 0) { - thetaLabel <- theta[seq_len(model$lenThetaLabel)] - theta <- theta[-seq_len(model$lenThetaLabel)] - } - thetaLabel <- calcThetaLabel(thetaLabel, model$constrExprs) - } - - # cov model - thetaCov <- NULL - thetaMain <- theta - if (model$lenThetaCov > 0) { - thetaCov <- theta[seq_len(model$lenThetaCov)] - thetaMain <- theta[-seq_len(model$lenThetaCov)] - } - - model$covModel <- fillCovModel(model$covModel, thetaCov, thetaLabel, - fillPhi = fillPhi, method = method) - model$matrices <- fillMainModel(model, thetaMain, thetaLabel, - fillPhi = fillPhi, method = method) - model -} - - -fillMainModel <- function(model, theta, thetaLabel, fillPhi = FALSE, - method = "lms") { - xis <- model$info$xis - numXis <- model$info$numXis - numEtas <- model$info$numEtas - M <- model$matrices - covModel <- model$covModel - - lMatrices <- model$labelMatrices[namesParMatrices] - pMatrices <- M[namesParMatrices] - M[namesParMatrices] <- fillMatricesLabels(pMatrices, lMatrices, thetaLabel) - - if (!is.null(model$covModel$matrices)) { - M$phi <- M$A <- expectedCovModel(covModel, method = method, sortedXis = xis) - } else if (method == "lms") { - M$A <- fillNA_Matrix(M$A, theta = theta, pattern = "^A[0-9]*$") - } else if (method == "qml") { - M$phi <- fillSymmetric(M$phi, fetch(theta, "^phi")) - } - - M$lambdaX <- fillNA_Matrix(M$lambdaX, theta = theta, pattern = "^lambdaX") - M$lambdaY <- fillNA_Matrix(M$lambdaY, theta = theta, pattern = "^lambdaY") - M$thetaDelta <- fillSymmetric(M$thetaDelta, fetch(theta, "^thetaDelta")) - M$thetaEpsilon <- fillSymmetric(M$thetaEpsilon, fetch(theta, "thetaEpsilon")) - M$psi <- fillSymmetric(M$psi, fetch(theta, "^psi")) - M$tauX <- fillNA_Matrix(M$tauX, theta = theta, pattern = "^tauX") - M$tauY <- fillNA_Matrix(M$tauY, theta = theta, pattern = "^tauY") - M$alpha <- fillNA_Matrix(M$alpha, theta = theta, pattern = "^alpha") - M$beta0 <- fillNA_Matrix(M$beta0, theta = theta, pattern = "^beta0") - M$gammaEta <- fillNA_Matrix(M$gammaEta, theta = theta, pattern = "^gammaEta") - M$gammaXi <- fillNA_Matrix(M$gammaXi, theta = theta, pattern = "^gammaXi") - M$omegaXiXi <- fillNA_Matrix(M$omegaXiXi, theta = theta, pattern = "^omegaXiXi") - M$omegaEtaXi <- fillNA_Matrix(M$omegaEtaXi, theta = theta, pattern = "^omegaEtaXi") - - if (fillPhi) M$phi <- M$A %*% t(M$A) - M -} - - -fillCovModel <- function(covModel, theta, thetaLabel, fillPhi = FALSE, - method = "lms") { - if (is.null(names(theta))) names(theta) <- names(covModel$theta) - if (is.null(covModel$matrices)) return(NULL) - M <- covModel$matrices - - lMatrices <- covModel$labelMatrices[namesParMatricesCov] - pMatrices <- M[namesParMatricesCov] - M[namesParMatricesCov] <- fillMatricesLabels(pMatrices, lMatrices, thetaLabel) - - M$psi <- fillSymmetric(M$psi, fetch(theta, "^psi")) - M$gammaEta <- fillNA_Matrix(M$gammaEta, theta = theta, pattern = "^gammaEta") - M$gammaXi <- fillNA_Matrix(M$gammaXi, theta = theta, pattern = "^gammaXi") - - if (method == "lms") { - M$A <- fillSymmetric(M$A, fetch(theta, "^A[0-9]+")) - } else if (method == "qml") { - M$phi <- fillSymmetric(M$phi, fetch(theta, "^phi")) - } - - if (fillPhi) M$phi <- M$A %*% t(M$A) - - covModel$matrices <- M - covModel -} - - -fillNA_Matrix <- function(X, theta, pattern) { - X[is.na(X)] <- fetch(theta, pattern) - X -} - - -fillSymmetric <- function(mat, values) { - mat[is.na(mat)] <- values - mat[upper.tri(mat)] <- t(mat)[upper.tri(mat)] - mat -} - - -# Set bounds for parameters to (0, Inf) -getParamBounds <- function(model, lowest = 1e-6) { - namePattern <- paste0("lambdaX[0-9]*$|lambdaY[0-9]*$|", - "thetaDelta[0-9]*$|thetaEpsilon[0-9]*$|", - "phi[0-9]*$|psi[0-9]*$") - lower <- rep(-Inf, model$freeParams) - upper <- rep(Inf, model$freeParams) - names(lower) <- names(upper) <- names(model$theta) - lower[grepl(namePattern, names(lower))] <- lowest - list(lower = lower, upper = upper) -} - - -checkStartingParams <- function(start, model) { - if (length(start) != length(model$theta)) { - stop2("The length of the starting parameters does not match the number of parameters in the model") - } - if (is.null(names(start))) { - names(start) <- names(model$theta) - } - if (!all(names(start) %in% names(model$theta))) { - stop2("The names of the starting parameters do not match the names of the parameters in the model") - } - - NULL -} diff --git a/R/model_pi.R b/R/model_pi.R deleted file mode 100644 index 41ee04f..0000000 --- a/R/model_pi.R +++ /dev/null @@ -1,198 +0,0 @@ -parseLavaan <- function(model.syntax = NULL, variableNames = NULL, match = FALSE) { - # Check if a model.syntax is provided, if not we should return an error - if (is.null(model.syntax)) - stop2("No model.syntax provided") - else if (!is.character(model.syntax)) - stop2("The provided model syntax is not a string!") - else if (length(model.syntax) > 1) - stop2("The provided model syntax is not of length 1") - - parTable <- modsemify(model.syntax) - structuralExprs <- parTable[parTable$op == "~",] - measureExprs <- parTable[parTable$op %in% c("=~", "<~"), ] - lVs <- unique(parTable$lhs[parTable$op == "=~"]) - vars <- unique(c(parTable$rhs[parTable$op %in% c("~", "=~") & - parTable$rhs != "1"], - parTable$lhs[parTable$op == "~"])) |> - stringr::str_split(pattern = ":", simplify = FALSE) |> - unlist() |> unique() - oVs <- vars[!vars %in% lVs] - prodNames <- parTable$rhs[grepl(":", parTable$rhs) & parTable$op == "~"] |> - unique() - prodNamesCleaned <- stringr::str_remove_all(prodNames, ":") - - # Get all the indicators in the model - inds <- unique(measureExprs$rhs[!grepl(":", measureExprs$rhs)]) - stopif(!all(inds %in% variableNames), - "Unable to find observed variables in data: ", - capturePrint(inds[!inds %in% variableNames])) - - # Are prods latent? - elementsInProds <- lapplyNamed(prodNames, - FUN = splitProdName, - pattern = ":", - names = prodNamesCleaned) - - # Inds belonging to latent variables which are specified in the syntax - indsLatents <- structureLavExprs(measureExprs) - - if (length(prodNamesCleaned) > 0) { - # Get inds belonging to latent variables, or if observed, just get the - # observed variable in prod terms - indsInLatentProds <- - lapplyNamed(elementsInProds[prodNamesCleaned], - FUN = getIndsMultipleVariables, - indsLatents = indsLatents, - names = prodNamesCleaned) - - # Creating a relDF for prodTerms - relDfs <- lapply(indsInLatentProds, - FUN = createRelDf, - match = match) - - # Get a list with all the inds in each interactionterm - indsInLatentProds <- lapplyNamed(indsInLatentProds, - FUN = function(x) unname(unlist(x)), - names = prodNamesCleaned) - # create the names for the indProds - indProdNames <- lapplyNamed(relDfs, - FUN = colnames, - names = names(relDfs)) - } else { # in the case where ther is no interaction effects - indsInLatentProds <- NULL - relDfs <- NULL - indProdNames <- NULL - } - - # Return modelSpec - modelSpec <- list(model.syntax = model.syntax, - parTable = parTable, - - oVs = oVs, - lVs = lVs, - prodNames = prodNamesCleaned, - elementsInProdNames = elementsInProds, - relDfs = relDfs, - - indsInLatentProds = indsInLatentProds, - latentProds = names(indsInLatentProds), - indProdNames = indProdNames) - modelSpec -} - - -# Function for structuring exprs in a parTable -structureLavExprs <- function(parTable = NULL) { - # If empty return NULL - if (is.null(parTable)) return(NULL) - # Same if nrow <= 0 - else if (nrow(parTable) <= 0) return(NULL) - - # Get Dependents - names <- unique(parTable$lhs) - - # see utils for definition of selectValuesByCol() and lapplyNamed() - lapplyNamed(names, selectValuesByCol, parTable, "rhs" ,"lhs") -} - - -getIndsVariable <- function(varName = NULL, indsLatents = NULL) { - stopif(is.null(varName), "Error in getIndsVariable(), varName is NULL") - # Get the names of the latent variables in the model - lVs <- names(indsLatents) - - # Check if our varName is a latent- or an observed variable - if (!(varName %in% lVs)) { - # If it is not a latent variable, we should just return the observed variable - return(varName) - } else if (varName %in% lVs) { - # If it is a latent variable, we should return its inds - return(indsLatents[[varName]]) - } - - # Error if it is neither a latent- nor an observerd variable - stop2("Something went wrong in getIndsVariable(), ", - "varName neither observed not latent") -} - - -# Function for getting inds for mutliple variables -getIndsMultipleVariables <- function(varNames = NULL, indsLatents = NULL) { - # varNames = vector with variableNames - # output should be a list - # Check that arguements are supplied/not NULL - stopif(is.null(varNames), "Error in getIndsMultipleVariables(), varNames is NULL") - # use getIndsVariable for each element in varNames, and return a named list - lapplyNamed(varNames, - FUN = getIndsVariable, - indsLatents = indsLatents) -} - - -splitProdName <- function(prodName, pattern) { - stopif(is.null(prodName) ,"prodNames in splitProdName was NULL") - stopif(is.null(pattern) ,"pattern in splitProdName was NULL") - stringr::str_split_1(prodName, pattern) -} - - -splitProdNamesVec <- function(prodNames, pattern) { - stopif(is.null(prodNames) ,"prodNames in splitProdNamesVec was NULL") - stopif(is.null(pattern) ,"pattern in splitProdNamesVec was NULL") - unlist(stringr::str_split(prodNames, pattern)) -} - - -fixProdNames <- function(prodName, pattern = NULL) { - stopif(is.null(pattern) ,"pattern in fixProdNames was NULL") - stringr::str_remove_all(prodName, pattern) -} - - -fixLatentNamesSyntax <- function(model.syntax, pattern) { - stringr::str_replace_all(model.syntax, pattern, "") -} - - -createRelDf <- function(indsProdTerm, match = FALSE) { - if (match) { - lengths <- vapply(indsProdTerm, FUN.VALUE = integer(1L), - FUN = length) - if ((shortest <- min(lengths)) != (longest <- max(lengths))) { - warning2("Unequal number of indicators for latent variables ", - "in product term, not all indicators will be used") - indsProdTerm <- lapply(indsProdTerm, - FUN = function(x, shortest) x[seq_len(shortest)], - shortest = shortest) - } - relDf <- t(as.data.frame(indsProdTerm)) - } else if (!match) { - allCombos <- t(expand.grid(indsProdTerm)) - relDf <- NULL - for (i in seq_len(ncol(allCombos))) { - if (!greplRowDf(allCombos[, i], relDf)) { - relDf <- cbind(relDf, allCombos[, i]) - } - } - } - - names <- apply(relDf, MARGIN = 2, FUN = stringr::str_c, collapse = "") - structure(as.data.frame(relDf), - names = names, - row.names = names(indsProdTerm)) -} - - -# This should return just an observed variable, if it does not belong in a latent one -getIndsLatents <- function(names, latents) { - lapplyNamed(names, - FUN = function(name, latents) { - if (name %in% names(latents)) { - latents[[name]] - } else { - name - } - }, - latents = latents, - names = names) -} diff --git a/R/modsem-package.R b/R/modsem-package.R deleted file mode 100644 index fdc6248..0000000 --- a/R/modsem-package.R +++ /dev/null @@ -1,9 +0,0 @@ -#' @keywords internal -"_PACKAGE" - -## usethis namespace: start -#' @name modsem -#' @importFrom Rcpp sourceCpp -#' @useDynLib modsem, .registration = TRUE -## usethis namespace: end -NULL diff --git a/R/modsem.R b/R/modsem.R deleted file mode 100644 index e7a70b8..0000000 --- a/R/modsem.R +++ /dev/null @@ -1,125 +0,0 @@ -#' Interaction between latent variables -#' -#' @param model.syntax lavaan syntax -#' -#' @param data dataframe -#' -#' @param method method to use: -#' "rca" = residual centering approach (passed to lavaan), -#' "uca" = unconstrained approach (passed to lavaan), -#' "dblcent" = double centering approach (passed to lavaan), -#' "pind" = prod ind approach, with no constraints or centering (passed to lavaan), -#' "lms" = laten model structural equations (not passed to lavaan). -#' "qml" = quasi maximum likelihood estimation of laten model structural equations (not passed to lavaan). -#' "custom" = use parameters specified in the function call (passed to lavaan) -#' -#' @param ... arguments passed to other functions depending on method (see modsem_pi, modsem_da, and modsem_mplus) -#' @return modsem object -#' @export -#' @description -#' modsem is a function for estimating interaction effects between latent variables, -#' in structural equation models (SEM's). -#' Methods for estimating interaction effects in SEM's can basically be split into -#' two frameworks: 1. Product Indicator based approaches ("dblcent", "rca", "uca", -#' "ca", "pind"), and 2. Distributionally based approaches ("lms", "qml"). -#' For the product indicator based approaces, modsem() is essentially a just -#' a fancy wrapper for lavaan::sem() which generates the -#' necessary syntax, and variables for the estimation of models with latent product indicators. -#' The distributionally based approaches are implemented in seperately, and are -#' are not estimated using lavaan::sem(), but rather using custom functions (largely) -#' written in C++ for performance reasons. For greater control, it is advised that -#' you use one of the sub-functions (modsem_pi, modsem_da, modsem_mplus) directly, -#' as passing additional arguments to them via modsem() can lead to unexpected behavior. -#' @examples -#' library(modsem) -#' # For more examples check README and/or GitHub. -#' # One interaction -#' m1 <- ' -#' # Outer Model -#' X =~ x1 + x2 +x3 -#' Y =~ y1 + y2 + y3 -#' Z =~ z1 + z2 + z3 -#' -#' # Inner model -#' Y ~ X + Z + X:Z -#' ' -#' -#' # Double centering approach -#' est1 <- modsem(m1, oneInt) -#' summary(est1) -#' -#' \dontrun{ -#' # The Constrained Approach -#' est1_ca <- modsem(m1, oneInt, method = "ca") -#' summary(est1_ca) -#' -#' # LMS approach -#' est1_lms <- modsem(m1, oneInt, method = "lms") -#' summary(est1_lms) -#' -#' # QML approach -#' est1_qml <- modsem(m1, oneInt, method = "qml") -#' summary(est1_qml) -#' -#' } -#' -#' # Theory Of Planned Behavior -#' tpb <- ' -#' # Outer Model (Based on Hagger et al., 2007) -#' ATT =~ att1 + att2 + att3 + att4 + att5 -#' SN =~ sn1 + sn2 -#' PBC =~ pbc1 + pbc2 + pbc3 -#' INT =~ int1 + int2 + int3 -#' BEH =~ b1 + b2 -#' -#' # Inner Model (Based on Steinmetz et al., 2011) -#' INT ~ ATT + SN + PBC -#' BEH ~ INT + PBC -#' BEH ~ INT:PBC -#' ' -#' -#' # double centering approach -#' est_tpb <- modsem(tpb, data = TPB) -#' summary(est_tpb) -#' -#' \dontrun{ -#' # The Constrained Approach -#' est_tpb_ca <- modsem(tpb, data = TPB, method = "ca") -#' summary(est_tpb_ca) -#' -#' # LMS approach -#' est_tpb_lms <- modsem(tpb, data = TPB, method = "lms") -#' summary(est_tpb_lms) -#' -#' # QML approach -#' est_tpb_qml <- modsem(tpb, data = TPB, method = "qml") -#' summary(est_tpb_qml) -#' } -modsem <- function(model.syntax = NULL, - data = NULL, - method = "dblcent", - ...) { - if (is.null(model.syntax)) { - stop2("No model.syntax provided") - } else if (!is.character(model.syntax)) { - stop2("The provided model syntax is not a string!") - } else if (length(model.syntax) > 1) { - stop2("The provided model syntax is not of length 1") - } - - if (is.null(data)) { - stop2("No data provided") - } else if (!is.data.frame(data)) { - data <- as.data.frame(data) - } - - if (method %in% c("rca", "uca", "dblcent", "pind", "ca", "custom")) { - modsem_pi(model.syntax, data = data, method = method, ...) - } else if (method %in% c("lms", "qml")) { - modsem_da(model.syntax, data = data, method = method, ...) - } else if (method == "mplus") { - modsem_mplus(model.syntax, data = data, ...) - } else { - stop2("Method not recognized") - } -} diff --git a/R/modsem_da.R b/R/modsem_da.R deleted file mode 100644 index 255c989..0000000 --- a/R/modsem_da.R +++ /dev/null @@ -1,303 +0,0 @@ -# Estimate SEM using distribution analytic (DA) approaches -# Last updated: 29.05.2024 - -#' Interaction between latent variables using lms and qml approaches -#' -#' @param model.syntax lavaan syntax -#' -#' @param data dataframe -#' -#' @param method method to use: -#' "lms" = laten model structural equations (not passed to lavaan). -#' "qml" = quasi maximum likelihood estimation of laten model structural equations (not passed to lavaan). -#' -#' @param verbose should estimation progress be shown -#' -#' @param optimize should starting parameters be optimized -#' -#' @param nodes number of quadrature nodes (points of integration) used in lms, -#' increased number gives better estimates but slower computation. How many is needed, depends on the complexity of the model -#' For simple models you somwhere between 16-24 should be enough, for more complex higher numbers may be needed. -#' For models where there is an interaction effects between and endogenous and exogenous variable -#' the number of nodes should at least be 32, but practically (e.g., ordinal/skewed data) more than 32 is recommended. In cases, -#' where data is non-normal it might be better to use the qml approach instead. For large -#' numbers of nodes, you might want to change the 'quad.range' argument. -#' -#' @param convergence convergence criterion. Lower values give better estimates but slower computation. -#' -#' @param optimizer optimizer to use, can be either "nlminb" or "L-BFGS-B". For LMS, "nlminb" is recommended. -#' For QML, "L-BFGS-B" may be faster if there is a large number of iterations, but slower if there are few iterations. -#' -#' @param center.data should data be centered before fitting model -#' -#' @param standardize.data should data be scaled before fitting model, will be overridden by -#' standardize if standardize is set to TRUE. -#' NOTE: It is recommended that you estimate the model normally and then standardize the output using -#' `standardized_estimates()`. -#' -#' @param standardize.out should output be standardized (note will alter the relationsships of -#' parameter constraints, since to parameters are scaled unevenly, even if they -#' have the same label). This does not alter the estimation of the model, only the -#' output. -#' NOTE: It is recommended that you estimate the model normally and then standardize the output using -#' `standardized_estimates()`. -#' -#' @param mean.observed should mean structure of the observed variables be estimated, -#' will be overridden by standardize if standardize is set to TRUE. -#' NOTE: Not recommended unless you know what you are doing. -#' -#' @param standardize will standardize the data before fitting the model, remove the mean -#' structure of the observed variables, and standardize the output. Note that standardize.data -#' mean.observed, standardize.out will be overridden by standardize if standardize is set to TRUE. -#' NOTE: It is recommended that you estimate the model normally and then standardize the output using -#' `standardized_estimates()`. -#' -#' @param double try to double the number of dimensions of integrations used in LMS, -#' this will be extremely slow, but should be more similar to mplus. -#' -#' @param cov.syntax model syntax for implied covariance matrix (see 'vignette("interaction_two_etas", "modsem")') -#' -#' @param calc.se should standard errros be computed, NOTE: If 'FALSE' information matrix will not be computed either -#' -#' @param FIM should fisher information matrix be calculated using observed of expected. must be either "observed" or "expected" -#' -#' @param EFIM.S if expected fisher information matrix is computed, EFIM.S selects the sample size of the generated data -#' -#' @param OFIM.hessian should observed fisher information be computed using hessian? if FALSE, it is computed using gradient -#' -#' @param EFIM.parametric should data for calculating expected fisher information matrix be -#' simulated parametrically (simulated based on the assumptions- and implied parameters -#' from the model), or non-parametrically (stochastically sampled). If you believe that -#' normality assumptions are violated, 'EFIM.parametric = FALSE' might be the better option. -#' -#' @param robust.se should robust standard errors be computed? Meant to be used for QML, -#' can be unreliable with the LMS-approach. -#' @param max.iter max numebr of iterations -#' @param max.step max steps for the M-step in the EM algorithm (LMS) -#' @param fix.estep if TRUE, E-step will be fixed and the prior probabilities are set to the best prior probabilities, -#' if loglikelihood is decreasing for more than 30 iterations. -#' @param start starting parameters -#' @param epsilon finite difference for numerical derivatives -#' @param quad.range range in z-scores to perform numerical integration in LMS using -#' Gaussian-Hermite Quadratures. By default Inf, such that f(t) is integrated from -Inf to Inf, -#' but this will likely be inefficient and pointless at large number of nodes. Nodes outside -#' +/- quad.range will be ignored. -#' -#' @param n.threads number of cores to use for parallel processing, if NULL, it will use <= 2 threads, -#' if an integer is specified, it will use that number of threads (e.g., `n.threads = 4`, will use 4 threads) -#' if = "default" it will use the default number of threads (2). -#' if = "max" it will use all available threads, "min" will use 1 thread. -#' @param ... additional arguments to be passed to the estimation function -#' -#' @return modsem_da object -#' @export -#' -#' @description -#' modsem_da is a function for estimating interaction effects between latent variables, -#' in structural equation models (SEMs), using distributional analytic (DA) approaches. -#' Methods for estimating interaction effects in SEM's can basically be split into -#' two frameworks: 1. Product Indicator based approaches ("dblcent", "rca", "uca", -#' "ca", "pind"), and 2. Distributionally based approaches ("lms", "qml"). -#' modsem_da() handles the latter, and can estimate models using both qml and lms -#' necessary syntax, and variables for the estimation of models with latent product indicators. -#' NOTE: run 'default_settings_da()' to see default arguments. -#' -#' @examples -#' library(modsem) -#' # For more examples check README and/or GitHub. -#' # One interaction -#' m1 <- " -#' # Outer Model -#' X =~ x1 + x2 +x3 -#' Y =~ y1 + y2 + y3 -#' Z =~ z1 + z2 + z3 -#' -#' # Inner model -#' Y ~ X + Z + X:Z -#' " -#' -#' \dontrun{ -#' # QML Approach -#' est1 <- modsem_da(m1, oneInt, method = "qml") -#' summary(est1) -#' -#' -#' # Theory Of Planned Behavior -#' tpb <- " -#' # Outer Model (Based on Hagger et al., 2007) -#' ATT =~ att1 + att2 + att3 + att4 + att5 -#' SN =~ sn1 + sn2 -#' PBC =~ pbc1 + pbc2 + pbc3 -#' INT =~ int1 + int2 + int3 -#' BEH =~ b1 + b2 -#' -#' # Inner Model (Based on Steinmetz et al., 2011) -#' # Covariances -#' ATT ~~ SN + PBC -#' PBC ~~ SN -#' # Causal Relationsships -#' INT ~ ATT + SN + PBC -#' BEH ~ INT + PBC -#' BEH ~ INT:PBC -#' " -#' -#' # lms approach -#' estTpb <- modsem_da(tpb, data = TPB, method = lms) -#' summary(estTpb) -#' } -#' -modsem_da <- function(model.syntax = NULL, - data = NULL, - method = "lms", - verbose = NULL, - optimize = NULL, - nodes = NULL, - convergence = NULL, - optimizer = NULL, - center.data = NULL, - standardize.data = NULL, - standardize.out = NULL, - standardize = NULL, - mean.observed = NULL, - cov.syntax = NULL, - double = NULL, - calc.se = NULL, - FIM = NULL, - EFIM.S = NULL, - OFIM.hessian = NULL, - EFIM.parametric = NULL, - robust.se = NULL, - max.iter = NULL, - max.step = NULL, - fix.estep = NULL, - start = NULL, - epsilon = NULL, - quad.range = NULL, - n.threads = NULL, - ...) { - if (is.null(model.syntax)) { - stop2("No model.syntax provided") - } else if (!is.character(model.syntax)) { - stop2("The provided model syntax is not a string!") - } else if (length(model.syntax) > 1) { - stop2("The provided model syntax is not of length 1") - } - - if (is.null(data)) { - stop2("No data provided") - } else if (!is.data.frame(data)) { - data <- as.data.frame(data) - } - - args <- - getMethodSettingsDA(method, - args = - list( - verbose = verbose, - optimize = optimize, - nodes = nodes, - convergence = convergence, - optimizer = optimizer, - center.data = center.data, - standardize.data = standardize.data, - standardize.out = standardize.out, - standardize = standardize, - mean.observed = mean.observed, - double = double, - calc.se = calc.se, - FIM = FIM, - EFIM.S = EFIM.S, - OFIM.hessian = OFIM.hessian, - EFIM.parametric = EFIM.parametric, - robust.se = robust.se, - max.iter = max.iter, - max.step = max.step, - fix.estep = fix.estep, - epsilon = epsilon, - quad.range = quad.range, - n.threads = n.threads - ) - ) - - if (!method %in% c("lms", "qml")) { - stop2("Method must be either 'lms' or 'qml'") - } - - if (args$center.data) { - data <- lapplyDf(data, FUN = function(x) x - mean(x)) - } - if (args$standardize.data) { - data <- lapplyDf(data, FUN = scaleIfNumeric, scaleFactor = FALSE) - } - - model <- specifyModelDA(model.syntax, - data = data, - method = method, - m = args$nodes, - cov.syntax = cov.syntax, - mean.observed = args$mean.observed, - double = args$double, - quad.range = args$quad.range - ) - - if (args$optimize) { - model <- tryCatch(optimizeStartingParamsDA(model), - warning = function(w) { - warning2("warning when optimizing starting parameters:\n", w) - suppressWarnings(optimizeStartingParamsDA(model)) - }, error = function(e) { - warning2("unable to optimize starting parameters:\n", e) - model - }) - } - - if (!is.null(start)) { - checkStartingParams(start, model = model) # throws error if somethings wrong - model$theta <- start - } - - est <- switch(method, - "qml" = estQml(model, - verbose = args$verbose, - convergence = args$convergence, - calc.se = args$calc.se, - FIM = args$FIM, - EFIM.S = args$EFIM.S, - OFIM.hessian = args$OFIM.hessian, - EFIM.parametric = args$EFIM.parametric, - robust.se = args$robust.se, - max.iter = args$max.iter, - epsilon = args$epsilon, - optimizer = args$optimizer, - ... - ), - "lms" = emLms(model, - verbose = args$verbose, - convergence = args$convergence, - calc.se = args$calc.se, - FIM = args$FIM, - EFIM.S = args$EFIM.S, - OFIM.hessian = args$OFIM.hessian, - EFIM.parametric = args$EFIM.parametric, - robust.se = args$robust.se, - max.iter = args$max.iter, - max.step = args$max.step, - epsilon = args$epsilon, - optimizer = args$optimizer, - fix.estep = args$fix.estep, - ... - ) - ) - - class(est) <- c("modsem_da", "modsem") - if (args$standardize.out) { - est$type.estimates <- "standardized" - est$parTable <- standardized_estimates(est) - } - - # clean up - resetThreads() - - est$args <- args - est -} diff --git a/R/modsem_mplus.R b/R/modsem_mplus.R deleted file mode 100644 index b3a7be5..0000000 --- a/R/modsem_mplus.R +++ /dev/null @@ -1,200 +0,0 @@ -#' Estimation latent interactions through mplus -#' -#' @param model.syntax lavaan/modsem syntax -#' @param data dataset -#' @param estimator estimator argument passed to mplus -#' @param type type argument passed to mplus -#' @param algorithm algorithm argument passed to mplus -#' @param process process argument passed to mplus -#' @param ... arguments passed to other functions -#' -#' @return modsem_mplus object -#' @export -#' -#' @examples -#' # Theory Of Planned Behavior -#' tpb <- ' -#' # Outer Model (Based on Hagger et al., 2007) -#' ATT =~ att1 + att2 + att3 + att4 + att5 -#' SN =~ sn1 + sn2 -#' PBC =~ pbc1 + pbc2 + pbc3 -#' INT =~ int1 + int2 + int3 -#' BEH =~ b1 + b2 -#' -#' # Inner Model (Based on Steinmetz et al., 2011) -#' # Covariances -#' ATT ~~ SN + PBC -#' PBC ~~ SN -#' # Causal Relationsships -#' INT ~ ATT + SN + PBC -#' BEH ~ INT + PBC -#' BEH ~ INT:PBC -#' ' -#' -#' \dontrun{ -#' estTpbMplus <- modsem_mplus(tpb, data = TPB) -#' summary(estTpbLMS) -#' } -#' -modsem_mplus <- function(model.syntax, - data, - estimator = "ml", - type = "random", - algorithm = "integration", - process = "8", - ...) { - parTable <- modsemify(model.syntax) - indicators <- unique(parTable[parTable$op == "=~", "rhs", drop = TRUE]) - intTerms <- unique(getIntTermRows(parTable)$rhs) - intTermsMplus <- stringr::str_remove_all(intTerms, ":") |> - stringr::str_to_upper() - - model <- MplusAutomation::mplusObject( - TITLE = "Running Model via Mplus", - usevariables = indicators, - ANALYSIS = - paste(paste("estimator =", estimator), - paste("type =", type), - paste("algorithm =", algorithm), - paste("process =", process, ";\n"), - sep = ";\n"), - MODEL = parTableToMplusModel(parTable, ...), - rdata = data[indicators], - ) - results <- MplusAutomation::mplusModeler(model, - modelout = "mplusResults.inp", - run = 1L) - coefs <- MplusAutomation::extract.mplus.model(results) - coefsTable <- data.frame(lhsOpRhs = coefs@coef.names, - est = coefs@coef, - std.error = coefs@se, - p.value = coefs@pvalues) - # Measurement Model - indicatorsCaps <- stringr::str_to_upper(indicators) - patternMeas <- - paste0("(", stringr::str_c(indicatorsCaps, collapse = "|"), ")") |> - paste0("<-(?!>|Intercept)") - measCoefNames <- grepl(patternMeas, coefsTable$lhsOpRhs, perl = TRUE) - - # Mplus has lhs/rhs in reversed order for the measurement model, - # compared to lavaan, - measRhs <- stringr::str_split_i(coefsTable$lhsOpRhs[measCoefNames], - "<-", i = 1) - measLhs <- stringr::str_split_i(coefsTable$lhsOpRhs[measCoefNames], - "<-", i = 2) - measModel <- data.frame(lhs = measLhs, op = "=~", rhs = measRhs) |> - cbind(coefsTable[measCoefNames, c("est", "std.error", "p.value")]) - - # Structural Model - measrRemoved <- coefsTable[!measCoefNames, ] - patternStruct <- "<-(?!>|Intercept)" - structCoefNames <- grepl(patternStruct, measrRemoved$lhsOpRhs, perl = TRUE) - - structLhs <- stringr::str_split_i(measrRemoved$lhsOpRhs[structCoefNames], - "<-", i = 1) - structRhs <- stringr::str_split_i(measrRemoved$lhsOpRhs[structCoefNames], - "<-", i = 2) - structModel <- data.frame(lhs = structLhs, op = "~", rhs = structRhs) |> - cbind(measrRemoved[structCoefNames, c("est", "std.error", "p.value")]) - - for (i in seq_along(intTerms)) { - xzMplus <- intTermsMplus[[i]] - xzModsem <- intTerms[[i]] - structModel[structModel$rhs == xzMplus, "rhs"] <- xzModsem - structModel[structModel$lhs == xzMplus, "lhs"] <- xzModsem - } - - # Variances and Covariances - structMeasrRemoved <- measrRemoved[!structCoefNames, ] - patternCovVar <- "<->" - covVarCoefNames <- grepl(patternCovVar, structMeasrRemoved$lhsOpRhs, perl = TRUE) - covVarLhs <- stringr::str_split_i(structMeasrRemoved$lhsOpRhs[covVarCoefNames], - "<->", i = 1) - covVarRhs <- stringr::str_split_i(structMeasrRemoved$lhsOpRhs[covVarCoefNames], - "<->", i = 2) - covVarModel <- data.frame(lhs = covVarLhs, op = "~~", rhs = covVarRhs) |> - cbind(structMeasrRemoved[covVarCoefNames, c("est", "std.error", "p.value")]) - - # Intercepts - covStructMeasrRemoved <- structMeasrRemoved[!covVarCoefNames, ] - patternIntercept <- "<-Intercept" - interceptNames <- grepl(patternIntercept, covStructMeasrRemoved$lhsOpRhs, perl = TRUE) - interceptLhs <- stringr::str_split_i(covStructMeasrRemoved$lhsOpRhs[interceptNames], - "<-", i = 1) - interceptModel <- data.frame(lhs = interceptLhs, op = "~", rhs = 1) |> - cbind(covStructMeasrRemoved[interceptNames, c("est", "std.error", "p.value")]) - - mplusParTable <- rbind(measModel, structModel, covVarModel, interceptModel) - mplusParTable [c("lhs", "rhs")] <- - lapplyDf(mplusParTable[c("lhs", "rhs")], function(x) - stringr::str_remove_all(x, " ")) - - mplusParTable$ci.lower <- mplusParTable$est - 1.96 * mplusParTable$std.error - mplusParTable$ci.upper <- mplusParTable$est + 1.96 * mplusParTable$std.error - mplusParTable$p.value[mplusParTable$p.value == 999] <- NA - mplusParTable$z.value <- mplusParTable$est / mplusParTable$std.error - mplusParTable$z.value[is.infinite(mplusParTable$z.value)] <- NA - mplusParTable$label <- "" - - modelSpec <- list(parTable = mplusParTable, - model = results, - coefs = coefs, - data = data) - structure(modelSpec, - class = "modsem_mplus", - method = "Mplus") -} - - -parTableToMplusModel <- function(parTable, ignoreLabels = TRUE) { - # INTERACTIONEXPRESSIOns - interactions <- parTable[grepl(":", parTable$rhs), "rhs"] - elemsInInts <- stringr::str_split(interactions, ":") - newRows <- lapply(elemsInInts, - function(x) { - if (length(x) != 2) { - stop2("Number of variables in interaction must be two") - } - lhs <- paste0(x[[1]], x[[2]]) - rhs <- paste(x[[1]], "XWITH", x[[2]]) - createParTableRow(c(lhs, rhs), op = ":") - }) |> - purrr::list_rbind() - parTable <- rbind(parTable, newRows) - - parTable$op <- replaceLavOpWithMplus(parTable$op) - out <- "" - if (ignoreLabels) parTable[["mod"]] <- "" - for (i in 1:nrow(parTable)) { - if (parTable[["mod"]][i] != "") { - warning2("Using labels in Mplus, was this intended?") - modifier <- paste0("* (", parTable[["mod"]][[i]],")") - - } else { - modifier <- "" - } - line <- paste0(parTable[["lhs"]][[i]], " ", - parTable[["op"]][[i]], " ", - parTable[["rhs"]][[i]], - modifier, ";" ,"\n") - out <- paste0(out, line) - } - stringr::str_remove_all(out, ":") -} - - -replaceLavOpWithMplus <- function(op) { - vapply(op, - FUN = switchLavOpToMplus, - FUN.VALUE = character(1L)) -} - - -switchLavOpToMplus <- function(op) { - switch(op, - "=~" = "BY", - "~" = "ON", - "~~" = "WITH", - ":" = "|", - stop2("Operator not supported for use in Mplus: ", op, "\n")) -} diff --git a/R/modsem_pi.R b/R/modsem_pi.R deleted file mode 100644 index b5dcaf0..0000000 --- a/R/modsem_pi.R +++ /dev/null @@ -1,530 +0,0 @@ -#' Interaction between latent variables using product indicators -#' -#' @param model.syntax lavaan syntax -#' -#' @param data dataframe -#' -#' @param method method to use: -#' "rca" = residual centering approach (passed to lavaan), -#' "uca" = unconstrained approach (passed to lavaan), -#' "dblcent" = double centering approach (passed to lavaan), -#' "pind" = prod ind approach, with no constraints or centering (passed to lavaan), -#' "custom" = use parameters specified in the function call (passed to lavaan) -#' @param match should the product indicators be created by using the match-strategy -#' -#' @param standardize.data should data be scaled before fitting model -#' -#' @param first.loading.fixed Sould the first factorloading in the latent prod be fixed to one? -#' -#' @param center.before should inds in prods be centered before computing prods (overwritten by method, if method != NULL) -#' -#' @param center.after should ind prods be centered after they have been computed? -#' -#' @param residuals.prods should ind prods be centered using residuals (overwritten by method, if method != NULL) -#' -#' @param residual.cov.syntax should syntax for residual covariances be produced (overwritten by method, if method != NULL) -#' -#' @param constrained.prod.mean should syntax prod mean be produced (overwritten by method, if method != NULL) -#' -#' @param center.data should data be centered before fitting model -#' -#' @param constrained.loadings should syntax for constrained loadings be produced (overwritten by method, if method != NULL) -#' -#' @param constrained.var should syntax for constrained variances be produced (overwritten by method, if method != NULL) -#' -#' @param constrained.res.cov.method method for constraining residual covariances -#' -#' @param auto.scale methods which should be scaled automatically (usually not useful) -#' -#' @param auto.center methods which should be centered automatically (usually not useful) -#' -#' @param estimator estimator to use in lavaan -#' -#' @param group group variable for multigroup analysis -#' -#' @param run should the model be run via lavaan, if FALSE only modified syntax and data is returned -#' -#' @param suppress.warnings.lavaan should warnings from lavaan be supressed? -#' -#' @param ... arguments passed to other functions, e.g,. lavaan -#' -#' @return modsem object -#' @export -#' @description -#' modsem_pi is a function for estimating interaction effects between latent variables, -#' in structural equation models (SEMs), using product indicators. -#' Methods for estimating interaction effects in SEM's can basically be split into -#' two frameworks: 1. Product Indicator based approaches ("dblcent", "rca", "uca", -#' "ca", "pind"), and 2. Distributionally based approaches ("lms", "qml"). -#' modsem_pi() is essentially a just -#' a fancy wrapper for lavaan::sem() which generates the -#' necessary syntax, and variables for the estimation of models with latent product indicators. -#' use `default_settings_pi()` to get the default settings for the different methods. -#' @examples -#' library(modsem) -#' # For more examples check README and/or GitHub. -#' # One interaction -#' m1 <- ' -#' # Outer Model -#' X =~ x1 + x2 +x3 -#' Y =~ y1 + y2 + y3 -#' Z =~ z1 + z2 + z3 -#' -#' # Inner model -#' Y ~ X + Z + X:Z -#' ' -#' -#' # Double centering approach -#' est1 <- modsem_pi(m1, oneInt) -#' summary(est1) -#' -#' \dontrun{ -#' # The Constrained Approach -#' est1Constrained <- modsem_pi(m1, oneInt, method = "ca") -#' summary(est1Constrained) -#' } -#' -#' # Theory Of Planned Behavior -#' tpb <- ' -#' # Outer Model (Based on Hagger et al., 2007) -#' ATT =~ att1 + att2 + att3 + att4 + att5 -#' SN =~ sn1 + sn2 -#' PBC =~ pbc1 + pbc2 + pbc3 -#' INT =~ int1 + int2 + int3 -#' BEH =~ b1 + b2 -#' -#' # Inner Model (Based on Steinmetz et al., 2011) -#' # Covariances -#' ATT ~~ SN + PBC -#' PBC ~~ SN -#' # Causal Relationsships -#' INT ~ ATT + SN + PBC -#' BEH ~ INT + PBC -#' BEH ~ INT:PBC -#' ' -#' -#' # double centering approach -#' estTpb <- modsem_pi(tpb, data = TPB) -#' summary(estTpb) -#' -#' \dontrun{ -#' # The Constrained Approach -#' estTpbConstrained <- modsem_pi(tpb, data = TPB, method = "ca") -#' summary(estTpbConstrained) -#' } -modsem_pi <- function(model.syntax = NULL, - data = NULL, - method = "dblcent", - match = NULL, - standardize.data = FALSE, - center.data = FALSE, - first.loading.fixed = TRUE, - center.before = NULL, - center.after = NULL, - residuals.prods = NULL, - residual.cov.syntax = NULL, - constrained.prod.mean = NULL, - constrained.loadings = NULL, - constrained.var = NULL, - constrained.res.cov.method = NULL, - auto.scale = "none", - auto.center = "none", - estimator = "ML", - group = NULL, - run = TRUE, - suppress.warnings.lavaan = FALSE, - ...) { - stopif(is.null(model.syntax), "No model syntax provided in modsem") - stopif(is.null(data), "No data provided in modsem") - if (!is.data.frame(data)) data <- as.data.frame(data) - - methodSettings <- - getMethodSettingsPI(method, args = - list(center.before = center.before, - center.after = center.after, - residuals.prods = residuals.prods, - residual.cov.syntax = residual.cov.syntax, - constrained.prod.mean = constrained.prod.mean, - constrained.loadings = constrained.loadings, - constrained.var = constrained.var, - constrained.res.cov.method = constrained.res.cov.method, - first.loading.fixed = first.loading.fixed, - match = match)) - - # Get the specifications of the model - modelSpec <- parseLavaan(model.syntax, colnames(data), - match = methodSettings$match) - - # Data Processing ----------------------------------------------------------- - data <- data[c(modelSpec$oVs, group)] - completeCases <- stats::complete.cases(data) - if (any(!completeCases)) { - warning2("Removing missing values case-wise.") - data <- data[completeCases, ] - } - - if (standardize.data || method %in% auto.scale) { - data <- lapplyDf(data, FUN = scaleIfNumeric, scaleFactor = FALSE) - } - - if (center.data || method %in% auto.center) { - data <- lapplyDf(data, FUN = function(x) x - mean(x)) - } - - prodInds <- - createProdInds(modelSpec, - data = data, - center.before = methodSettings$center.before, - center.after = methodSettings$center.after, - residuals.prods = methodSettings$residuals.prods) - mergedProdInds <- combineListDf(prodInds) - - # using list_cbind so that mergedProdInds can be NULL - newData <- purrr::list_cbind(list(data, mergedProdInds)) - - # Genereating a new syntax with constraints and measurmentmodel - parTable <- addSpecsParTable(modelSpec, - residual.cov.syntax = methodSettings$residual.cov.syntax, - constrained.res.cov.method = methodSettings$constrained.res.cov.method, - constrained.prod.mean = methodSettings$constrained.prod.mean, - constrained.loadings = methodSettings$constrained.loadings, - constrained.var = methodSettings$constrained.var, - firstFixed = first.loading.fixed, - ...) - - newSyntax <- parTableToSyntax(parTable, removeColon = TRUE) - - modelSpec$prodInds <- prodInds - modelSpec$syntax <- newSyntax - modelSpec$data <- newData - modelSpec$parTable <- parTable - - if (run) { - lavWrapper <- getWarningWrapper(silent = suppress.warnings.lavaan) - lavEst <- tryCatch(lavaan::sem(newSyntax, newData, estimator = estimator, - group = group, ...) |> lavWrapper(), - error = function(cnd) { - warning2("Error in Lavaan: \n") - warning2(capturePrint(cnd)) - NULL - }) - coefParTable <- tryCatch(lavaan::parameterEstimates(lavEst), - error = function(cnd) NULL) - modelSpec$lavaan <- lavEst - modelSpec$coefParTable <- coefParTable - } - - structure(modelSpec, class = c("modsem_pi", "modsem"), method = method) -} - - -createProdInds <- function(modelSpec, - data, - center.before = FALSE, - center.after = FALSE, - residuals.prods = FALSE) { - indProds <- purrr::map2(.x = modelSpec$relDfs, - .y = modelSpec$indsInLatentProds, - .f = createIndProds, - data = data, - centered = center.before) - if (residuals.prods) { - indProds <- purrr::map2(.x = indProds, .y = modelSpec$indsInLatentProds, - .f = calculateResidualsDf, data = data) - - } else if (!is.logical(residuals.prods)) { - stop2("residualProds was neither FALSE nor TRUE in createProdInds") - } - - if (center.after) { - indProds <- lapply(indProds, FUN = function(df) - lapplyDf(df, FUN = function(x) x - mean(x))) - - } - - indProds -} - - -createIndProds <- function(relDf, indNames, data, centered = FALSE) { - # Getting the indProd names - varnames <- unname(colnames(relDf)) - # Selecting the inds from the dataset - inds <- data[indNames] - # Check if inds are numeric - isNumeric <- sapply(inds, is.numeric) - - stopif(any(!isNumeric), "Expected inds to be numeric when creating prods") - - # Centering them - if (centered) inds <- lapplyDf(inds, FUN = function(x) x - mean(x)) - - prods <- lapplyNamed(varnames, - FUN = function(varname, data, relDf) - multiplyIndicatorsCpp(data[relDf[[varname]]]), - data = inds, relDf = relDf, names = varnames) - - # return as data.frame() - structure(prods, row.names = seq_len(nrow(data)), - class = "data.frame") -} - - -calculateResidualsDf <- function(dependentDf, independentNames, data) { - # Using purrr::list_cbind() is more efficient than cbind() - combinedData <- purrr::list_cbind(list(dependentDf, data)) - - # Getting the names of the dependent variables - dependentNames <- colnames(dependentDf) - # Getting formula - formula <- getResidualsFormula(dependentNames, independentNames) - - if (length(dependentNames <= 1)) { - res <- as.data.frame(stats::residuals(stats::lm(formula = formula, - combinedData))) - colnames(res) <- dependentNames - return(res) - } - - stats::residuals(stats::lm(formula = formula, combinedData)) -} - - -getResidualsFormula <- function(dependendtNames, indepNames) { - formulaDep <- paste0("cbind(", stringr::str_c(dependendtNames, - collapse = ", "), ")") - formulaIndep <- stringr::str_c(indepNames, collapse = " + ") - paste0(formulaDep, " ~ ", formulaIndep) -} - - -addSpecsParTable <- function(modelSpec, - residual.cov.syntax = FALSE, - constrained.res.cov.method = "equality", - constrained.prod.mean = FALSE, - constrained.loadings = FALSE, - constrained.var = FALSE, - firstFixed = TRUE, - ...) { - relDfs <- modelSpec$relDfs - latentProds <- modelSpec$latentProds - indProdNames <- modelSpec$indProdNames - parTable <- modelSpec$parTable - - if (is.null(relDfs) || length(relDfs) < 1) return(parTable) - - measureParTable <- purrr::map2(.x = latentProds, .y = indProdNames, - .f = getParTableMeasure, operator = "=~", - firstFixed = firstFixed) |> - purrr::list_rbind() - parTable <- rbindParTable(parTable, measureParTable) - - if (constrained.var || constrained.loadings || constrained.prod.mean) { - parTable <- addVariances(parTable) |> - addCovariances() |> - labelParameters() |> - labelFactorLoadings() - } - - if (!is.logical(residual.cov.syntax)) { - stop2("residual.cov.syntax is not FALSE or TRUE in generateSyntax") - - } else if (residual.cov.syntax) { - residualCovariances <- purrr::map(.x = relDfs, .f = getParTableResCov, - method = constrained.res.cov.method, - pt = parTable, ...) |> - purrr::list_rbind() - parTable <- rbindParTable(parTable, residualCovariances) - } - - if (constrained.var) parTable <- specifyVarCov(parTable, relDfs) - if (constrained.loadings) parTable <- specifyFactorLoadings(parTable, relDfs) - - if (constrained.prod.mean) { - restrictedMeans <- purrr::map2(modelSpec$prodNames, - modelSpec$elementsInProdNames, - getParTableRestrictedMean, - createLabels = !constrained.var, - pt = parTable) |> - purrr::list_rbind() - parTable <- rbindParTable(parTable, restrictedMeans) - } - - modEnv$parTable <- parTable - parTable -} - - -# this function assumes a prod of only two latent variables no more -getParTableRestrictedMean <- function(prodName, elementsInProdName, - createLabels = TRUE, pt) { - stopif(length(elementsInProdName) > 2, - "The mean of a latent prod should not be constrained when there", - " are more than two variables in the prod term. Please use a", - " different method \n") - - meanLabel <- createLabelMean(prodName) - meanStructure <- createParTableRow(vecLhsRhs = c(prodName, "1"), - op = "~", mod = meanLabel) - covEquation <- trace_path(pt, elementsInProdName[[1]], elementsInProdName[[2]]) - meanFormula <- createParTableRow(vecLhsRhs = c(meanLabel, covEquation), - op = "==") - rbind(meanStructure, meanFormula) -} - - -multiplyInds <- function(df) { - if (is.null(df)) return(NULL) - if (ncol(df) <= 1) return(df[[1]]) - - y <- cbind.data.frame(df[[1]] * df[[2]], - df[,-(1:2),drop = FALSE]) - - multiplyInds(y) -} - - -getParTableMeasure <- function(dependentName, - predictorNames, - operator = "=~", - firstFixed = FALSE) { - stopif(length(dependentName) > 1, "Expected dependentName ", - "to be a single string in getParTableMeasure") - - if (length(predictorNames) == 1 && dependentName == predictorNames) { - # In this case it should be seen as an observed variable, - # and should not have a measurement model - return(NULL) - } - - nRows <- length(predictorNames) - parTable <- data.frame(lhs = rep(dependentName, nRows), - op = rep(operator, nRows), - rhs = predictorNames, - mod = vector("character", nRows)) - - if (firstFixed) parTable[["mod"]][[1]] <- "1" - parTable -} - - -createParTableRow <- function(vecLhsRhs, op, mod = "") { - data.frame(lhs = vecLhsRhs[[1]], op = op, rhs = vecLhsRhs[[2]], mod = mod) -} - - -#' Get lavaan syntax for product indicator approaches -#' -#' @param model.syntax lavaan syntax -#' -#' @param method method to use: -#' "rca" = residual centering approach, -#' "uca" = unconstrained approach, -#' "dblcent" = double centering approach, -#' "pind" = prod ind approach, with no constraints or centering, -#' "custom" = use parameters specified in the function call -#' @param match should the product indicators be created by using the match-strategy -#' -#' @param match should product indicators be made using the match strategy -#' @param ... arguments passed to other functions (e.g., modsem_pi) -#' -#' @return character vector -#' @export -#' @description -#' get_pi_syntax is a function for creating the lavaan syntax used for estimating -#' latent interaction models using one of the product indiactors in lavaan. -#' @examples -#' library(modsem) -#' library(lavaan) -#' m1 <- ' -#' # Outer Model -#' X =~ x1 + x2 +x3 -#' Y =~ y1 + y2 + y3 -#' Z =~ z1 + z2 + z3 -#' -#' # Inner model -#' Y ~ X + Z + X:Z -#' ' -#' syntax <- get_pi_syntax(m1) -#' data <- get_pi_data(m1, oneInt) -#' est <- sem(syntax, data) -get_pi_syntax <- function(model.syntax, - method = "dblcent", - match = FALSE, - ...) { - oVs <- getOVs(model.syntax = model.syntax) - emptyData <- as.data.frame(matrix(0, nrow = 1, ncol = length(oVs), - dimnames = list(NULL, oVs))) - modsem_pi(model.syntax, method = method, match = match, - data = emptyData, run = FALSE, ...)$syntax -} - - -#' Get data with product indicators for different approaches -#' -#' @param model.syntax lavaan syntax -#' @param data data to create product indicators from -#' @param method method to use: -#' "rca" = residual centering approach, -#' "uca" = unconstrained approach, -#' "dblcent" = double centering approach, -#' "pind" = prod ind approach, with no constraints or centering, -#' "custom" = use parameters specified in the function call -#' @param match should the product indicators be created by using the match-strategy -#' -#' @param match should product indicators be made using the match strategy -#' @param ... arguments passed to other functions (e.g., modsem_pi) -#' -#' @return data.frame -#' @export -#' @description -#' get_pi_syntax is a function for creating the lavaan syntax used for estimating -#' latent interaction models using one of the product indiactors in lavaan. -#' @examples -#' library(modsem) -#' library(lavaan) -#' m1 <- ' -#' # Outer Model -#' X =~ x1 + x2 +x3 -#' Y =~ y1 + y2 + y3 -#' Z =~ z1 + z2 + z3 -#' -#' # Inner model -#' Y ~ X + Z + X:Z -#' ' -#' syntax <- get_pi_syntax(m1) -#' data <- get_pi_data(m1, oneInt) -#' est <- sem(syntax, data) -get_pi_data <- function(model.syntax, data, method = "dblcent", - match = FALSE, ...) { - modsem_pi(model.syntax, data = data, method = method, match = match, - run = FALSE, ...)$data -} - - - -#' extract lavaan object from modsem object estimated using product indicators -#' -#' @param object modsem object -#' -#' @return lavaan object -#' @export -#' @examples -#' library(modsem) -#' m1 <- ' -#' # Outer Model -#' X =~ x1 + x2 + x3 -#' Y =~ y1 + y2 + y3 -#' Z =~ z1 + z2 + z3 -#' -#' # Inner model -#' Y ~ X + Z + X:Z -#' ' -#' est <- modsem_pi(m1, oneInt) -#' lav_est <- extract_lavaan(est) -extract_lavaan <- function(object) { - if (!inherits(object, "modsem_pi")) { - stop2("object is not of class modsem_pi") - } - object$lavaan -} diff --git a/R/optimize_da.R b/R/optimize_da.R deleted file mode 100644 index 6e38ff1..0000000 --- a/R/optimize_da.R +++ /dev/null @@ -1,174 +0,0 @@ -optimizeStartingParamsDA <- function(model) { - etas <- model$info$etas - indsEtas <- model$info$allIndsEtas - xis <- model$info$xis - numXis <- model$info$numXis - indsXis <- model$info$allIndsXis - data <- model$data - - syntax <- paste(model$syntax, model$covModel$syntax, - model$info$lavOptimizerSyntaxAdditions, - sep = "\n") - parTable <- modsem_pi(syntax, data, method = "dblcent", - meanstructure = TRUE, - suppress.warnings.lavaan = TRUE)$coefParTable - - # Main Model - matricesMain <- model$matrices - - LambdaX <- findEstimatesParTable(matricesMain$lambdaX, parTable, op = "=~", - rows_lhs = FALSE) - LambdaY <- findEstimatesParTable(matricesMain$lambdaY, parTable, op = "=~", - rows_lhs = FALSE) - - ThetaEpsilon <- findEstimatesParTable(matricesMain$thetaEpsilon, parTable, op = "~~") - ThetaDelta <- findEstimatesParTable(matricesMain$thetaDelta, parTable, op = "~~") - - Psi <- findEstimatesParTable(matricesMain$psi, parTable, op = "~~") - Phi <- findEstimatesParTable(matricesMain$phi, parTable, op = "~~") - - A <- findEstimatesParTable(matricesMain$A, parTable, op = "~~") - A[upper.tri(A)] <- t(A[lower.tri(A)]) - A <- t(tryCatch(chol(A), error = function(x) diag(ncol(A)))) - - beta0 <- findInterceptsParTable(matricesMain$beta0, parTable, fill = 0) - alpha <- findInterceptsParTable(matricesMain$alpha, parTable, fill = 0) - - GammaEta <- findEstimatesParTable(matricesMain$gammaEta, parTable, op = "~") - GammaXi <- findEstimatesParTable(matricesMain$gammaXi, parTable, op = "~") - - OmegaEtaXi <- findInteractionEstimatesParTable(matricesMain$omegaEtaXi, - parTable = parTable) - OmegaXiXi <- findInteractionEstimatesParTable(matricesMain$omegaXiXi, - parTable = parTable) - tauX <- findInterceptsParTable(matricesMain$tauX, parTable, fill = 0) - tauY <- findInterceptsParTable(matricesMain$tauY, parTable, fill = 0) - - thetaMain <- unlist(list(LambdaX[is.na(matricesMain$lambdaX)], - LambdaY[is.na(matricesMain$lambdaY)], - tauX[is.na(matricesMain$tauX)], - tauY[is.na(matricesMain$tauY)], - ThetaDelta[is.na(matricesMain$thetaDelta)], - ThetaEpsilon[is.na(matricesMain$thetaEpsilon)], - Phi[is.na(matricesMain$phi)], - A[is.na(matricesMain$A)], - Psi[is.na(matricesMain$psi)], - alpha[is.na(matricesMain$alpha)], - beta0[is.na(matricesMain$beta0)], - GammaXi[is.na(matricesMain$gammaXi)], - GammaEta[is.na(matricesMain$gammaEta)], - OmegaXiXi[is.na(matricesMain$omegaXiXi)], - OmegaEtaXi[is.na(matricesMain$omegaEtaXi)])) - - # Cov Model - matricesCov <- model$covModel$matrices - if (!is.null(matricesCov)) { - PsiCovModel <- findEstimatesParTable(matricesCov$psi, parTable, op = "~~") - PhiCovModel <- findEstimatesParTable(matricesCov$phi, parTable, op = "~~") - ACovModel <- findEstimatesParTable(matricesCov$A, parTable, op = "~~") - ACovModel[upper.tri(ACovModel)] <- t(ACovModel[lower.tri(ACovModel)]) - ACovModel <- t(tryCatch(chol(ACovModel), error = function(x) - diag(ncol(ACovModel)))) - GammaEtaCovModel <- findEstimatesParTable(matricesCov$gammaEta, parTable, op = "~") - GammaXiCovModel <- findEstimatesParTable(matricesCov$gammaXi, parTable, op = "~") - - thetaCov <- unlist(list(PhiCovModel[is.na(matricesCov$phi)], - ACovModel[is.na(matricesCov$A)], - PsiCovModel[is.na(matricesCov$psi)], - GammaXiCovModel[is.na(matricesCov$gammaXi)], - GammaEtaCovModel[is.na(matricesCov$gammaEta)])) - } else thetaCov <- NULL - - # labelTheta - thetaLabel <- getLabeledParamsLavaan(parTable, model$constrExprs$fixedParams) - - # Combinging the two - theta <- c(thetaLabel, thetaCov, thetaMain) - if (length(theta) == length(model$theta)) { - names(theta) <- names(model$theta) - model$theta <- theta - } - - model -} - - -findEstimatesParTable <- function(mat, parTable, op = NULL, rows_lhs = TRUE, - fill = NULL) { - if (is.null(op)) stop("Missing operator") - for (row in rownames(mat)) { - for (col in colnames(mat)) { - if (is.na(mat[row, col])) - mat[row, col] <- extractFromParTable(row = row, op = op, col = col, - parTable = parTable, - rows_lhs = rows_lhs, fill = fill) - } - } - mat -} - - -findInterceptsParTable <- function(mat, parTable, fill = NULL) { - for (row in rownames(mat)) { - if (is.na(mat[row, ])) - mat[row, ] <- extractFromParTable(row = row, op = "~1", col = "", - parTable = parTable, rows_lhs = TRUE, - fill = fill) - } - mat -} - - -findInteractionEstimatesParTable <- function(omega, parTable) { - rows <- rownames(omega) - cols <- colnames(omega) - - for (row in rows) for (col in cols) { - if (!is.na(omega[row, col])) next - eta <- getEtaRowLabelOmega(row) - x <- getXiRowLabelOmega(row) - xz <- createDoubleIntTerms(x = x, z = col, sep = "") - omega[row, col] <- extractFromParTable(eta, "~", xz, parTable, - rows_lhs = TRUE) - } - omega -} - - -extractFromParTable <- function(row, op, col, parTable, rows_lhs = TRUE, fill = NULL) { - if (rows_lhs) { - out <- parTable[parTable$lhs == row & - parTable$op == op & - parTable$rhs %in% col, "est"] - } else { - out <- parTable[parTable$lhs == col & - parTable$op == op & - parTable$rhs %in% row, "est"] - } - - if (length(out) == 0 && op == "~~") { - out <- parTable[parTable$lhs == col & parTable$op == op & - parTable$rhs %in% row, "est"] - } - - if (length(out) == 0) { - if (is.null(fill)) stop("No match found") - out <- fill - } - - if (length(out) > 1) stop("Incorrect length of matches") - out -} - - -sortParTable <- function(parTable, lhs, op, rhs) { - out <- NULL - for (l in lhs) { - for (r in rhs) { - row <- parTable[parTable$lhs == l & parTable$op == op & parTable$rhs == r, ] - if (NROW(row) == 0) next - out <- rbind(out, row) - } - } - out$est -} diff --git a/R/parser.R b/R/parser.R deleted file mode 100644 index 132535c..0000000 --- a/R/parser.R +++ /dev/null @@ -1,213 +0,0 @@ -evalToken <- function(token, lhs, rhs) { - UseMethod("evalToken") -} - - -#' @export -evalToken.LavOperator <- function(token, lhs, rhs) { - if (is.LavToken(rhs)) { - rhs <- list(rhs) - } - if (is.LavToken(lhs)) { - lhs <- list(lhs) - } - if (!is.atomic(lhs)) { - if (is.LavOperator(lhs$op)) { - stop2("Unexpected operator ", highlightErrorToken(lhs$op)) - } - } else if (!is.atomic(rhs)) { - if (is.LavOperator(rhs$op)) { - stop2("Unexpected operator ", highlightErrorToken(rhs$op)) - } - } - list(lhs = lhs, op = token, rhs = rhs) -} - - -#' @export -evalToken.LavToken <- function(token, lhs, rhs) { - token -} - - -#' @export -evalToken.LavAdd <- function(token, lhs, rhs) { - if (is.LavToken(rhs)) { - rhs <- list(rhs) - } - if (is.LavToken(lhs)) { - lhs <- list(lhs) - } - c(lhs, rhs) -} - - -#' @export -evalToken.LavModify <- function(token, lhs, rhs) { - structure(rhs, - modifier = lhs) -} - - -#' @export -evalToken.LavBlank <- function(token, lhs, rhs) { - NULL -} - - -#' @export -evalToken.LavInteraction <- function(token, lhs, rhs) { - if (!"LavName" %in% class(lhs) || !"LavName" %in% class(rhs)) { - stop2("Interactions are reserved for objects ", highlightErrorToken(token)) - } - out <- paste0(lhs, token, rhs) - attributes(out) <- attributes(lhs) - out -} - - -#' @export -evalToken.LavComment <- function(token, lhs, rhs) { - NULL -} - - -#' @export -evalToken.LavFunction <- function(token, lhs, rhs) { - functionCall <- paste0(token, stringr::str_c(unlist(rhs), collapse = ","), ")") - out <- eval(rlang::parse_expr(functionCall), envir = modEnv) - attributes(out) <- attributes(token) - out -} - - -#' @export -evalToken.LeftBracket <- function(token, lhs, rhs) { - rhs -} - - -#' @export -evalToken.RightBracket <- function(token, lhs, rhs) { - lhs -} - - -#' @export -evalToken.LavSeperator <- function(token, lhs, rhs) { - if ("LavToken" %in% class(rhs)) { - rhs <- list(rhs) - } - if ("LavToken" %in% class(lhs)) { - lhs <- list(lhs) - } - c(lhs, rhs) -} - - -evalTokens <- function(syntaxTree) { - if (is.null(syntaxTree) || length(syntaxTree) == 0) { - return(NULL) - } - lhs <- evalTokens(syntaxTree$lhs) - rhs <- evalTokens(syntaxTree$rhs) - - return(evalToken(syntaxTree$node, - lhs = lhs, - rhs = rhs)) -} - - -parseSyntaxTrees <- function(syntaxTrees) { - lapply(syntaxTrees, evalTokens) -} - - -createParTableBranch <- function(syntaxTree) { - if (is.null(syntaxTree)) { - return(NULL) - } - rhs <- vector("character", length(syntaxTree[["rhs"]])) - mod <- rhs - - for (i in seq_along(syntaxTree[["rhs"]])) { - rhs[[i]] <- getTokenString(syntaxTree[["rhs"]][[i]]) - modifier <- getTokenString(attr(syntaxTree[["rhs"]][[i]], "modifier")) - if (!is.null(modifier)) { - mod[[i]] <- modifier - } - } - lhs <- vapply(syntaxTree[["lhs"]], FUN.VALUE = character(1L), - FUN = getTokenString) - lhs <- vapply(lhs, FUN.VALUE = character(length(rhs)), - FUN = function(x, len) - rep(x, len), - len = length(rhs)) |> as.vector() - op <- rep(getTokenString(syntaxTree$op), length(rhs)) - data.frame(lhs = lhs, op = op, rhs = rhs, mod = mod) -} - - -#' Generate parameter table for lavaan syntax -#' -#' @param syntax model syntax -#' -#' @return data.frame with columns lhs, op, rhs, mod -#' @export modsemify -#' -#' @examples -#' library(modsem) -#' m1 <- ' -#' # Outer Model -#' X =~ x1 + x2 +x3 -#' Y =~ y1 + y2 + y3 -#' Z =~ z1 + z2 + z3 -#' -#' # Inner model -#' Y ~ X + Z + X:Z -#'' -#' modsemify(m1) -modsemify <- function(syntax) { - stopif(!is.character(syntax) && length(syntax) > 1, - "Syntax is not a string og length 1") - syntaxTrees <- createSyntaxTreesSyntax(syntax) - parsedTrees <- parseSyntaxTrees(syntaxTrees) - purrr::list_rbind(lapply(parsedTrees, - FUN = createParTableBranch)) -} - - -parTableToSyntax <- function(parTable, removeColon = FALSE) { - out <- '' - if (removeColon) { - parTable$lhs <- stringr::str_remove_all(parTable$lhs, ":") - parTable$rhs <- stringr::str_remove_all(parTable$rhs, ":") - parTable$mod <- stringr::str_remove_all(parTable$mod, ":") - } - for (i in 1:nrow(parTable)) { - if (parTable[["mod"]][i] != "") { - modifier <- paste0(parTable[["mod"]][[i]], "*") - } else { - modifier <- "" - } - line <- paste0(parTable[["lhs"]][[i]], " ", - parTable[["op"]][[i]], " ", - modifier, - parTable[["rhs"]][[i]], "\n") - out <- paste0(out, line) - } - - out -} - - -mergeTokens <- function(x, y) { - stopif(!"LavName" %in% class(x) || !"LavName" %in% class(x), - "Interactions are reserved for objects ", - highlightErrorToken(x)) - - out <- paste0(x, y) - attributes(out) <- attributes(x) - out - -} diff --git a/R/plot_interaction.R b/R/plot_interaction.R deleted file mode 100644 index 43e5026..0000000 --- a/R/plot_interaction.R +++ /dev/null @@ -1,125 +0,0 @@ -#' Plot Interaction Effects -#' -#' @param x The name of the variable on the x-axis -#' @param z The name of the moderator variable -#' @param y The name of the outcome variable -#' @param xz The name of the interaction term. If the interaction term is not specified, it -#' it will be created using `x` and `z`. -#' @param vals_x The values of the x variable to plot, the more values the smoother the std.error-area will be -#' @param vals_z The values of the moderator variable to plot. A seperate regression -#' line ("y ~ x | z") will be plotted for each value of the moderator variable -#' @param model An object of class `modsem_pi`, `modsem_da`, or `modsem_mplus` -#' @param alpha_se The alpha level for the std.error area -#' @param ... Additional arguments passed to other functions -#' @return A ggplot object -#' @export -#' @examples -#' library(modsem) -#' \dontrun{ -#' m1 <- " -#' # Outer Model -#' X =~ x1 -#' X =~ x2 + x3 -#' Z =~ z1 + z2 + z3 -#' Y =~ y1 + y2 + y3 -#' -#' # Inner model -#' Y ~ X + Z + X:Z -#' " -#' est1 <- modsem(m1, data = oneInt) -#' plot_interaction("X", "Z", "Y", "X:Z", -3:3, c(-0.2, 0), est1) -#' -#' tpb <- " -#' # Outer Model (Based on Hagger et al., 2007) -#' ATT =~ att1 + att2 + att3 + att4 + att5 -#' SN =~ sn1 + sn2 -#' PBC =~ pbc1 + pbc2 + pbc3 -#' INT =~ int1 + int2 + int3 -#' BEH =~ b1 + b2 -#' -#' # Inner Model (Based on Steinmetz et al., 2011) -#' # Causal Relationsships -#' INT ~ ATT + SN + PBC -#' BEH ~ INT + PBC -#' # BEH ~ ATT:PBC -#' BEH ~ PBC:INT -#' # BEH ~ PBC:PBC -#' " -#' -#' est2 <- modsem(tpb, TPB, method = "lms") -#' plot_interaction(x = "INT", z = "PBC", y = "BEH", xz = "PBC:INT", -#' vals_z = c(-0.5, 0.5), model = est2) -#' } -plot_interaction <- function(x, z, y, xz = NULL, vals_x = seq(-3, 3, .001) , - vals_z, model, alpha_se = 0.15, ...) { - if (!isModsemObject(model) && !isLavaanObject(model)) { - stop2("model must be of class 'modsem_pi', 'modsem_da', 'modsem_mplus' or 'lavaan'") - } - - if (is.null(xz)) xz <- paste(x, z, sep = ":") - xz <- c(xz, reverseIntTerm(xz)) - if (!inherits(model, c("modsem_da", "modsem_mplus")) && - !isLavaanObject(model)) { - xz <- stringr::str_remove_all(xz, ":") - } - - parTable <- parameter_estimates(model) - gamma_x <- parTable[parTable$lhs == x & parTable$op == "~", "est"] - - if (isLavaanObject(model)) { - # this won't work for multigroup models - nobs <- unlist(model@Data@nobs) - if (length(nobs) > 1) warning2("plot_interaction is not intended for multigroup models") - n <- nobs[[1]] - - } else { - n <- nrow(model$data) - } - - lVs <- c(x, z, y, xz) - coefs <- parTable[parTable$op == "~" & parTable$rhs %in% lVs & - parTable$lhs == y, ] - vars <- parTable[parTable$op == "~~" & parTable$rhs %in% lVs & - parTable$lhs == parTable$rhs, ] - gamma_x <- coefs[coefs$rhs == x, "est"] - var_x <- calcCovParTable(x, x, parTable) - gamma_z <- coefs[coefs$rhs == z, "est"] - var_z <- calcCovParTable(z, z, parTable) - gamma_xz <- coefs[coefs$rhs %in% xz, "est"] - sd <- sqrt(vars[vars$rhs == y, "est"]) # residual std.error - - if (length(gamma_x) == 0) stop2("coefficient for x not found in model") - if (length(var_x) == 0) stop2("variance of x not found in model") - if (length(gamma_z) == 0) stop2("coefficient for z not found in model") - if (length(var_z) == 0) stop2("variance of z not found in model") - if (length(gamma_xz) == 0) stop2("coefficient for xz not found in model") - if (length(sd) == 0) stop2("residual std.error of y not found in model") - - # creating margins - df <- expand.grid(x = vals_x, z = vals_z) - df$se_x <- calc_se(df$x, var = var_x, n = n, s = sd) - df$proj_y <- gamma_x * df$x + gamma_z + df$z + df$z * df$x * gamma_xz - df$cat_z <- as.factor(df$z) - - se_x <- df$se_x - proj_y <- df$proj_y - cat_z <- df$cat_z - # plotting margins - ggplot2::ggplot(df, ggplot2::aes(x = x, y = proj_y, colour = cat_z, group = cat_z,)) + - ggplot2::geom_smooth(method = "lm", formula = "y ~ x", se = FALSE) + - ggplot2::geom_ribbon(ggplot2::aes(ymin = proj_y - 1.96 * se_x, ymax = proj_y + 1.96 * se_x), - alpha = alpha_se, linewidth = 0, linetype = "blank") + - ggplot2::labs(x = x, y = y, colour = z) -} - - -# function for calculating std.error of predicted value -calc_se <- function(x, var, n, s) { - # x = values of x (predictor), - # this function assumes that 'mean(x) = 0' - # var = variance of x - # n = sample size - # s = residual std.error of model - SSx <- (n - 1) * var # sum of squares of x - s * sqrt(1 / n + x ^ 2 / SSx) -} diff --git a/R/print_partable.R b/R/print_partable.R deleted file mode 100644 index f2bad60..0000000 --- a/R/print_partable.R +++ /dev/null @@ -1,234 +0,0 @@ -colsOut <- c("lhs", "op", "rhs", "est", "std.error", - "z.value", "p.value", "ci.lower", "ci.upper") -header <- c("Variable", "op", "Variable", "Estimate", - "Std.Error", "z.value", "P(>|z|)", "CI.Lower", "CI.Upper") - - -formatParTable <- function(parTable, digits = 3, scientific = FALSE, - ci = FALSE, width = 14) { - parTable <- fillColsParTable(parTable) - - isStructOrMeasure <- parTable$op %in% c("~", "=~", "~~") & - parTable$rhs != "1" & parTable$lhs != parTable$rhs - parTable$lhs[isStructOrMeasure] <- - paste(parTable$lhs[isStructOrMeasure], parTable$op[isStructOrMeasure]) - - - isResVar <- parTable$op == "~~" & parTable$lhs == parTable$rhs - parTable$lhs[parTable$rhs == "1" | isResVar] <- - pasteLabels(parTable$lhs[parTable$rhs == "1" | isResVar], - parTable$label[parTable$rhs == "1" | isResVar], - width = width) - parTable$rhs[parTable$rhs != "1"] <- - pasteLabels(parTable$rhs[parTable$rhs != "1"], - parTable$label[parTable$rhs != "1"], width = width) - - parTable$lhs[!isStructOrMeasure] <- - format(parTable$lhs[!isStructOrMeasure], width = width, justify = "left") - parTable$rhs <- format(parTable$rhs, width = width, justify = "left") - parTable$p.value <- formatPval(parTable$p.value, scientific = scientific) - - if (!ci) { - header <- header[!grepl("CI", header)] - colsOut <- colsOut[!grepl("ci", colsOut)] - } - parTable <- parTable[colsOut] - - for (i in seq_len(length(colsOut) - 3) + 3) { # skip first 3 (lhs, op, rhs) - if (is.numeric(parTable[[i]])) parTable[[i]] <- round(parTable[[i]], digits) - maxWidth <- maxchar(c(header[[i]], parTable[[i]])) - parTable[[i]] <- format(parTable[[i]], width = maxWidth, - digits = digits, justify = "right") - parTable[[i]] <- stringr::str_replace_all(parTable[[i]], "NA", " ") - header[[i]] <- format(header[[i]], width = maxWidth, justify = "right") - } - - list(parTable = parTable, header = header) -} - - -printParTable <- function(parTable, - scientific = FALSE, - ci = FALSE, digits = 3, - loadings = TRUE, - regressions = TRUE, - covariances = TRUE, - intercepts = TRUE, - variances = TRUE, - padWidth = 2, - padWidthLhs = 2, - spacing = 2) { - formatted <- formatParTable(parTable, digits = digits, - ci = ci, scientific = scientific) - fParTable <- formatted$parTable - header <- formatted$header - lhs <- unique(fParTable$lhs) - - pad <- stringr::str_dup(" ", padWidth + padWidthLhs + - maxchar(fParTable$rhs) - 1) - space <- stringr::str_dup(" ", spacing) - - formattedHeader <- - paste0(pad, stringr::str_c(header[-(1:3)], collapse = space), "\n") - - # Measurement model - parTableLoadings <- fParTable[fParTable$op == "=~", ] - if (loadings && NROW(parTableLoadings) > 0) { - cat("Latent Variables:\n", formattedHeader) - printParTableDouble(parTableLoadings, padWidth = padWidth, padWidthLhs = padWidthLhs, - spacing = spacing) - } - - # Regressions - parTableRegressions <- fParTable[parTable$op == "~" & parTable$rhs != "1", ] - if (regressions && NROW(parTableRegressions) > 0) { - cat("\nRegressions:\n", formattedHeader) - printParTableDouble(parTableRegressions, padWidth = padWidth, padWidthLhs = padWidthLhs, - spacing = spacing) - } - - # Intercepts - parTableIntercepts <- fParTable[parTable$op == "~" & parTable$rhs == "1", ] - if (intercepts && NROW(parTableIntercepts) > 0) { - cat("\nIntercepts:\n", formattedHeader) - printParTableSingle(parTableIntercepts, padWidth = padWidth, padWidthLhs = padWidthLhs, - spacing = spacing) - } - - # Covariances - parTableCovariances <- fParTable[parTable$op == "~~" & parTable$lhs != parTable$rhs, ] - if (covariances && NROW(parTableCovariances) > 0) { - cat("\nCovariances:\n", formattedHeader) - printParTableDouble(parTableCovariances, padWidth = padWidth, padWidthLhs = padWidthLhs, - spacing = spacing) - } - - # Variances - parTableVariances <- fParTable[parTable$op == "~~" & parTable$lhs == parTable$rhs, ] - if (variances && NROW(parTableVariances) > 0) { - cat("\nVariances:\n", formattedHeader) - printParTableSingle(parTableVariances, padWidth = padWidth, padWidthLhs = padWidthLhs, - spacing = spacing) - - } -} - - -printParTableDouble <- function(parTable, padWidth = 2, padWidthLhs = 2, - spacing = 2) { - lhs <- unique(parTable$lhs) - pad <- stringr::str_dup(" ", padWidth) - - for (l in lhs) { - cat(paste0(pad, l), "\n") - printRowsParTable(lhs = parTable[parTable$lhs == l, "rhs", drop = FALSE], - rhs = parTable[parTable$lhs == l, -(1:3), drop = FALSE], - padWidthLhs = padWidthLhs + padWidth, - spacing = spacing) - } -} - - -printParTableSingle <- function(parTable, padWidth = 2, padWidthLhs = 2, - spacing = 2) { - lhs <- unique(parTable$lhs) - pad <- stringr::str_dup(" ", padWidth) - - printRowsParTable(lhs = parTable[ , "lhs", drop = FALSE], - rhs = parTable[ , -(1:3), drop = FALSE], - padWidthLhs = padWidthLhs + padWidth, - spacing = spacing) -} - - -printRowsParTable <- function(lhs, rhs, padWidthLhs = 2, - spacing = 2) { - rhs <- lapplyDf(rhs, FUN = format) - padLhs <- stringr::str_dup(" ", padWidthLhs) - space <- stringr::str_dup(" ", spacing) - - out <- "" - for (i in seq_len(nrow(rhs))) { - fStrRhs <- stringr::str_c(rhs[i, ], collapse = space) - out <- paste0(out, padLhs, lhs[i, 1], fStrRhs, "\n") - } - cat(out) -} - - -pasteLabels <- function(vars, labels, width = 14, widthVar = 7, widthLabel = 4) { - pasted <- paste0(vars, " (", labels, ")") - widths <- nchar(pasted) - vars[widths > width] <- - abbreviate(vars[widths > width], minlength = widthVar) - labels[widths > width] <- - abbreviate(labels[widths > width], minlength = widthLabel) - labels[labels != ""] <- paste0("(", labels[labels != ""], ")") - - for (i in seq_along(vars)) { - ncharVar <- nchar(vars[[i]]) - ncharLabel <- nchar(labels[[i]]) - sep <- stringr::str_dup(" ", width - ncharVar - ncharLabel) - - vars[[i]] <- paste0(vars[[i]], sep, labels[[i]]) - } - vars -} - - -allignLhsRhs <- function(lhs, rhs, pad = "", width.out = 50) { - if (length(lhs) != length(rhs)) { - warning("lhs and rhs must have the same length") - if (length(lhs) > length(rhs)) lhs <- rhs[seq_along(lhs)] - else rhs <- lhs[seq_along(rhs)] - } - - out <- "" - width.out <- width.out - nchar(pad) - for (i in seq_along(lhs)) { - ncharLhs <- nchar(lhs[[i]]) - ncharRhs <- nchar(rhs[[i]]) - - sep <- stringr::str_dup(" ", max(0, width.out - ncharLhs - ncharRhs)) - line <- paste0(pad, lhs[[i]], sep, rhs[[i]], "\n") - out <- paste0(out, line) - } - out -} - - -# this is really ugly, but it is the easiest way to get the width of the -# printed table without splitting the function into multiple functions -# in a messy way -getWidthPrintedParTable <- function(parTable, - scientific = FALSE, - ci = FALSE, - digits = 3, - loadings = TRUE, - regressions = TRUE, - covariances = TRUE, - intercepts = TRUE, - variances = TRUE, - padWidth = 2, - padWidthLhs = 2, - spacing = 2) { - formatted <- formatParTable(parTable, digits = digits, - ci = ci, scientific = scientific) - fParTable <- formatted$parTable - header <- formatted$header - lhs <- unique(fParTable$lhs) - - pad <- stringr::str_dup(" ", padWidth + padWidthLhs + - maxchar(fParTable$rhs) - 1) - space <- stringr::str_dup(" ", spacing) - - fStrHeader <- stringr::str_c(header[-(1:3)], collapse = space) - formattedHeader <- paste0(pad, fStrHeader, "\n") - nchar(formattedHeader) -} - - -formatPval <- function(p, scientific = TRUE) { - if (scientific) return(format.pval(p)) - format(round(p, digits = 3), nsmall = 3) -} diff --git a/R/quadrature.R b/R/quadrature.R deleted file mode 100644 index ee07e14..0000000 --- a/R/quadrature.R +++ /dev/null @@ -1,20 +0,0 @@ -# Calculate weights and node points for mixture functions via Gauss-Hermite -# quadrature as defined in Klein & Moosbrugger (2000) -quadrature <- function(m, k, cut = Inf) { - if (k == 0 || m == 0) return(list(n = matrix(0), w = 1, k = 0, m = m)) - singleDimGauss <- fastGHQuad::gaussHermiteData(m) - - nodes <- singleDimGauss$x - weights <- singleDimGauss$w - - select <- abs(nodes) < cut - nodes <- nodes[select] - weights <- weights[select] - m <- length(weights) - - nodes <- lapply(seq_len(k), function(k) nodes) |> - expand.grid() |> as.matrix() - weights <- lapply(seq_len(k), function(k) weights) |> - expand.grid() |> apply(MARGIN = 1, prod) - list(n = nodes * sqrt(2), w = weights * pi ^ (-k/2), k = k, m = m) -} diff --git a/R/residual_cov_pi.R b/R/residual_cov_pi.R deleted file mode 100644 index aad1fce..0000000 --- a/R/residual_cov_pi.R +++ /dev/null @@ -1,173 +0,0 @@ -getParTableResCov <- function(relDf, method, ...) { - switch(method, - "simple" = getParTableResCov.simple(relDf), - "ca" = getParTableResCov.ca(relDf, ...), - "equality" = getParTableResCov.equality(relDf, ...)) -} - - - -# Simple ----------------------------------------------------------------------- -getParTableResCov.simple <- function(relDf) { - if (ncol(relDf) <= 1) { - return(NULL) - } - prodNames <- sort(colnames(relDf)) - uniqueCombinations <- getUniqueCombos(prodNames) - # Now we want to specify the covariance based on shared inds - isShared <- vector("logical", length = nrow(uniqueCombinations)) - - for (i in seq_len(nrow(uniqueCombinations))) { - indsProd1 <- unlist(relDf[uniqueCombinations[i, "V1"]]) - indsProd2 <- unlist(relDf[uniqueCombinations[i, "V2"]]) - # Compare the Inds in prod1 and prod2, and convert to integer - sharedValues <- as.integer(indsProd1 %in% indsProd2) - # Sum the values - numberShared <- sum(sharedValues) - if (numberShared >= 1) { - isShared[[i]] <- TRUE - } else if (numberShared == 0) { - isShared[[i]] <- FALSE - } - } - # Syntax for oblique covariances - prodsSharingInds <- uniqueCombinations[isShared, c("V1", "V2")] - if (nrow(prodsSharingInds) > 0) { - syntaxOblique <- apply(prodsSharingInds, - MARGIN = 1, - FUN = createParTableRow, - op = "~~") |> - purrr::list_rbind() - } else { - syntaxOblique <- NULL - } - prodsNotSharingInds <- uniqueCombinations[!isShared, c("V1", "V2")] - if (nrow(prodsNotSharingInds) > 0) { - syntaxOrthogonal <- apply(prodsNotSharingInds, - MARGIN = 1, - FUN = createParTableRow, - op = "~~", - mod = "0") |> - purrr::list_rbind() - } else { - syntaxOrthogonal <- NULL - } - rbind(syntaxOrthogonal, syntaxOblique) -} - - - -# Rescovs with same indicator constrained to equality -------------------------- -getParTableResCov.equality <- function(relDf, setToZero = FALSE) { - if (ncol(relDf) <= 1) { - return(NULL) - } - prodNames <- sort(colnames(relDf)) - sharedMatrix <- matrix("", nrow = length(prodNames), ncol = length(prodNames), - dimnames = list(prodNames, prodNames)) - # Now we want to specify the covariance based on shared inds - for (i in prodNames) { - for (j in prodNames) { - sharedIndicators <- relDf[[i]][relDf[[i]] %in% relDf[[j]]] - sharedMatrix[i, j] <- stringr::str_c(sharedIndicators, - collapse = "_") - } - } - labelMatrix <- sharedMatrix - labelMatrix <- ifelse(labelMatrix == "", "0", paste0("share_", labelMatrix)) - labelMatrix[upper.tri(labelMatrix, diag = TRUE)] <- "" - - uniqueCombos <- getUniqueCombos(prodNames) - uniqueCombos[["labels"]] <- vector("character", length = nrow(uniqueCombos)) - for (i in seq_len(nrow(uniqueCombos))) { - uniqueCombos[["labels"]][[i]] <- labelMatrix[uniqueCombos[i, "V2"], - uniqueCombos[i, "V1"]] - } - - parTable <- apply(uniqueCombos, MARGIN = 1, - FUN = function(x) - createParTableRow(x[c("V1", "V2")], op = "~~", mod = x[["labels"]]) - ) |> - purrr::list_rbind() - if (!setToZero) parTable <- parTable[parTable$mod != 0, ] - parTable -} - - - -# Constrained Approach --------------------------------------------------------- -getParTableResCov.ca <- function(relDf, pt) { - if (nrow(relDf) > 2) { - stop2("Constrained approach for constraining residual covariances should ", - "not be used with latent products with more than two components") - } - if (ncol(relDf) <= 1) { - return(NULL) - } - prodNames <- colnames(relDf) - labelMatrix <- matrix("", nrow = length(prodNames), ncol = length(prodNames), - dimnames = list(prodNames, prodNames)) - # Now we want to specify the covariance based on shared inds - parTable <- NULL - for (i in 2:nrow(labelMatrix)) { - rhs <- rownames(labelMatrix)[[i]] - - for (j in 1:(i-1)) { - lhs <- colnames(relDf)[[j]] - sharedIndicators <- relDf[[i]] %in% relDf[[j]] - - if (sum(sharedIndicators) >= 1) { - labelMatrix[i, j] <- createLabelCov(rhs, lhs) - - } else if (sum(sharedIndicators) < 1) { - labelMatrix[i, j] <- "0" - } - - parTable <- rbind(parTable, - createParTableRow(c(rhs, lhs), - op = "~~", - mod = labelMatrix[i,j])) - } - - } - - #apply eq constraints to those which are not set to zero - if (length(parTable$mod[parTable$mod != "0"]) > 0) { - eqConstraints <- apply(parTable[parTable$mod != "0", c("lhs", "rhs")], - MARGIN = 1, - FUN = function(vars, relDf) - getFormulaResCovProdInd(vars[["lhs"]], - vars[["rhs"]], - relDf, pt), - relDf = relDf) |> - purrr::list_rbind() - } else { - eqConstraints <- NULL - } - rbind(parTable, eqConstraints) -} - - - -getFormulaResCovProdInd <- function(indProd1, indProd2, relDf, pt) { - if (is.null(indProd1) || is.null(indProd2)) { - return(NULL) - } - cols <- c(indProd1, indProd2) - rowShared <- relDf[relDf[[indProd1]] %in_paired% relDf[[indProd2]], cols] - forceRowNames(rowShared) <- rownames(relDf)[relDf[[indProd1]] %in_paired% relDf[[indProd2]]] - rowNotShared <- relDf[!(relDf[[indProd1]] %in_paired% relDf[[indProd2]]), cols] - forceRowNames(rowNotShared) <- rownames(relDf)[!relDf[[indProd1]] %in_paired% relDf[[indProd2]]] - - latentNotShared <- rownames(rowNotShared) - indShared <- rowShared[1, 1] - indsNotShared <- unlist(rowNotShared[1, 1:2]) - lambdaShared <- createLabelLambda(indsNotShared, latentNotShared) - varLatentNotShared <- trace_path(pt, latentNotShared, latentNotShared) - varIndShared <- createLabelVar(indShared) - - rhs <- paste(lambdaShared[[1]], lambdaShared[[2]], - varLatentNotShared, varIndShared, sep = " * ") - lhs <- createLabelCov(indProd1, indProd2) - createParTableRow(c(lhs, rhs), op = "==") -} diff --git a/R/run_multiple_models.R b/R/run_multiple_models.R deleted file mode 100644 index e004f44..0000000 --- a/R/run_multiple_models.R +++ /dev/null @@ -1,27 +0,0 @@ -# ------------------------------------------------------------------------------ -# Function for running the same model with multiple methods -allMethods <- c("rca", "uca", "ca", "dblcent", "mplus", "pind") -allNativeMethods <- allMethods[allMethods != "mplus"] -fastMethods <- c("rca", "uca", "dblcent", "pind") - -runMultipleMethods <- function(model.syntax, - data, - methods = allNativeMethods, - ...) { - estimates <- structure(vector("list", length = length(methods)), - names = methods) - for (method in methods) { - estimates[[method]] <- tryCatch( - modsem(model.syntax, data, method, ...), - warning = function(w) { - warning2("Warning in ", method, "\n", capturePrint(w), "\n") - modsem(model.syntax, data, method, ...) - }, - error = function(e) { - warning2("Error in ", method, "\n", capturePrint(e)) - NA - } - ) - } - estimates -} diff --git a/R/set_threads.R b/R/set_threads.R deleted file mode 100644 index 86252b7..0000000 --- a/R/set_threads.R +++ /dev/null @@ -1,53 +0,0 @@ -ThreadEnv <- rlang::env(n.threads = NULL) - - -setThreadEnv <- function(n.threads) { - ThreadEnv$n.threads <- n.threads -} - - -resetThreadEnv <- function() { - setThreadEnv(NULL) -} - - -setThreads <- function(n) { - if (is.null(n)) k <- getDefaultThreads() - else if (is.numeric(n)) k <- getThreadsN(n) - else if (is.character(n)) { - k <- switch(n, - "default" = getDefaultThreads(), - "max" = getMaxThreads(), - "min" = getMinThreads(), - stop2("Invalid string specifying number of threads")) - } else stop2("Invalid number of threads, must be integer, NULL, or character") - - ThreadEnv$n.threads <- k -} - - -resetThreads <- function() { - resetThreadEnv() -} - - -getDefaultThreads <- function() { - defaultCRAN <- 2 - getThreadsN(defaultCRAN) -} - - -getMaxThreads <- function() { - parallel::detectCores() -} - - -getMinThreads <- function() { - getThreadsN(n = 1) -} - - -getThreadsN <- function(n) { - ncores <- parallel::detectCores() - ifelse(n >= ncores, ncores, n) -} diff --git a/R/simulate_partable.R b/R/simulate_partable.R deleted file mode 100644 index eeaf384..0000000 --- a/R/simulate_partable.R +++ /dev/null @@ -1,136 +0,0 @@ -simulateDataParTable <- function(parTable, N, colsOVs = NULL, colsLVs = NULL) { - # endogenous variables (etas)model - etas <- getSortedEtas(parTable, isLV = TRUE, checkAny = TRUE) - numEtas <- length(etas) - - indsEtas <- getIndsLVs(parTable, etas) - numIndsEtas <- vapply(indsEtas, FUN.VALUE = vector("integer", 1L), - FUN = length) - allIndsEtas <- unlist(indsEtas) - numAllIndsEtas <- length(allIndsEtas) - - # exogenouts variables (xis) and interaction terms - xis <- getXis(parTable, checkAny = TRUE) - numXis <- length(xis) - - indsXis <- getIndsLVs(parTable, xis) - numIndsXis <- vapply(indsXis, FUN.VALUE = vector("integer", 1L), - FUN = length) - allIndsXis <- unlist(indsXis) - numAllIndsXis <- length(allIndsXis) - - # interaction terms - intTerms <- getIntTerms(parTable) - intTermRows <- getIntTermRows(parTable) - varsIntTerms <- getVarsInts(intTermRows, removeColonNames = FALSE) - if (any(vapply(varsIntTerms, FUN.VALUE = numeric(1L), FUN = length) > 2)) { - stop2("Cannot simulate data for interaction effects with more than two ", - "components, yet") - } - # simulate data for xis - phi <- rmvnormParTable(parTable, type = "phi", N = N) - psi <- rmvnormParTable(parTable, type = "psi", N = N) - theta <- rmvnormParTable(parTable, type = "theta", N = N) - - dataLVs <- phi - - subVarsIntTerms <- varsIntTerms - for (eta in etas) { - toBuildXZ <- vapply(subVarsIntTerms, FUN.VALUE = logical(1L), - FUN = function(x) all(x %in% colnames(dataLVs))) - XZ <- mutliplyPairs(dataLVs, XZ = subVarsIntTerms[toBuildXZ]) - subVarsIntTerms <- subVarsIntTerms[!toBuildXZ] - dataLVs <- cbind(dataLVs, XZ) - - structExprsEta <- parTable[parTable$lhs == eta & - parTable$op == "~" & - parTable$rhs != "1", , drop = FALSE] - alpha <- parTable[parTable$lhs == eta & - parTable$op == "~" & - parTable$rhs == "1", "est"] - if (NROW(alpha) == 0) alpha <- 0 - - y <- rep(alpha, length = N) - for (i in seq_len(NROW(structExprsEta))) { - row <- structExprsEta[i, , drop = FALSE] - y <- y + row$est * dataLVs[ , row$rhs] - } - - y <- y + psi[, eta] - dataLVs <- cbind(dataLVs, matrix(y, nrow = N, dimnames = list(NULL, eta))) - } - - dataXZs <- dataLVs[, intTerms] - dataLVs <- dataLVs[, c(xis, etas)] - dataOVs <- matrix(0, nrow = N, ncol = numAllIndsXis + numAllIndsEtas, - dimnames = list(NULL, c(allIndsXis, allIndsEtas))) - indsLVs <- c(indsXis, indsEtas) - interceptVector <- rep(1, N) - - for (lV in c(xis, etas)) { - inds <- indsLVs[[lV]] - tau <- getIntercepts(inds, parTable = parTable) - lambda <- getLambda(lV = lV, inds = inds, parTable = parTable) - dataOVs[, inds] <- - interceptVector %*% t(tau) + - dataLVs[, lV] %*% t(lambda) + - theta[, inds] - } - - if (!is.null(colsOVs)) dataOVs <- dataOVs[ , colsOVs] - if (!is.null(colsLVs)) dataLVs <- dataLVs[ , colsLVs] - - list(oV = dataOVs, lV = dataLVs) -} - - -rmvnormParTable <- function(parTable, type = "phi", N) { - vars <- switch(type, - phi = getXis(parTable, checkAny = TRUE), - psi = getSortedEtas(parTable, checkAny = TRUE, isLV = TRUE), - theta = getInds(parTable)) - - vcov <- matrix(0, nrow = length(vars), ncol = length(vars), - dimnames = list(vars, vars)) - - vcovExpres <- parTable[parTable$lhs %in% vars & - parTable$op == "~~" & - parTable$rhs %in% vars, ] - - for (i in seq_len(nrow(vcovExpres))) { - lhs <- vcovExpres[i, "lhs"] - rhs <- vcovExpres[i, "rhs"] - est <- vcovExpres[i, "est"] - vcov[lhs, rhs] <- vcov[rhs, lhs] <- est - } - - if (type == "phi") beta0 <- getIntercepts(vars, parTable = parTable) - else beta0 <- rep(0, length(vars)) - - X <- as.matrix(mvtnorm::rmvnorm(n = N, mean = beta0, sigma = vcov)) - colnames(X) <- vars - X -} - - -mutliplyPairs <- function(X, XZ) { - if (!is.list(XZ)) stop("Expected xz to be a list: ", XZ) - prods <- matrix(0, nrow = NROW(X), ncol = length(XZ), - dimnames = list(NULL, names(XZ))) - for (i in seq_len(length(XZ))) { - col <- names(XZ)[[i]] - xz <- XZ[[i]] - prods[, col] <- X[ , xz[[1]]] * X[ , xz[[2]]] - } - prods -} - - -getLambda <- function(lV, inds, parTable) { - lambda <- parTable[parTable$lhs == lV & - parTable$op == "=~" & - parTable$rhs %in% inds, ] - out <- lambda$est - names(out) <- lambda$rhs - out[inds] -} diff --git a/R/tokenizer.R b/R/tokenizer.R deleted file mode 100644 index ef79cd8..0000000 --- a/R/tokenizer.R +++ /dev/null @@ -1,455 +0,0 @@ -modsemParseEnv <- rlang::env( - syntaxLines = NULL -) - - -resetModsemParseEnv <- function() { - modsemParseEnv$syntaxLines <- NULL -} - - -getCharsLine <- function(line, i = 1) { - if (is.null(line) || nchar(line) == 0) { - return(" ") - } else if (i > nchar(line)) { - return(NULL) - } - rest <- getCharsLine(line, i + 1) - c(substr(line, i, i),rest) -} - - -getLines <- function(syntax) { - operators <- c("==", ":=", "~~", "~", "+", "*", "<-", "->", "<", ">") - for (op in operators) { - pattern <- paste0("\\", op, "\\s*[\n|;]") - syntax <- stringr::str_replace_all(syntax, pattern, op) - } - lines <- strsplit(syntax, "\n|;") |> - unlist() |> - as.list() |> - lapply(getCharsLine) - lines <- purrr::imap(lines, - function(x, lNum) - structure(x, lineNumber = lNum)) - lines -} - - -createTokensLine <- function(line, i = 1, - token = NULL, listTokens = list()) { - if (i > length(line)) { - return(appendToList(listTokens, token)) - } - - if (length(listTokens) > 0 && is.MathOperator(last(listTokens))) { - token <- buildMathExprToken(line[i:length(line)], pos = i) - return(appendToList(listTokens, token)) - } - - - if (is.null(token)) { - token <- initializeToken(line[[i]], pos = i, line) - } else { - if (fitsToken(token, nextChar = line[[i]])) { - token <- addCharToken(token, nextChar = line[[i]]) - - } else { - listTokens <- appendToList(listTokens, token) - token <- initializeToken(line[[i]], pos = i, line) - } - } - if ("LavComment" %in% class(token)) { - return(listTokens) - } - return(createTokensLine(line, i + 1, token, listTokens = listTokens)) -} - - -initializeToken <- function(char, pos, line) { - # optimaly this should be a switch - if (grepl("#", char)) { - type <- "LavComment" - priority <- 999 - } else if (grepl("\\s+", char)) { - type <- "LavBlank" - priority <- 999 - } else if (grepl("[[:alpha:]_.]", char)) { - type <- "LavName" - priority <- 10 - } else if (grepl("[\\(\\)]", char)) { - type <- "LavClosure" - priority <- 2 - } else if (grepl("[\\=\\~\\*\\+\\<\\>\\-\\,\\:\\^\\/]", char)) { - type <- "LavOperator" - priority <- 0 - } else if (grepl("[[:alnum:]]", char)) { - type <- "LavNumeric" - priority <- 10 - } else if (grepl('\\"' , char)) { - type <- "LavString" - priority <- 10 - } else { - stop2("Unrecognized class of token in line ", attr(line, "lineNumber"), - " pos ", pos, "\n", - highlightError(line, pos = pos)) - } - structure(char, - pos = pos, - lineNumber = attr(line, "lineNumber"), - priority = priority, - class = c(type, "LavToken")) -} - - -buildMathExprToken <- function(restLine, pos) { - token <- stringr::str_c(restLine, collapse = "") - structure(token, - pos = pos, - lineNumber = attr(restLine, "lineNumber"), - priority = 10, - class = c("LavMathExpr", "LavToken")) -} - - -fitsToken <- function(token, nextChar) { - UseMethod("fitsToken") -} - - -#' @export -fitsToken.LavName <- function(token, nextChar) { - stopif(length(nextChar) != 1, "Wrong length of nextChar", nextChar) - # if object name ends with ( it is a function, - # and next char belongs to a new object - if (grepl("\\($", token)) { - return(FALSE) - } - grepl("[[:alpha:][:digit:]_.\\(]", nextChar)[[1]] -} - - -#' @export -fitsToken.LavString <- function(token, nextChar) { - stopif(length(nextChar) != 1, "Wrong length of nextChar", nextChar) - # if object name ends with ( it is a function, - # and next char belongs to a new object - if (grepl('\\"$', token)) { - return(FALSE) - } - grepl("[[:graph:][:space:]]", nextChar)[[1]] -} - - -#' @export -fitsToken.LavOperator <- function(token, nextChar) { - stopif(length(nextChar) != 1, "Wrong length of nextChar", nextChar) - - completeToken <- paste0(token, nextChar) - switch(completeToken, - "=~" = TRUE, - "~~" = TRUE, - "<-" = TRUE, - "->" = TRUE, - "==" = TRUE, - "!=" = TRUE, - ":=" = TRUE, - FALSE) -} - - -#' @export -fitsToken.LavBlank <- function(token, nextChar) { - stopif(length(nextChar) != 1, "Wrong length of nextChar", nextChar) - grepl("\\s+", nextChar) -} - - -#' @export -fitsToken.LavClosure <- function(token, nextChar) { - FALSE -} - - -#' @export -fitsToken.LavNumeric <- function(token, nextChar) { - stopif(length(nextChar) != 1, "Wrong length of nextChar", nextChar) - grepl("[[:digit:].]", nextChar) -} - - -#' @export -fitsToken.LavComment <- function(token, nextChar) { - TRUE -} - - -assignSubClass <- function(token) { - UseMethod("assignSubClass") -} - - -#' @export -assignSubClass.LavOperator <- function(token) { - switch (getTokenString(token), - "=~" = {subClass <- "LavMeasure"; priority <- 0}, - "~" = {subClass <- "LavPredict"; priority <- 0}, - "~~" = {subClass <- "LavCovar"; priority <- 0}, - "+" = {subClass <- "LavAdd"; priority <- 1}, - "*" = {subClass <- "LavModify"; priority <- 2}, - "<" = {subClass <- "LavLessLeft"; priority <- 0}, - ">" = {subClass <- "LavLessRight"; priority <- 0}, - "==" = {subClass <- "LavEquals"; priority <- 0}, - ":" = {subClass <- "LavInteraction"; priority <- 2}, - ":=" = {subClass <- "LavMediation"; priority <- 0}, - "," = {subClass <- "LavSeperator"; priority <- 0}, - stop2("Unrecognized operator: ", highlightErrorToken(token)) - ) - structure(token, - class = c(subClass, class(token)), - priority = priority) -} - - -#' @export -assignSubClass.LavClosure <- function(token) { - switch(getTokenString(token), - "(" = {subClass <- "LeftBracket"; priority <- 3}, - ")" = {subClass <- "RightBracket"; priority <- 3}, - stop2("Unrecognized operator: ", token) - ) - structure(token, - class = c(subClass, class(token)), - priority = priority) -} - - -#' @export -assignSubClass.LavName <- function(token) { - if (grepl("\\($", getTokenString(token))) { - subClass <- "LavFunction" - priority <- 3 - } else { - subClass <- "LavObject" - priority <- 3 - } - structure(token, - class = c(subClass, class(token)), - priority = priority) -} - - -#' @export -assignSubClass.LavNumeric <- function(token) { - token -} - - -#' @export -assignSubClass.LavToken <- function(token) { - token -} - - -#' @export -assignSubClass.LavMathExpr <- function(token) { - token -} - - -appendToList <- function(list, elem) { - list[[length(list) + 1]] <- elem - list -} - - -prioritizeTokens <- function(listTokens, i = 1, brackets = list(), - nLeftBrackets = 0) { - if (is.null(listTokens) || i > length(listTokens)) { - stopif(nLeftBrackets != 0, "Unmatched left bracket", - highlightErrorToken(brackets[[1]])) - return(listTokens) - } else if (is.RightClosure(listTokens[[i]])) { - brackets <- brackets[-length(brackets)] - nLeftBrackets <- nLeftBrackets - 1 - stopif(nLeftBrackets < 0, "Unmatched right bracket ", - highlightErrorToken(listTokens[[i]])) - } - getTokenPriority(listTokens[[i]]) <- - getTokenPriority(listTokens[[i]]) + nLeftBrackets*10 - - if (is.LeftClosure(listTokens[[i]])) { - brackets <- appendToList(brackets, listTokens[[i]]) - nLeftBrackets <- nLeftBrackets + 1 - } - prioritizeTokens(listTokens, i + 1, brackets = brackets, - nLeftBrackets = nLeftBrackets) -} - - -removeLavBlankLine <- function(line, removeComments = TRUE) { - if (is.null(line) || length(line) == 0) { - return(line) - } - isBlankOrComment <- vapply(line, - FUN.VALUE = logical(1L), - FUN = function(token) is.LavBlankOrComment(token)) - line[!isBlankOrComment] -} - - -tokenizeSyntax <- function(syntax, optimize = TRUE) { - resetModsemParseEnv() - stopif(!is.character(syntax), "Syntax must be a string") - - lines <- getLines(syntax) - modsemParseEnv$syntaxLines <- lines - - tokenizedLines <- lines |> - lapply(createTokensLine) |> - lapply(removeLavBlankLine) |> - lapply(FUN = function(tokens) lapply(tokens, assignSubClass)) - - tokenizedLines <- tokenizedLines |> - lapply(prioritizeTokens) - - isEmpty <- vapply(tokenizedLines, FUN.VALUE = logical(1L), - FUN = function(line) is.null(line) || length(line) == 0) - - tokenizedLines[!isEmpty] -} - - -mergeTokensToString <- function(listTokens) { - vapply(listTokens, FUN.VALUE = character(1L), - FUN = getTokenString) |> - stringr::str_c(collapse = "") -} - - -addCharToken <- function(token, nextChar) { - stopif(length(nextChar) != 1, "Wrong length of nextChar ", nextChar) - - out <- paste0(token, nextChar) - attributes(out) <- attributes(token) - out -} - - -highlightError <- function(line, pos) { - line <- stringr::str_c(line, collapse = "") - indent <- " " - message <- paste0(indent, line, "\n", - indent, stringr::str_c(rep(" ", pos - 1), - collapse = ""), "^") - message -} - - -highlightErrorToken <- function(token) { - lineNumber <- attr(token, "lineNumber") - line <- modsemParseEnv$syntaxLines[[lineNumber]] - if (is.list(line)) { - line <- vapply(line, - FUN.VALUE = character(1L), - FUN = getTokenString) - } - line <- stringr::str_c(line, collapse = "") - pos <- getTokenPosition(token) - indent <- " " - message <- paste0("\n", indent, line, "\n", - indent, stringr::str_c(rep(" ", pos - 1), - collapse = ""), "^") - message -} - - -getTokenString <- function(token) { - token[[1]] -} - - - -getTokenPriority <- function(token) { - attr(token, "priority") -} - - -`getTokenPriority<-` <- function(token, value) { - attr(token, "priority") <- value - token -} - - -getTokenPosition <- function(token) { - attr(token, "pos") -} - - -is.LavToken <- function(token) { - inherits(token, "LavToken") -} - - -is.LavName <- function(token) { - inherits(token, "LavName") -} - - -is.LavOperator <- function(token) { - inherits(token, "LavOperator") -} - - -is.MathOperator <- function(token) { - switch(getTokenString(token), - "==" = TRUE, - "<" = TRUE, - ">" = TRUE, - ":=" = TRUE, - FALSE) -} - - -is.LavBlankOrComment <- function(token) { - inherits(token, "LavBlank") || inherits(token, "LavComment") -} - - -is.LeftClosure <- function(token) { - inherits(token, "LeftBracket") || inherits(token, "LavFunction") -} - - -is.RightClosure <- function(token) { - inherits(token, "RightBracket") -} - - -is.LavClosure <- function(token) { - inherits(token, "LavClosure") -} - - -is.LavOperator <- function(token) { - inherits(token, "LavOperator") -} - - -is.firstClassOperator <- function(token) { - switch(getTokenString(token), - "=~" = TRUE, - "~" = TRUE, - "~~" = TRUE, - "<" = TRUE, - ">" = TRUE, - "==" = TRUE, - FALSE) -} - - -#' @export -as.character.LavToken <- function(x, ...) { - attributes(x) <- NULL - x -} diff --git a/R/trace_paths_wright.R b/R/trace_paths_wright.R deleted file mode 100644 index ad7235b..0000000 --- a/R/trace_paths_wright.R +++ /dev/null @@ -1,158 +0,0 @@ -# Functions for tracing paths in SEMs, used both for calculating (co-)variances, -# as well as calculating formulas for (co-)variances. -prepParTable <- function(pt, addCovPt = TRUE, maxlen = 100) { - # Remove any potential ':' from the model - pt <- lapplyDf(pt, stringr::str_remove_all, pattern = ":") - structuralVars <- pt[pt$op == "~" & pt$rhs != "1", c("lhs", "rhs")] |> - unlist() |> unique() - pt <- pt[pt$lhs %in% structuralVars & pt$rhs %in% structuralVars, ] - pt$mod[pt$mod == ""] <- apply(pt[pt$mod == "", c("lhs", "op", "rhs")], - MARGIN = 1, FUN = stringr::str_c, collapse = "") - if (addCovPt) { - reversedCovPaths <- pt[pt$lhs != pt$rhs & pt$op == "~~", ] - colnames(reversedCovPaths) <- c("rhs", "op", "lhs", "mod") - pt <- rbind(pt, reversedCovPaths) - } - - pt -} - - -tracePathsRecurively = function(x, y, pt, maxlen, currentPath = c(), - covCount = 0, from = "lhs", depth = 1) { - if (depth > maxlen) { - warning2("Encountered a non-recursive model (infinite loop) when tracing paths") - return(NULL) - } - - if (covCount > 1) return(NULL) - if (x == y && from == "rhs") return(list(currentPath)) - - branches <- pt[pt[[from]] == x, ] - if (NROW(branches) == 0) return(NULL) - - paths <- list() - for (i in seq_len(nrow(branches))) { - branch <- branches[i, ] - nextFrom <- from - nextCovCount <- covCount - nextVar <- ifelse(from == "lhs", yes = branch$rhs, no = branch$lhs) - - # reverse direction of travel if covariance (but not before selecting next variable) - if (branch$op == "~~") { - nextCovCount <- covCount + 1 - nextFrom <- ifelse(from == "lhs", yes = "rhs", no = "lhs") - } - - newPaths <- tracePathsRecurively(x = nextVar, y = y, pt = pt, maxlen = maxlen, - currentPath = c(currentPath, branch$mod), - covCount = nextCovCount, - from = nextFrom, depth = depth + 1) - paths <- c(paths, newPaths) - - } - paths -} - - -cleanTracedPaths <- function(paths) { - squaredExprs <- purrr::map_chr(paths, function(x) { - reps <- as.data.frame(table(sort(x))) - dplyr::if_else(reps[[2]] > 1, - true = paste(reps[[1]], reps[[2]], sep = " ^ "), - false = reps[[1]]) |> - stringr::str_c(collapse = " * ") - }) - reps <- as.data.frame(table(squaredExprs)) - dplyr::if_else(reps[[2]] > 1, - true = paste(reps[[2]], reps[[1]], sep = " * "), - false = reps[[1]]) |> - stringr::str_c(collapse = " + ") -} - - -generateSyntax <- function(x, y, pt, maxlen = 100, parenthesis = TRUE, ...) { - pt <- prepParTable(pt, ...) - paths <- tracePathsRecurively(x = x, y = y, pt = pt, maxlen = maxlen) - - if (!length(paths)) return(NA) - cleaned <- cleanTracedPaths(paths) - if (parenthesis) cleaned <- paste0("(", cleaned, ")") - cleaned -} - - -addMissingCovariances <- function(pt) { - pt <- pt[pt$op != "=~" & pt$rhs != "1", ] - - xis <- getXis(pt, checkAny = FALSE, isLV = FALSE) - xis <- xis[!grepl(":", xis)] - - if (length(xis)) { - cxis <- getUniqueCombos(xis, match = TRUE) - phi <- data.frame(lhs = cxis[[1]], op = "~~", rhs = cxis[[2]], mod = "") - } else phi <- NULL - - etas <- getEtas(pt, checkAny = FALSE, isLV = FALSE) - if (length(etas)) { - psi <- data.frame(lhs = etas, op = "~~", rhs = etas, mod = "") - } else psi <- NULL - - if (is.null(psi) && is.null(phi)) return(pt) - - covs <- rbind(psi, phi) - for (i in seq_len(nrow(covs))) { - row <- covs[i, , drop = FALSE] - if (isRowInParTable(row = row, pt = pt, ignore = "mod")) next - pt <- rbind(pt, row) - } - - pt -} - - -#' Estimate formulas for (co-)variance paths using Wright's path tracing rules -#' -#' @param pt A data frame with columns lhs, op, rhs, and mod, from modsemify(syntax) -#' @param x source variable -#' @param y destination variable -#' @param parenthesis if TRUE, the output will be enclosed in parenthesis -#' @param missing.cov if TRUE covariances missing from the model syntax will be added -#' @param measurement.model if TRUE, the function will use the measurement model -#' @param maxlen maximum length of a path before aborting -#' @param ... additional arguments passed to trace_path -#' -#' @return A string with the estimated path (simplified if possible) -#' @export -#' @description -#' This function estimates the path from x to y using the path tracing rules, -#' note that it only works with structural parameters, so "=~" are ignored. unless -#' measurement.model = TRUE. -#' you want to use the measurement model, -#' "~" in the mod column of pt. -#' @examples -#' library(modsem) -#' m1 <- ' -#' # Outer Model -#' X =~ x1 + x2 +x3 -#' Y =~ y1 + y2 + y3 -#' Z =~ z1 + z2 + z3 -#' -#' # Inner model -#' Y ~ X + Z + X:Z -#'' -#' pt <- modsemify(m1) -#' trace_path(pt, x = "Y", y = "Y", missing.cov = TRUE) # variance of Y -trace_path <- function(pt, x, y, parenthesis = TRUE, missing.cov = FALSE, - measurement.model = FALSE, maxlen = 100, ...) { - if (measurement.model) { - measurmentRows <- pt$op == "=~" - measurmentRowsRhs <- pt$rhs[measurmentRows] - measurmentRowsLhs <- pt$lhs[measurmentRows] - pt$op[measurmentRows] <- "~" - pt$lhs[measurmentRows] <- measurmentRowsRhs - pt$rhs[measurmentRows] <- measurmentRowsLhs - } - if (missing.cov) pt <- addMissingCovariances(pt) - generateSyntax(x = x, y = y, pt = pt, maxlen = maxlen, parenthesis = parenthesis, ...) -} diff --git a/R/utils.R b/R/utils.R deleted file mode 100644 index 329e1eb..0000000 --- a/R/utils.R +++ /dev/null @@ -1,281 +0,0 @@ -warning2 <- function(...) { - warning(..., call. = FALSE) -} - - -stop2 <- function(...) { - stop(..., call. = FALSE) -} - - -stopif <- function(cond, ...) { - if (cond) stop2(...) -} - - -warnif <- function(cond, ...) { - if (cond) stop2(...) -} - - -# utils for all methods -calcCovParTable <- function(x, y, parTable, measurement.model = FALSE) { - parTable$mod <- as.character(parTable$est) - parTable <- parTable[c("lhs", "op", "rhs", "mod")] - eval(parse(text = trace_path(parTable, x, y, - measurement.model = measurement.model))) -} - - -reverseIntTerm <- function(xz) { - if (length(xz) > 1) stop2("xz must be a single string") - stringr::str_c(rev(stringr::str_split_1(xz, ":")), collapse = ":") -} - - -getEtas <- function(parTable, isLV = FALSE, checkAny = TRUE) { - cond <- parTable$op == "~" & parTable$rhs != "1" - if (isLV) { - lVs <- unique(parTable[parTable$op == "=~", "lhs"]) - cond <- cond & parTable$lhs %in% lVs - } - etas <- unique(parTable[cond, "lhs"]) - if (checkAny && length(etas) == 0) stop2("No etas found") - etas -} - - -getSortedEtas <- function(parTable, isLV = FALSE, checkAny = TRUE) { - structExprs <- parTable[parTable$op == "~" & parTable$rhs != "1", ] - unsortedEtas <- getEtas(parTable, isLV = isLV, checkAny = checkAny) - sortedEtas <- c() - while (length(sortedEtas) < length(unsortedEtas) && nrow(structExprs) > 0) { - if (all(unique(structExprs$lhs) %in% structExprs$rhs)) { - stop2("Model is non-recursive") - } - for (i in seq_len(nrow(structExprs))) { - eta <- structExprs[i, "lhs"] - if (eta %in% structExprs$rhs) next - sortedEtas <- c(eta, sortedEtas) - structExprs <- structExprs[!grepl(eta, structExprs$lhs), ] - break - } - } - - if (!all(sortedEtas %in% unsortedEtas) && - length(sortedEtas) != length(unsortedEtas)) { - warning("unable to sort etas") - return(unsortedEtas) - } - sortedEtas -} - - -getXis <- function(parTable, etas = NULL, isLV = TRUE, checkAny = TRUE) { - if (is.null(etas)) etas <- getEtas(parTable, isLV = isLV) - if (!isLV) { - xis <- unique(parTable[parTable$rhs != "1" & - parTable$lhs %in% etas, "rhs"]) - return(xis) - } - xis <- unique(parTable[parTable$op == "=~" & - !parTable$lhs %in% etas, "lhs"]) - if (checkAny && length(xis) == 0) stop2("No xis found") - xis -} - - -getLVs <- function(parTable) { - unique(parTable[parTable$op == "=~", "lhs"]) -} - - -getOVs <- function(parTable = NULL, model.syntax = NULL) { - if (!is.null(model.syntax)) parTable <- modsemify(model.syntax) - if (is.null(parTable)) stop2("Missing parTable") - lVs <- getLVs(parTable) - select <- parTable$op %in% c("=~", "~", "~~") & parTable$rhs != "1" - vars <- unique(c(parTable$lhs[select], parTable$rhs[select])) - vars[!vars %in% lVs] -} - - -getIndsLVs <- function(parTable, lVs) { - measrExprs <- parTable[parTable$op == "=~" & - parTable$lhs %in% lVs, ] - if (NROW(measrExprs) == 0) stop2("No measurement expressions found, for", lVs) - lapplyNamed(lVs, FUN = function(lV) measrExprs[measrExprs$lhs == lV, "rhs"], - names = lVs) -} - - -getInds <- function(parTable) { - unique(unlist(getIndsLVs(parTable, lVs = getLVs(parTable)))) -} - - -getIntTermRows <- function(parTable) { - structExprs <- parTable[parTable$op == "~" & parTable$rhs != "1", ] - structExprs[grepl(":", structExprs$rhs), ] -} - - -getIntTerms <- function(parTable) { - structExprs <- parTable[parTable$op == "~" & parTable$rhs != "1", ] - unique(structExprs[grepl(":", structExprs$rhs), "rhs"]) -} - - -getVarsInts <- function(intTerms, removeColonNames = TRUE) { - if (removeColonNames) names <- stringr::str_remove_all(intTerms$rhs, ":") - else names <- intTerms$rhs - lapplyNamed(intTerms$rhs, FUN = stringr::str_split_1, pattern = ":", - names = names) -} - - -maxchar <- function(x) { - max(nchar(x), na.rm = TRUE) -} - - -fillColsParTable <- function(parTable) { - colNames <- c("lhs", "op", "rhs", "label", "est", - "std.error", "z.value", "p.value", "ci.lower", "ci.upper") - parTable[colNames[!colNames %in% colnames(parTable)]] <- NA - parTable[colNames] -} - - -# function for getting unique combinations of two values in x -getUniqueCombos <- function(x, match = FALSE) { - # Base case, x is 1 length long and there are no unique combos - if (length(x) <= 1) { - return(NULL) - } - rest <- getUniqueCombos(x[-1], match = FALSE) - combos <- data.frame(V1 = rep(x[[1]], length(x) - 1), - V2 = x[-1]) - if (match) combos <- rbind(data.frame(V1 = x, V2 = x), combos) - rbind(combos, rest) -} - - -lastRow <- function(x) { - x[nrow(x), ] -} - - -lapplyMatrix <- function(X, FUN, FUN.VALUE, ...) { - matrix(vapply(X, FUN.VALUE = FUN.VALUE, FUN = FUN, ...), - nrow = length(FUN.VALUE), ncol = length(X), ...) -} - - -# Wrapper of lapply where elements are names based on names argument, by default names -# are based on X -lapplyNamed <- function(X, FUN, ..., names = X) { - structure(lapply(X, FUN, ...), - names = names) -} - - -lapplyDf <- function(df, FUN, ...) { - structure(lapply(df, FUN, ...), - names = names(df), - row.names = seq_len(nrow(df)), - class = "data.frame") -} - - -isModsemObject <- function(x) { - inherits(x, c("modsem_pi", "modsem_da", "modsem_mplus")) -} - - -getIntercept <- function(x, parTable) { - if (length(x) > 1) stop2("x must be a single string") - - intercept <- parTable[parTable$lhs == x & - parTable$op == "~" & - parTable$rhs == "1", "est"] - - if (length(intercept) == 0) return(0) - intercept -} - - -getIntercepts <- function(x, parTable) { - out <- vapply(x, FUN.VALUE = numeric(1L), FUN = function(x_i) - getIntercept(x_i, parTable = parTable)) - names(out) <- x - out -} - - -getMean <- function(x, parTable) { - if (length(x) > 1) stop2("x must be a single string") - - meanY <- getIntercept(x, parTable = parTable) - gamma <- parTable[parTable$lhs == x & parTable$op == "~" & - parTable$rhs != "1", , drop = FALSE] - - if (NROW(gamma) == 0) return(meanY) - for (i in NROW(gamma)) { - meanX <- getMean(gamma[i, "rhs"], parTable = parTable) - meanY <- meanY + gamma[i, "est"] * meanX - } - - meanY -} - - -centerInteraction <- function(parTable) { - rows <- getIntTermRows(parTable) - for (i in NROW(rows)) { - Y <- rows[i, "lhs"] - XZ <- unlist(stringr::str_split(rows[i, "rhs"], ":")) - X <- XZ[[1]] - Z <- XZ[[2]] - - meanX <- getMean(X, parTable) - meanZ <- getMean(Z, parTable) - - gammaXZ <- rows[i, "est"] - gamma <- parTable[parTable$lhs == Y & parTable$op == "~" & - parTable$rhs != "1", , drop = FALSE] - gammaX <- gamma[gamma$rhs == X, "est"] + gammaXZ * meanZ - - gammaZ <- gamma[gamma$rhs == Z, "est"] + gammaXZ * meanX - - parTable[parTable$lhs == Y & parTable$op == "~" & - parTable$rhs == X, "est"] <- gammaX - - parTable[parTable$lhs == Y & parTable$op == "~" & - parTable$rhs == Z, "est"] <- gammaZ - } - - parTable -} - - -getWarningWrapper <- function(silent = FALSE) { # function factory - if (silent) return(suppressWarnings) - function(x) x -} - - -isRowInParTable <- function(row, pt, ignore = NULL) { - if (!is.null(ignore)) { - row <- row[!names(row) %in% ignore] - pt <- pt[!colnames(pt) %in% ignore] - } - - for (i in seq_len(nrow(pt))) { - x <- unlist(row) - y <- unlist(pt[i, ]) - if (all(x == y)) return(TRUE) - } - - return(FALSE) -} diff --git a/R/utils_da.R b/R/utils_da.R deleted file mode 100644 index f80bbd7..0000000 --- a/R/utils_da.R +++ /dev/null @@ -1,388 +0,0 @@ -# Utils for lms approach -# Last updated: 31.07.2024 - - -getFreeParams <- function(model) { - model$freeParams -} - - -fetch <- function(x, pattern = ".*") { - x[grepl(pattern, names(x))] -} - - -stripMatrices <- function(matrices, fill = -1) { - lapply(matrices, function(mat) { - mat[!is.na(mat)] <- fill - mat - }) -} - - -removeInteractions <- function(model) { - model$matrices$OmegaEtaXi[TRUE] <- 0 - model$matrices$OmegaXiXi[TRUE] <- 0 - model -} - - -# Faster version of mvtnorm::dmvnorm() given that sigma is positive -# there are some drawbacks to using mvnfast. In particular, -# its a little less consistent -dmvn <- function(X, mean, sigma, log = FALSE) { - return(tryCatch(mvnfast::dmvn(X, mean, sigma, log, ncores = 2), #ThreadEnv$n.threads), - error = function(e) mvtnorm::dmvnorm(X, mean, sigma, log))) -} - - -diagPartitionedMat <- function(X, Y) { - if (is.null(X)) return(Y) else if (is.null(Y)) return(X) - structure(rbind(cbind(X, matrix(0, nrow = NROW(X), ncol = NCOL(Y))), - cbind(matrix(0, nrow = NROW(Y), ncol = NCOL(X)), Y)), - dimnames = list(c(rownames(X), rownames(Y)), - c(colnames(X), colnames(Y)))) -} - - -formatNumeric <- function(x, digits = 3) { - if (is.numeric(x)) { - format(round(x, digits), nsmall = digits, digits = digits, - trim = FALSE, justify = "right") - } else { - format(x, trim = FALSE, justify = "right") - } -} - - -oneWayTableToDataFrame <- function(table) { - df <- as.data.frame(table) - out <- data.frame(freq = df$Freq) - rownames(out) <- df$Var1 - out -} - - -whichIsMax <- function(x) { - which(x == max(x)) -} - - -getK_NA <- function(omegaEta) { - sum(apply(omegaEta, 1, function(x) any(is.na(x)))) -} - - -sortData <- function(data, allIndsXis, allIndsEtas) { - if (!all(c(allIndsXis, allIndsEtas) %in% colnames(data))) - stop2("Missing Observed Variables in Data") - data[c(allIndsXis, allIndsEtas)] -} - - -anyAllNA <- function(data) { - any(vapply(data, FUN.VALUE = logical(1L), function(x) all(is.na(x)))) -} - - -castDataNumericMatrix <- function(data) { - data <- tryCatch({ - numericData <- lapplyDf(data, FUN = as.numeric) - }, - warning = function(w) { - warning2("Warning in converting data to numeric: \n", w) - numericData <- suppressWarnings(lapplyDf(data, FUN = as.numeric)) - if (anyAllNA(numericData)) stop2("Unable to conver data to type numeric") - numeric - }, - error = function(e) { - stop2("Unable to convert data to type numeric") - }) - as.matrix(data) -} - - -filterData <- function(data) { - completeCases <- stats::complete.cases(data) - if (any(!completeCases)) warning2("Removing missing values case-wise.") - data[completeCases, ] -} - - -cleanAndSortData <- function(data, allIndsXis, allIndsEtas) { - if (is.null(data)) return(NULL) - # sort Data before optimizing starting params - sortData(data, allIndsXis, allIndsEtas) |> - castDataNumericMatrix() |> filterData() -} - - -canBeNumeric <- function(x, includeNA = FALSE) { - if (includeNA) x[x == ""] <- 0 - !is.na(suppressWarnings(as.numeric(x))) -} - - -createDoubleIntTerms <- function(x, z = NULL, sep = ":") { - if (is.null(z)) { - z <- x[[2]] - x <- x[[1]] - } - c(paste0(x, sep, z), paste0(z, sep, x)) -} - - -getFreeOrConstIntTerms <- function(varsInInt, eta, intTerms) { - expr <- intTerms[intTerms$lhs == eta & intTerms$rhs %in% - createDoubleIntTerms(varsInInt), "mod"] - if (canBeNumeric(expr, includeNA = TRUE)) return(as.numeric(expr)) - 0 -} - - -getLabelIntTerms <- function(varsInInt, eta, intTerms) { - expr <- intTerms[intTerms$lhs == eta & intTerms$rhs %in% - createDoubleIntTerms(varsInInt), "mod"] - if (!canBeNumeric(expr)) return(expr) - "" -} - - -getEmptyModel <- function(parTable, cov.syntax, parTableCovModel, - method = "lms") { - parTable$mod <- "" - parTable <- removeConstraintExpressions(parTable) - - if (!is.null(parTableCovModel)) { - parTableCovModel$mod <- "" - parTableCovModel <- removeConstraintExpressions(parTableCovModel) - } - - specifyModelDA(parTable = parTable, method = method, - cov.syntax = cov.syntax, - parTableCovModel = parTableCovModel, - auto.constraints = FALSE, createTheta = FALSE, - checkModel = FALSE) -} - - -#' @export -as.character.matrix <- function(x, empty = TRUE, ...) { - if (empty) x[TRUE] <- "" - matrix(as.character(x), nrow = NROW(x), ncol = NCOL(x), - dimnames = dimnames(x)) -} - - -replaceNonNaModelMatrices <- function(model, value = -999) { - model$matrices <- lapply(model$matrices, function(x) { - x[!is.na(x)] <- value - x - }) - model -} - - -removeUnknownLabels <- function(parTable) { - fixedParams <- unique(parTable[parTable$op %in% c("==", ">", "<"), ]$lhs) - parTable[!parTable$lhs %in% fixedParams & - !parTable$op %in% c("==", ">", "<") & - !parTable$lhs %in% parTable$mod, ] -} - - -getLabeledParamsLavaan <- function(parTable, fixedParams = NULL) { - if (is.null(parTable$label)) return(NULL) - labelRows <- parTable[parTable$label != "" & - !parTable$label %in% fixedParams, - c("est", "label"), drop = FALSE] |> - lapply(unique) - - theta <- as.numeric(labelRows$est) - names(theta) <- labelRows$label - theta -} - - -checkModel <- function(model, covModel = NULL, method = "lms") { - modelXis <- model$info$xis - if (!is.null(covModel$info)) { - covModelEtas <- covModel$info$etas - covModelXis <- covModel$info$xis - if (!all(c(covModelXis, covModelEtas) %in% modelXis)) { - stop2("All latent variables in the cov-model must be an exogenous variable in the main model") - } - if (!all(modelXis %in% c(covModelXis, covModelEtas))) { - stop2("All exogenous variables in main model must be part of the cov-model") - } - } - - checkNodesLms(parTable = rbind(model$parTable, covModel$parTable), - nodes = model$quad$m, method = method) - NULL -} - - -checkNodesLms <- function(parTable, - nodes, - method = "lms", - minNodesXiXi = 16, - minNodesXiEta = 32, - minNodesEtaEta = 48) { - if (method == "qml") return(NULL) - - etas <- getEtas(parTable, isLV = TRUE) - xis <- getXis(parTable, etas = etas, isLV = TRUE) - varsInts <- getVarsInts(getIntTermRows(parTable)) - - nodesXiXi_ok <- TRUE - nodesXiEta_ok <- TRUE - nodesEtaEta_ok <- TRUE - - lapply(varsInts, FUN = function(x) { - if (all(x %in% xis)) nodesXiXi_ok <<- nodes >= minNodesXiXi - else if (all(x %in% etas)) nodesEtaEta_ok <<- nodes >= minNodesEtaEta - else if (any(x %in% etas)) nodesXiEta_ok <<- nodes >= minNodesXiEta - else warning2("Unable to classify latent variables in interaction terms") - }) - - if (!nodesXiXi_ok) { - warning2("It is recommended that you have at least ", minNodesXiXi, " nodes ", - "for interaction effects between exogenous variables in the lms approach ", - "'nodes = ", nodes, "'") - } - if (!nodesXiEta_ok) { - warning2("It is recommended that you have at least ", minNodesXiEta, " nodes ", - "for interaction effects between exogenous and endogenous variables in the lms approach ", - "'nodes = ", nodes, "'") - } - if (!nodesEtaEta_ok) { - warning2("It is recommended that you have at least ", minNodesEtaEta, " nodes ", - "for interaction effects between endogenous variables in the lms approach ", - "'nodes = ", nodes, "'") - } -} - - -removeInteractionVariances <- function(parTable) { - parTable[!(parTable$op == "~~" & grepl(":", parTable$lhs) & - grepl(":", parTable$rhs) & parTable$rhs == parTable$lhs), ] -} - - -tr <- function(mat) sum(diag(mat)) - - -traceOmegaXiXi <- function(omega, numEta, numXi) { - lastRow <- 0 - lastCol <- 0 - trace <- numeric(numEta) - for (i in seq_len(numEta)) { - trace[[i]] <- tr(omega[seq_len(numXi) + (i - 1) * numXi, - seq_len(numXi) + (i - 1) * numXi]) - } - trace -} - - -diagPartitioned <- function(matrix, length) { - out <- matrix(0, nrow = length * nrow(matrix), - ncol = length * ncol(matrix)) - nrows <- nrow(matrix) - rows <- seq_len(nrows) - ncols <- ncol(matrix) - cols <- seq_len(ncols) - for (i in seq_len(length)) { - out[rows + (i - 1) * nrows, - cols + (i - 1) * ncols] <- matrix - } - out -} - - -repPartitionedRows <- function(matrix, length = 1) { - if (length <= 1) return(matrix) - out <- matrix - for (i in seq_len(length - 1)) { - out <- rbind(out, matrix) - } - out -} - - -repPartitionedCols <- function(matrix, length = 1) { - if (length <= 1) return(matrix) - out <- matrix - for (i in seq_len(length - 1)) { - out <- cbind(out, matrix) - } - out -} - - -diagBindSquareMatrices <- function(X, Y) { - XY <- matrix(0, nrow = NROW(X), ncol = NCOL(Y), - dimnames = list(rownames(X), colnames(Y))) - rbind(cbind(X, XY), cbind(t(XY), Y)) -} - - -#' @export -as.logical.matrix <- function(x, ...) { - structure(x != 0, - dim = dim(x), - dimnames = dimnames(x)) -} - - -isScalingY <- function(x) { - (seq_along(x) %in% which(x == 0)) | seq_along(x) %in% which.max(x == 1) -} - - -runningAverage <- function(x, n = 10) { - if (length(x) < n) return(0) - last <- length(x) - if (last < n) first <- 1 else first <- last - n + 1 - mean(x[first:last]) -} - - -nNegativeLast <- function(x, n = 10) { - if (length(x) < n) return(0) - last <- length(x) - if (last < n) first <- 1 else first <- last - n + 1 - sum(x[first:last] < 0) -} - - -getDegreesOfFreedom <- function(m, coef) { - t <- (m * (m + 1)) / 2 - df <- t - length(coef) - nMeans <- sum(grepl("tau|alpha", names(coef))) - df + nMeans -} - - -getInfoQuad <- function(quad) { - list(dim = quad$k, nodes.dim = quad$m, nodes.total = quad$m ^ quad$k) -} - - -getFixedInterceptSyntax <- function(indicator, syntax, parTable) { - if (is.null(indicator) || is.null(syntax) || - NROW(parTable[parTable$lhs == indicator & - parTable$op == "~" & parTable$rhs == "1", ])) return(syntax) - else addition <- paste0("\n", indicator, " ~ 0 * 1") - paste0(syntax, addition) -} - - -getEtaRowLabelOmega <- function(label) { - stringr::str_split_1(label, "~")[[1]] -} - - -getXiRowLabelOmega <- function(label) { - stringr::str_split_1(label, "~")[[2]] -} diff --git a/R/utils_pi.R b/R/utils_pi.R deleted file mode 100644 index 13fd5bc..0000000 --- a/R/utils_pi.R +++ /dev/null @@ -1,159 +0,0 @@ -# function for selecting rows in a dataframe matching values on a specified column (type chr_vec) -selectRowsByCol <- function(value, df, column) { - column <- as.character(column) - out <- df[df[[column]] == value,] - - if (nrow(out) <= 0) return(NULL) - else return(out) -} - - -# function for selecting values in the column of a dataframe matching values on a specified column (type chr_vec) -selectValuesByCol <- function(value, df, column.value, column.match) { - column.value <- as.character(column.value) - column.match <- as.character(column.match) - - # this should return a vector anyways, but here i force it - out <- as.vector(df[[column.value]][df[[column.match]] == value]) - - if (length(out) <= 0) return(NULL) - else return(out) - -} - - -# scale numeric vector is numeric -scaleIfNumeric <- function(x, scaleFactor = TRUE) { - if (is.null(x)) { - warning2("x in scaleIfNumeric was NULL") - return(NULL) - } - if (scaleFactor == TRUE & is.factor(x)) { - x <- as.numeric(x) - } - if (is.numeric(x)) { - (x - mean(x, na.rm = TRUE))/stats::sd(x, na.rm = TRUE) - } else x -} - - -# A fancy version of purrr::list_cbind() - # This function will remove duplicate columns, before combining the - # dataframes - # note this function might be slow for large df's, I might create a C++ - # version of it -combineListDf <- function(listDf) { - # This function should work recursively - if (is.null(listDf) || length(listDf) < 1) { - return(NULL) - - } else if (length(listDf) == 1) { - return(listDf[[1]]) - - # This shouldnt really be necessary, but in the case that the function - # recieves a dataframe, we should just return it - } else if (is.data.frame(listDf)) { - return(listDf) - } - - # Basecase combine the first two columns of the df - # check if there are matching colnames (the first df has priority) - matchingColnames <- colnames(listDf[[2]]) %in% colnames(listDf[[1]]) - - if (sum(as.integer(matchingColnames)) > 0) { - duplicates <- stringr::str_c(colnames(listDf[[2]])[matchingColnames], - collapse = ", ") - warning2( - "There were some duplicate product indicators, was this intended?\n", - "The duplicates of these product indicators were removed: \n", - duplicates, "\n") - } - - combinedDf <- cbind.data.frame( - listDf[[1]], listDf[[2]][,!matchingColnames, drop = FALSE] - ) - - combineListDf(c(list(combinedDf), listDf[-(1:2)])) -} - - -maxDepth <- function(list, max = 2, depth = 1) { - - if (is.null(list) | !is.list(list)) { - return(depth) - } - - if (depth > max) { - stop2("Incorrectly nested syntax") - } - deepest <- 1 - for (i in seq_along(list)) { - branchDepth <- maxDepth(list[[i]], max = max, depth + 1) - if (branchDepth > deepest) { - deepest <- branchDepth - } - } - deepest - -} - - -capturePrint <- function(x, ...) { - paste(utils::capture.output(print(x, ...)), collapse = "\n") -} - - -rbindParTable <- function(parTable, newRows) { - # Merges rows of two partables, and replaces duplicates in the lhs partable - # check for duplicates in lhs, op & rhs - if (is.null(newRows) || NROW(newRows) == 0 || NCOL(newRows) == 0) { - return(parTable) - } - newParTableRows <- apply(newRows[c("lhs", "op", "rhs")], - MARGIN = 1, - FUN = function(row) - list(row)) |> - purrr::list_flatten() - - duplicateRows <- apply(parTable[c("lhs", "op", "rhs")], - MARGIN = 1, - FUN = function(row, parTableRows) - list(row) %in% parTableRows, - parTableRows = newParTableRows) - if (sum(as.integer(duplicateRows)) > 0) { - warning2("Some duplicates in the parTable was removed, have you accidentally ", - "specified some of these in your syntax? \n", - capturePrint(parTable[duplicateRows, ])) - } - rbind(parTable[!duplicateRows, ], newRows) -} - - -greplRowDf <- function(col, df) { - if (is.null(df)) return(FALSE) - any(apply(df, MARGIN = 2, - FUN = function(dfCol) all(sort(col) == sort(dfCol)))) -} - - -`forceRowNames<-` <- function(df, value) { - attr(df, "row.names") <- value - df -} - - -# see functions for generating formulas for -# constrained approach -`%in_paired%` <- function(x, y) { - out <- vector("logical", length(x)) - for (i in seq_along(x)) { - matches <- y == x[[i]] - if (any(matches)) { - y <- y[!matches] - out[[i]] <- TRUE - } else { - out[[i]] <- FALSE - } - } - out -} diff --git a/README.md b/README.md index 24b116e..c66df68 100644 --- a/README.md +++ b/README.md @@ -1,116 +1,2 @@ -# `modsem` -This is a package which allows you to perform interactions between latent variables (i.e., moderation) in CB-SEM. -See https://kss2k.github.io/intro_modsem/ for a tutorial. - -# To Install -``` -# From CRAN -install.packages("modsem") - -# Latest version from Github -install.packages("devtools") -devtools::install_github("kss2k/modsem", build_vignettes = TRUE) -``` - -# Methods/Approaches - -There are a number of approaches for estimating interaction effects in SEM. In `modsem()`, the `method = "method"` argument allows you to choose which to use. - -- `"ca"` = constrained approach (Algina & Moulder, 2001) - - Note that constraints can become quite complicated for complex models, - particularly when there is an interaction including enodgenous variables. - The method can therefore be quite slow. -- `"uca"` = unconstrained approach (Marsh, 2004) -- `"rca"` = residual centering approach (Little et al., 2006) -- `"dblcent"` = double centering approach (Marsh., 2013) - - default -- `"pind"` = basic product indicator approach (not recommended) -- `"lms"` = The Latent Moderated Structural equations (LMS) approach, see the [vignette](https://kss2k.github.io/intro_modsem/articles/lms_qml.html) -- `"qml"` = The Quasi Maximum Likelihood (QML) approach, see the [vignette](https://kss2k.github.io/intro_modsem/articles/lms_qml.html) -- `"mplus"` - - estimates model through Mplus, if it is installed - -# Examples - -## One interaction -``` -library(modsem) -m1 <- ' - # Outer Model - X =~ x1 + x2 +x3 - Y =~ y1 + y2 + y3 - Z =~ z1 + z2 + z3 - - # Inner model - Y ~ X + Z + X:Z -' - -# Double centering approach -est1_dca <- modsem(m1, oneInt) -summary(est1_dca) - -# Constrained approach -est1_ca <- modsem(m1, oneInt, method = "ca") -summary(est1_ca) - -# QML approach -est1_qml <- modsem(m1, oneInt, method = "qml") -summary(est1_qml, standardized = TRUE) - -# LMS approach -est1_lms <- modsem(m1, oneInt, method = "lms") -summary(est1_lms) -``` - -## Theory Of Planned Behavior -``` -tpb <- " -# Outer Model (Based on Hagger et al., 2007) - ATT =~ att1 + att2 + att3 + att4 + att5 - SN =~ sn1 + sn2 - PBC =~ pbc1 + pbc2 + pbc3 - INT =~ int1 + int2 + int3 - BEH =~ b1 + b2 - -# Inner Model (Based on Steinmetz et al., 2011) - # Causal Relationsships - INT ~ ATT + SN + PBC - BEH ~ INT + PBC - BEH ~ PBC:INT -" - -# double centering approach -est_tpb_dca <- modsem(tpb, data = TPB, method = "dblcent") -summary(est_tpb_dca) - -# Constrained approach using Wrigths path tracing rules for generating -# the appropriate constraints -est_tpb_ca <- modsem(tpb, data = TPB, method = "ca") -summary(est_tpb_ca) - -# LMS approach -est_tpb_lms <- modsem(tpb, data = TPB, method = "lms") -summary(est_tpb_lms, standardized = TRUE) - -# QML approach -est_tpb_qml <- modsem(tpb, data = TPB, method = "qml") -summary(est_tpb_qml, standardized = TRUE) -``` -## Interactions between two observed variables -``` -est2 <- modsem('y1 ~ x1 + z1 + x1:z1', data = oneInt, method = "pind") -summary(est2) - -## Interaction between an obsereved and a latent variable -m3 <- ' - # Outer Model - X =~ x1 + x2 +x3 - Y =~ y1 + y2 + y3 - - # Inner model - Y ~ X + z1 + X:z1 -' - -est3 <- modsem(m3, oneInt, method = "pind") -summary(est3) -``` +# intro_modsem +This is a repo for the pkgdown websiste for the modsem package diff --git a/_pkgdown.yml b/_pkgdown.yml deleted file mode 100644 index d71acfb..0000000 --- a/_pkgdown.yml +++ /dev/null @@ -1,4 +0,0 @@ -url: ~ -template: - bootstrap: 5 - diff --git a/articles/customizing.html b/articles/customizing.html new file mode 100644 index 0000000..8ee1e0d --- /dev/null +++ b/articles/customizing.html @@ -0,0 +1,333 @@ + + + + + + + +customizing interaction terms • modsem + + + + + + + + Skip to contents + + +
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customizing interaction terms

+ + + Source: vignettes/customizing.Rmd +
customizing.Rmd
+
+ + + + +

By default, modsem() creates product indicators for you, based on the +interaction specified in your model. Behind the scenes we can see that +modsem() creates in total 9 variables (product indicators) used as the +indicators for your latent product.

+
+m1 <- '
+# Outer Model
+X =~ x1 + x2 + x3
+Y =~ y1 + y2 + y3
+Z =~ z1 + z2 + z3
+
+# Inner model
+Y ~ X + Z + X:Z 
+'
+
+est1 <- modsem(m1, oneInt)
+cat(est1$syntax)
+#> X =~ x1
+#> X =~ x2
+#> X =~ x3
+#> Y =~ y1
+#> Y =~ y2
+#> Y =~ y3
+#> Z =~ z1
+#> Z =~ z2
+#> Z =~ z3
+#> Y ~ X
+#> Y ~ Z
+#> Y ~ XZ
+#> XZ =~ 1*x1z1
+#> XZ =~ x2z1
+#> XZ =~ x3z1
+#> XZ =~ x1z2
+#> XZ =~ x2z2
+#> XZ =~ x3z2
+#> XZ =~ x1z3
+#> XZ =~ x2z3
+#> XZ =~ x3z3
+#> x1z1 ~~ 0*x2z2
+#> x1z1 ~~ 0*x2z3
+#> x1z1 ~~ 0*x3z2
+#> x1z1 ~~ 0*x3z3
+#> x1z2 ~~ 0*x2z1
+#> x1z2 ~~ 0*x2z3
+#> x1z2 ~~ 0*x3z1
+#> x1z2 ~~ 0*x3z3
+#> x1z3 ~~ 0*x2z1
+#> x1z3 ~~ 0*x2z2
+#> x1z3 ~~ 0*x3z1
+#> x1z3 ~~ 0*x3z2
+#> x2z1 ~~ 0*x3z2
+#> x2z1 ~~ 0*x3z3
+#> x2z2 ~~ 0*x3z1
+#> x2z2 ~~ 0*x3z3
+#> x2z3 ~~ 0*x3z1
+#> x2z3 ~~ 0*x3z2
+#> x1z1 ~~ x1z2
+#> x1z1 ~~ x1z3
+#> x1z1 ~~ x2z1
+#> x1z1 ~~ x3z1
+#> x1z2 ~~ x1z3
+#> x1z2 ~~ x2z2
+#> x1z2 ~~ x3z2
+#> x1z3 ~~ x2z3
+#> x1z3 ~~ x3z3
+#> x2z1 ~~ x2z2
+#> x2z1 ~~ x2z3
+#> x2z1 ~~ x3z1
+#> x2z2 ~~ x2z3
+#> x2z2 ~~ x3z2
+#> x2z3 ~~ x3z3
+#> x3z1 ~~ x3z2
+#> x3z1 ~~ x3z3
+#> x3z2 ~~ x3z3
+

Whilst this often is sufficient, you might want some control over how +these indicators are created. In general, modsem() has two mechanisms +for giving control over the creating of indicator products: 1. By +specifying the measurement model of your latent product your self, and +2. By using the mean() and sum() function, collectively known as +parceling operations.

+
+

Specifying The Measurement Model +

+

By default, modsem() creates all possible combinations of different +product indicators. However, another common approach is to match the +indicators by order. For example, let’s say you have an interaction +between the laten variables X and Z: ‘X =~ x1 + x2’ and ‘Z =~ z1 + z2’. +By default you would get ‘XZ =~ x1z1 + x1z2 + x2z1 + x2z2’. If you +wanted to use the matching approach you would want to get ‘XZ +=~ x1z1 + x2z2’ instead. To achieve this you can use the ‘match = TRUE’ +argument.

+
+m2 <- '
+# Outer Model
+X =~ x1 + x2
+Y =~ y1 + y2
+Z =~ z1 + z2
+
+# Inner model
+Y ~ X + Z + X:Z 
+'
+
+est2 <- modsem(m2, oneInt, match = TRUE)
+summary(est2)
+#> modsem: 
+#> Method = dblcent
+#> lavaan 0.6-18 ended normally after 41 iterations
+#> 
+#>   Estimator                                         ML
+#>   Optimization method                           NLMINB
+#>   Number of model parameters                        22
+#> 
+#>   Number of observations                          2000
+#> 
+#> Model Test User Model:
+#>                                                       
+#>   Test statistic                                11.355
+#>   Degrees of freedom                                14
+#>   P-value (Chi-square)                           0.658
+#> 
+#> Parameter Estimates:
+#> 
+#>   Standard errors                             Standard
+#>   Information                                 Expected
+#>   Information saturated (h1) model          Structured
+#> 
+#> Latent Variables:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>   X =~                                                
+#>     x1                1.000                           
+#>     x2                0.819    0.021   38.127    0.000
+#>   Y =~                                                
+#>     y1                1.000                           
+#>     y2                0.807    0.010   82.495    0.000
+#>   Z =~                                                
+#>     z1                1.000                           
+#>     z2                0.836    0.024   35.392    0.000
+#>   XZ =~                                               
+#>     x1z1              1.000                           
+#>     x2z2              0.645    0.024   26.904    0.000
+#> 
+#> Regressions:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>   Y ~                                                 
+#>     X                 0.688    0.029   23.366    0.000
+#>     Z                 0.576    0.029   20.173    0.000
+#>     XZ                0.706    0.032   22.405    0.000
+#> 
+#> Covariances:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>  .x1z1 ~~                                             
+#>    .x2z2              0.000                           
+#>   X ~~                                                
+#>     Z                 0.202    0.025    8.182    0.000
+#>     XZ                0.003    0.026    0.119    0.905
+#>   Z ~~                                                
+#>     XZ                0.042    0.026    1.621    0.105
+#> 
+#> Variances:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>    .x1                0.179    0.022    8.029    0.000
+#>    .x2                0.151    0.015    9.956    0.000
+#>    .y1                0.184    0.021    8.577    0.000
+#>    .y2                0.136    0.014    9.663    0.000
+#>    .z1                0.197    0.025    7.802    0.000
+#>    .z2                0.138    0.018    7.831    0.000
+#>    .x1z1              0.319    0.035    9.141    0.000
+#>    .x2z2              0.244    0.016   15.369    0.000
+#>     X                 0.962    0.042   23.120    0.000
+#>    .Y                 0.964    0.042   23.110    0.000
+#>     Z                 0.987    0.044   22.260    0.000
+#>     XZ                1.041    0.054   19.441    0.000
+
+
+

More complicated models +

+

I you want even more control you can use the +get_pi_syntax() and get_pi_data() functions, +such that you can extract the modified syntax and data from modsem, and +alter them accordingly. This can be particularly useful in cases where +you want to estimate a model using a feature in lavaan, which isn’t +available in modsem. For example, (as of yet) the syntax for both +ordered- and multigroup models isn’t as flexible as in lavaan. Thus you +can modify the auto-generated syntax (with the altered dataset) from +modsem to suit your needs.

+
+m3 <- '
+# Outer Model
+X =~ x1 + x2
+Y =~ y1 + y2
+Z =~ z1 + z2
+
+# Inner model
+Y ~ X + Z + X:Z 
+'
+syntax <- get_pi_syntax(m3)
+cat(syntax)
+#> X =~ x1
+#> X =~ x2
+#> Y =~ y1
+#> Y =~ y2
+#> Z =~ z1
+#> Z =~ z2
+#> Y ~ X
+#> Y ~ Z
+#> Y ~ XZ
+#> XZ =~ 1*x1z1
+#> XZ =~ x2z1
+#> XZ =~ x1z2
+#> XZ =~ x2z2
+#> x1z1 ~~ 0*x2z2
+#> x1z2 ~~ 0*x2z1
+#> x1z1 ~~ x1z2
+#> x1z1 ~~ x2z1
+#> x1z2 ~~ x2z2
+#> x2z1 ~~ x2z2
+
+data <- get_pi_data(m3, oneInt)
+head(data)
+#>           x1         x2         y1         y2         z1         z2       x1z1
+#> 1  2.4345722  1.3578655  1.4526897  0.9560888  0.8184825 1.60708140 -0.4823019
+#> 2  0.2472734  0.2723201  0.5496756  0.7115311  3.6649148 2.60983102 -2.2680403
+#> 3 -1.3647759 -0.5628205 -0.9835467 -0.6697747  1.7249386 2.10981827 -1.9137416
+#> 4  3.0432836  2.2153763  6.4641465  4.7805981  2.5697116 3.26335379  2.9385205
+#> 5  2.8148841  2.7029616  2.2860280  2.1457643  0.3467850 0.07164577 -1.4009548
+#> 6 -0.5453450 -0.7530642  1.1294876  1.1998472 -0.2362958 0.60252657  1.7465860
+#>         x2z1       x1z2       x2z2
+#> 1 -0.1884837  0.3929380 -0.0730934
+#> 2 -2.6637694 -1.2630544 -1.4547433
+#> 3 -1.4299711 -2.3329864 -1.7383407
+#> 4  1.3971422  3.9837389  1.9273102
+#> 5 -1.1495704 -2.2058995 -1.8169042
+#> 6  2.2950753  0.7717365  1.0568143
+
+
+
+ + + +
+

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+
+ +
+

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+ +

Articles

+
+ +
+

All vignettes

+
+ +
customizing interaction terms
+
+
interaction effects between endogenous variables
+
+
using lavaan functions
+
+
LMS and QML approaches
+
+
methods
+
+
modsem
+
+
observed variables in the LMS- and QML approach
+
+
plotting interaction effects
+
+
quadratic effects
+
+
+
+ + +
+

Developed by Kjell Solem Slupphaug.

+
+ +
+

Site built with pkgdown 2.1.0.

+
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+ + + + + + + diff --git a/articles/interaction_two_etas.html b/articles/interaction_two_etas.html new file mode 100644 index 0000000..7a5997d --- /dev/null +++ b/articles/interaction_two_etas.html @@ -0,0 +1,603 @@ + + + + + + + +interaction effects between endogenous variables • modsem + + + + + + + + Skip to contents + + +
+ + + + +
+
+ +

interaction effects between endogenous variables

+ + + Source: vignettes/interaction_two_etas.Rmd +
interaction_two_etas.Rmd
+
+ + + + +
+

The Problem +

+

Interaction effects between two endogenous (i.e., dependent) +variables work as you would expect for the product indicator methods +("dblcent", "rca", "ca", "uca"). For the lms- and qml +approach however, it is not as straight forward.

+

The lms- and qml approach can (by default) handle interaction effects +between endogenous and exogenous (i.e., independent) variables, but not +interaction effects between two endogenous variables. When there is an +interaction effect between two endogenous variables, the equations +cannot easily be written in ‘reduced’ form – meaning that normal +estimation procedures won’t work.

+
+
+

The Solution +

+

This being said, there is a work-around for these limitations for +both the lms- and qml-approach. In essence, the model can be split into +two parts, one linear and one non-linear. Basically, you can replace the +covariance matrix used in the estimation of the non-linear model, with +the model-implied covariance matrix from a linear model. Thus you can +treat an endogenous variable as if it were exogenous – given that it can +be expressed in a linear model.

+
+
+

Example +

+

Let’s consider the the theory of planned behaviour (TPB) where we +wish to estimate the quadratic effect of INT on BEH (INT:INT). With the +following model:

+
+tpb <- ' 
+# Outer Model (Based on Hagger et al., 2007)
+  ATT =~ att1 + att2 + att3 + att4 + att5
+  SN =~ sn1 + sn2
+  PBC =~ pbc1 + pbc2 + pbc3
+  INT =~ int1 + int2 + int3
+  BEH =~ b1 + b2
+
+# Inner Model (Based on Steinmetz et al., 2011)
+  INT ~ ATT + SN + PBC
+  BEH ~ INT + PBC 
+  BEH ~ INT:INT
+'
+

Since INT is an endogenous variable, its quadratic term (i.e., an +interaction effect with itself) would include two endogenous variables. +Thus we would ordinarily not be able to estimate this model using the +lms- or qml-approach. However, we can split the model into two parts, +one linear and one non-linear. While INT is an endogenous variable, it +can be expressed in a linear model – since it is not affected by any +interaction terms:

+
+tpb_linear <- 'INT ~ PBC + ATT + SN'
+

We could then remove this part from the original model, giving +us:

+
+tpb_nonlinear <- ' 
+# Outer Model (Based on Hagger et al., 2007)
+  ATT =~ att1 + att2 + att3 + att4 + att5
+  SN =~ sn1 + sn2
+  PBC =~ pbc1 + pbc2 + pbc3
+  INT =~ int1 + int2 + int3
+  BEH =~ b1 + b2
+
+# Inner Model (Based on Steinmetz et al., 2011)
+  BEH ~ INT + PBC 
+  BEH ~ INT:INT
+'
+

We could now just estimate the non-linear model, since INT now is an +exogenous variable. This would however not incorporate the structural +model for INT. To address this, we can make modsem replace the +covariance matrix (phi) of (INT, PBC, ATT, SN) with the model-implied +covariance matrix from the linear model, whilst estimating both models +simultaneously. To acheive this, we can use the cov.syntax +argument in modsem:

+
+est_lms <- modsem(tpb_nonlinear, data = TPB, cov.syntax = tpb_linear, method = "lms")
+#> Warning: It is recommended that you have at least 48 nodes for interaction
+#> effects between endogenous variables in the lms approach 'nodes = 24'
+summary(est_lms)
+#> Estimating null model
+#> EM: Iteration =     1, LogLik =   -28467.33, Change = -28467.332
+#> EM: Iteration =     2, LogLik =   -28124.48, Change =    342.852
+#> EM: Iteration =     3, LogLik =   -27825.10, Change =    299.377
+#> EM: Iteration =     4, LogLik =   -27581.12, Change =    243.980
+#> EM: Iteration =     5, LogLik =   -27370.69, Change =    210.431
+#> EM: Iteration =     6, LogLik =   -27175.43, Change =    195.264
+#> EM: Iteration =     7, LogLik =   -27000.48, Change =    174.946
+#> EM: Iteration =     8, LogLik =   -26848.56, Change =    151.919
+#> EM: Iteration =     9, LogLik =   -26711.51, Change =    137.051
+#> EM: Iteration =    10, LogLik =   -26592.54, Change =    118.968
+#> EM: Iteration =    11, LogLik =   -26504.04, Change =     88.504
+#> EM: Iteration =    12, LogLik =   -26466.85, Change =     37.190
+#> EM: Iteration =    13, LogLik =   -26452.38, Change =     14.465
+#> EM: Iteration =    14, LogLik =   -26439.05, Change =     13.331
+#> EM: Iteration =    15, LogLik =   -26430.51, Change =      8.541
+#> EM: Iteration =    16, LogLik =   -26422.81, Change =      7.698
+#> EM: Iteration =    17, LogLik =   -26403.89, Change =     18.924
+#> EM: Iteration =    18, LogLik =   -26402.25, Change =      1.642
+#> EM: Iteration =    19, LogLik =   -26401.21, Change =      1.037
+#> EM: Iteration =    20, LogLik =   -26400.26, Change =      0.955
+#> EM: Iteration =    21, LogLik =   -26399.30, Change =      0.960
+#> EM: Iteration =    22, LogLik =   -26398.64, Change =      0.658
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+#> EM: Iteration =    24, LogLik =   -26397.74, Change =      0.278
+#> EM: Iteration =    25, LogLik =   -26397.33, Change =      0.420
+#> EM: Iteration =    26, LogLik =   -26397.20, Change =      0.128
+#> EM: Iteration =    27, LogLik =   -26396.77, Change =      0.425
+#> EM: Iteration =    28, LogLik =   -26396.48, Change =      0.292
+#> EM: Iteration =    29, LogLik =   -26396.34, Change =      0.145
+#> EM: Iteration =    30, LogLik =   -26396.31, Change =      0.022
+#> EM: Iteration =    31, LogLik =   -26395.95, Change =      0.365
+#> EM: Iteration =    32, LogLik =   -26395.73, Change =      0.215
+#> EM: Iteration =    33, LogLik =   -26395.68, Change =      0.055
+#> EM: Iteration =    34, LogLik =   -26395.37, Change =      0.304
+#> EM: Iteration =    35, LogLik =   -26395.30, Change =      0.079
+#> EM: Iteration =    36, LogLik =   -26395.10, Change =      0.198
+#> EM: Iteration =    37, LogLik =   -26395.04, Change =      0.057
+#> EM: Iteration =    38, LogLik =   -26394.99, Change =      0.054
+#> EM: Iteration =    39, LogLik =   -26394.96, Change =      0.028
+#> EM: Iteration =    40, LogLik =   -26394.93, Change =      0.031
+#> EM: Iteration =    41, LogLik =   -26394.91, Change =      0.019
+#> EM: Iteration =    42, LogLik =   -26394.89, Change =      0.023
+#> EM: Iteration =    43, LogLik =   -26394.87, Change =      0.015
+#> EM: Iteration =    44, LogLik =   -26394.85, Change =      0.019
+#> EM: Iteration =    45, LogLik =   -26394.84, Change =      0.013
+#> EM: Iteration =    46, LogLik =   -26394.82, Change =      0.018
+#> EM: Iteration =    47, LogLik =   -26394.81, Change =      0.012
+#> EM: Iteration =    48, LogLik =   -26394.79, Change =      0.018
+#> EM: Iteration =    49, LogLik =   -26394.78, Change =      0.013
+#> EM: Iteration =    50, LogLik =   -26394.76, Change =      0.020
+#> EM: Iteration =    51, LogLik =   -26394.74, Change =      0.015
+#> EM: Iteration =    52, LogLik =   -26394.72, Change =      0.028
+#> EM: Iteration =    53, LogLik =   -26394.69, Change =      0.022
+#> EM: Iteration =    54, LogLik =   -26394.63, Change =      0.062
+#> EM: Iteration =    55, LogLik =   -26394.58, Change =      0.057
+#> EM: Iteration =    56, LogLik =   -26394.29, Change =      0.284
+#> EM: Iteration =    57, LogLik =   -26394.04, Change =      0.248
+#> EM: Iteration =    58, LogLik =   -26393.97, Change =      0.075
+#> EM: Iteration =    59, LogLik =   -26393.73, Change =      0.240
+#> EM: Iteration =    60, LogLik =   -26393.72, Change =      0.011
+#> EM: Iteration =    61, LogLik =   -26393.71, Change =      0.013
+#> EM: Iteration =    62, LogLik =   -26393.70, Change =      0.005
+#> EM: Iteration =    63, LogLik =   -26393.69, Change =      0.008
+#> EM: Iteration =    64, LogLik =   -26393.69, Change =      0.003
+#> EM: Iteration =    65, LogLik =   -26393.68, Change =      0.007
+#> EM: Iteration =    66, LogLik =   -26393.68, Change =      0.003
+#> EM: Iteration =    67, LogLik =   -26393.67, Change =      0.007
+#> EM: Iteration =    68, LogLik =   -26393.67, Change =      0.003
+#> EM: Iteration =    69, LogLik =   -26393.66, Change =      0.006
+#> EM: Iteration =    70, LogLik =   -26393.66, Change =      0.002
+#> EM: Iteration =    71, LogLik =   -26393.66, Change =      0.006
+#> EM: Iteration =    72, LogLik =   -26393.65, Change =      0.002
+#> EM: Iteration =    73, LogLik =   -26393.65, Change =      0.005
+#> EM: Iteration =    74, LogLik =   -26393.65, Change =      0.002
+#> EM: Iteration =    75, LogLik =   -26393.64, Change =      0.005
+#> EM: Iteration =    76, LogLik =   -26393.64, Change =      0.002
+#> EM: Iteration =    77, LogLik =   -26393.63, Change =      0.004
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+#> EM: Iteration =    79, LogLik =   -26393.63, Change =      0.004
+#> EM: Iteration =    80, LogLik =   -26393.63, Change =      0.002
+#> EM: Iteration =    81, LogLik =   -26393.62, Change =      0.004
+#> EM: Iteration =    82, LogLik =   -26393.62, Change =      0.002
+#> EM: Iteration =    83, LogLik =   -26393.62, Change =      0.004
+#> EM: Iteration =    84, LogLik =   -26393.61, Change =      0.002
+#> EM: Iteration =    85, LogLik =   -26393.61, Change =      0.003
+#> EM: Iteration =    86, LogLik =   -26393.61, Change =      0.002
+#> EM: Iteration =    87, LogLik =   -26393.60, Change =      0.003
+#> EM: Iteration =    88, LogLik =   -26393.60, Change =      0.003
+#> EM: Iteration =    89, LogLik =   -26393.60, Change =      0.003
+#> EM: Iteration =    90, LogLik =   -26393.60, Change =      0.003
+#> EM: Iteration =    91, LogLik =   -26393.59, Change =      0.003
+#> EM: Iteration =    92, LogLik =   -26393.59, Change =      0.003
+#> EM: Iteration =    93, LogLik =   -26393.59, Change =      0.003
+#> EM: Iteration =    94, LogLik =   -26393.59, Change =      0.003
+#> EM: Iteration =    95, LogLik =   -26393.58, Change =      0.003
+#> EM: Iteration =    96, LogLik =   -26393.58, Change =      0.003
+#> EM: Iteration =    97, LogLik =   -26393.58, Change =      0.002
+#> EM: Iteration =    98, LogLik =   -26393.58, Change =      0.003
+#> EM: Iteration =    99, LogLik =   -26393.57, Change =      0.002
+#> EM: Iteration =   100, LogLik =   -26393.57, Change =      0.003
+#> EM: Iteration =   101, LogLik =   -26393.57, Change =      0.002
+#> EM: Iteration =   102, LogLik =   -26393.57, Change =      0.003
+#> EM: Iteration =   103, LogLik =   -26393.56, Change =      0.002
+#> EM: Iteration =   104, LogLik =   -26393.56, Change =      0.003
+#> EM: Iteration =   105, LogLik =   -26393.56, Change =      0.002
+#> EM: Iteration =   106, LogLik =   -26393.56, Change =      0.003
+#> EM: Iteration =   107, LogLik =   -26393.55, Change =      0.002
+#> EM: Iteration =   108, LogLik =   -26393.55, Change =      0.003
+#> EM: Iteration =   109, LogLik =   -26393.55, Change =      0.002
+#> EM: Iteration =   110, LogLik =   -26393.55, Change =      0.003
+#> EM: Iteration =   111, LogLik =   -26393.54, Change =      0.002
+#> EM: Iteration =   112, LogLik =   -26393.54, Change =      0.003
+#> EM: Iteration =   113, LogLik =   -26393.54, Change =      0.002
+#> EM: Iteration =   114, LogLik =   -26393.54, Change =      0.003
+#> EM: Iteration =   115, LogLik =   -26393.53, Change =      0.002
+#> EM: Iteration =   116, LogLik =   -26393.53, Change =      0.003
+#> EM: Iteration =   117, LogLik =   -26393.53, Change =      0.002
+#> EM: Iteration =   118, LogLik =   -26393.53, Change =      0.003
+#> EM: Iteration =   119, LogLik =   -26393.53, Change =      0.001
+#> EM: Iteration =   120, LogLik =   -26393.52, Change =      0.003
+#> EM: Iteration =   121, LogLik =   -26393.52, Change =      0.001
+#> EM: Iteration =   122, LogLik =   -26393.52, Change =      0.003
+#> EM: Iteration =   123, LogLik =   -26393.52, Change =      0.001
+#> EM: Iteration =   124, LogLik =   -26393.51, Change =      0.003
+#> EM: Iteration =   125, LogLik =   -26393.51, Change =      0.001
+#> EM: Iteration =   126, LogLik =   -26393.51, Change =      0.003
+#> EM: Iteration =   127, LogLik =   -26393.51, Change =      0.001
+#> EM: Iteration =   128, LogLik =   -26393.51, Change =      0.003
+#> EM: Iteration =   129, LogLik =   -26393.50, Change =      0.001
+#> EM: Iteration =   130, LogLik =   -26393.50, Change =      0.003
+#> EM: Iteration =   131, LogLik =   -26393.50, Change =      0.001
+#> EM: Iteration =   132, LogLik =   -26393.50, Change =      0.003
+#> EM: Iteration =   133, LogLik =   -26393.50, Change =      0.001
+#> EM: Iteration =   134, LogLik =   -26393.49, Change =      0.003
+#> EM: Iteration =   135, LogLik =   -26393.49, Change =      0.001
+#> EM: Iteration =   136, LogLik =   -26393.49, Change =      0.003
+#> EM: Iteration =   137, LogLik =   -26393.49, Change =      0.001
+#> EM: Iteration =   138, LogLik =   -26393.48, Change =      0.003
+#> EM: Iteration =   139, LogLik =   -26393.48, Change =      0.001
+#> EM: Iteration =   140, LogLik =   -26393.48, Change =      0.003
+#> EM: Iteration =   141, LogLik =   -26393.48, Change =      0.001
+#> EM: Iteration =   142, LogLik =   -26393.48, Change =      0.003
+#> EM: Iteration =   143, LogLik =   -26393.48, Change =      0.001
+#> EM: Iteration =   144, LogLik =   -26393.47, Change =      0.003
+#> EM: Iteration =   145, LogLik =   -26393.47, Change =      0.001
+#> EM: Iteration =   146, LogLik =   -26393.47, Change =      0.003
+#> EM: Iteration =   147, LogLik =   -26393.47, Change =      0.000
+#> EM: Iteration =   148, LogLik =   -26393.46, Change =      0.003
+#> EM: Iteration =   149, LogLik =   -26393.46, Change =      0.000
+#> EM: Iteration =   150, LogLik =   -26393.46, Change =      0.003
+#> EM: Iteration =   151, LogLik =   -26393.46, Change =      0.000
+#> EM: Iteration =   152, LogLik =   -26393.46, Change =      0.004
+#> EM: Iteration =   153, LogLik =   -26393.46, Change =      0.000
+#> EM: Iteration =   154, LogLik =   -26393.45, Change =      0.004
+#> EM: Iteration =   155, LogLik =   -26393.45, Change =      0.000
+#> EM: Iteration =   156, LogLik =   -26393.45, Change =      0.004
+#> EM: Iteration =   157, LogLik =   -26393.45, Change =      0.000
+#> 
+#> modsem (version 1.0.3):
+#>   Estimator                                         LMS
+#>   Optimization method                         EM-NLMINB
+#>   Number of observations                           2000
+#>   Number of iterations                              136
+#>   Loglikelihood                               -23781.36
+#>   Akaike (AIC)                                 47670.72
+#>   Bayesian (BIC)                               47973.16
+#>  
+#> Numerical Integration:
+#>   Points of integration (per dim)                    24
+#>   Dimensions                                          1
+#>   Total points of integration                        24
+#>  
+#> Fit Measures for H0:
+#>   Loglikelihood                                  -26393
+#>   Akaike (AIC)                                 52892.89
+#>   Bayesian (BIC)                               53189.74
+#>   Chi-square                                      66.72
+#>   Degrees of Freedom (Chi-square)                    82
+#>   P-value (Chi-square)                            0.889
+#>   RMSEA                                           0.000
+#>  
+#> Comparative fit to H0 (no interaction effect)
+#>   Loglikelihood change                          2612.09
+#>   Difference test (D)                           5224.18
+#>   Degrees of freedom (D)                              1
+#>   P-value (D)                                     0.000
+#>  
+#> R-Squared:
+#>   BEH                                             0.235
+#>   INT                                             0.365
+#> R-Squared Null-Model (H0):
+#>   BEH                                             0.210
+#>   INT                                             0.367
+#> R-Squared Change:
+#>   BEH                                             0.025
+#>   INT                                            -0.002
+#> 
+#> Parameter Estimates:
+#>   Coefficients                           unstandardized
+#>   Information                                  expected
+#>   Standard errors                              standard
+#>  
+#> Latent Variables:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   INT =~ 
+#>     int1             1.000                             
+#>     int2             0.915      0.016    58.24    0.000
+#>     int3             0.807      0.015    54.49    0.000
+#>   ATT =~ 
+#>     att1             1.000                             
+#>     att2             0.876      0.012    71.51    0.000
+#>     att3             0.787      0.012    66.55    0.000
+#>     att4             0.693      0.011    60.50    0.000
+#>     att5             0.885      0.012    71.68    0.000
+#>   SN =~ 
+#>     sn1              1.000                             
+#>     sn2              0.893      0.017    52.59    0.000
+#>   PBC =~ 
+#>     pbc1             1.000                             
+#>     pbc2             0.912      0.013    69.14    0.000
+#>     pbc3             0.801      0.012    66.52    0.000
+#>   BEH =~ 
+#>     b1               1.000                             
+#>     b2               0.959      0.033    29.32    0.000
+#> 
+#> Regressions:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   BEH ~ 
+#>     INT              0.196      0.026     7.60    0.000
+#>     PBC              0.238      0.022    10.62    0.000
+#>     INT:INT          0.129      0.018     7.29    0.000
+#>   INT ~ 
+#>     PBC              0.218      0.029     7.51    0.000
+#>     ATT              0.210      0.025     8.28    0.000
+#>     SN               0.172      0.028     6.22    0.000
+#> 
+#> Intercepts:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     int1             1.005      0.020    49.42    0.000
+#>     int2             1.004      0.019    53.24    0.000
+#>     int3             0.998      0.017    57.46    0.000
+#>     att1             1.007      0.024    42.32    0.000
+#>     att2             1.001      0.021    47.17    0.000
+#>     att3             1.011      0.019    51.87    0.000
+#>     att4             0.994      0.018    55.74    0.000
+#>     att5             0.986      0.021    46.02    0.000
+#>     sn1              1.000      0.024    42.10    0.000
+#>     sn2              1.005      0.021    47.14    0.000
+#>     pbc1             0.991      0.023    43.04    0.000
+#>     pbc2             0.979      0.021    45.56    0.000
+#>     pbc3             0.986      0.019    51.24    0.000
+#>     b1               0.995      0.024    42.27    0.000
+#>     b2               1.014      0.022    45.68    0.000
+#>     BEH              0.000                             
+#>     INT              0.000                             
+#>     ATT              0.000                             
+#>     SN               0.000                             
+#>     PBC              0.000                             
+#> 
+#> Covariances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   PBC ~~ 
+#>     ATT              0.668      0.079     8.48    0.000
+#>     SN               0.675      0.054    12.48    0.000
+#>   ATT ~~ 
+#>     SN               0.625      0.029    21.63    0.000
+#> 
+#> Variances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     int1             0.161      0.009    18.28    0.000
+#>     int2             0.161      0.008    20.89    0.000
+#>     int3             0.170      0.007    23.51    0.000
+#>     att1             0.167      0.007    23.23    0.000
+#>     att2             0.150      0.006    24.81    0.000
+#>     att3             0.160      0.006    26.51    0.000
+#>     att4             0.163      0.006    27.46    0.000
+#>     att5             0.159      0.006    24.85    0.000
+#>     sn1              0.181      0.015    12.48    0.000
+#>     sn2              0.155      0.012    13.27    0.000
+#>     pbc1             0.145      0.008    18.27    0.000
+#>     pbc2             0.160      0.007    21.74    0.000
+#>     pbc3             0.154      0.007    23.69    0.000
+#>     b1               0.185      0.020     9.23    0.000
+#>     b2               0.136      0.018     7.52    0.000
+#>     BEH              0.475      0.024    19.71    0.000
+#>     PBC              0.960      0.037    26.13    0.000
+#>     ATT              1.000      0.058    17.32    0.000
+#>     SN               0.968      0.086    11.29    0.000
+#>     INT              0.481      0.019    24.97    0.000
+
+est_qml <- modsem(tpb_nonlinear, data = TPB, cov.syntax = tpb_linear, method = "qml")
+summary(est_qml)
+#> Estimating null model
+#> Starting M-step
+#> 
+#> modsem (version 1.0.3):
+#>   Estimator                                         QML
+#>   Optimization method                            NLMINB
+#>   Number of observations                           2000
+#>   Number of iterations                               76
+#>   Loglikelihood                               -26360.52
+#>   Akaike (AIC)                                 52829.04
+#>   Bayesian (BIC)                               53131.49
+#>  
+#> Fit Measures for H0:
+#>   Loglikelihood                                  -26393
+#>   Akaike (AIC)                                 52892.45
+#>   Bayesian (BIC)                               53189.29
+#>   Chi-square                                      66.27
+#>   Degrees of Freedom (Chi-square)                    82
+#>   P-value (Chi-square)                            0.897
+#>   RMSEA                                           0.000
+#>  
+#> Comparative fit to H0 (no interaction effect)
+#>   Loglikelihood change                            32.70
+#>   Difference test (D)                             65.41
+#>   Degrees of freedom (D)                              1
+#>   P-value (D)                                     0.000
+#>  
+#> R-Squared:
+#>   BEH                                             0.239
+#>   INT                                             0.370
+#> R-Squared Null-Model (H0):
+#>   BEH                                             0.210
+#>   INT                                             0.367
+#> R-Squared Change:
+#>   BEH                                             0.029
+#>   INT                                             0.003
+#> 
+#> Parameter Estimates:
+#>   Coefficients                           unstandardized
+#>   Information                                  observed
+#>   Standard errors                              standard
+#>  
+#> Latent Variables:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   INT =~ 
+#>     int1             1.000                             
+#>     int2             0.914      0.015    59.04    0.000
+#>     int3             0.807      0.015    55.65    0.000
+#>   ATT =~ 
+#>     att1             1.000                             
+#>     att2             0.878      0.012    71.56    0.000
+#>     att3             0.789      0.012    66.37    0.000
+#>     att4             0.695      0.011    61.00    0.000
+#>     att5             0.887      0.013    70.85    0.000
+#>   SN =~ 
+#>     sn1              1.000                             
+#>     sn2              0.888      0.017    52.62    0.000
+#>   PBC =~ 
+#>     pbc1             1.000                             
+#>     pbc2             0.913      0.013    69.38    0.000
+#>     pbc3             0.801      0.012    66.08    0.000
+#>   BEH =~ 
+#>     b1               1.000                             
+#>     b2               0.960      0.032    29.91    0.000
+#> 
+#> Regressions:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   BEH ~ 
+#>     INT              0.197      0.025     7.76    0.000
+#>     PBC              0.239      0.023    10.59    0.000
+#>     INT:INT          0.128      0.016     7.88    0.000
+#>   INT ~ 
+#>     PBC              0.222      0.030     7.51    0.000
+#>     ATT              0.213      0.026     8.17    0.000
+#>     SN               0.175      0.028     6.33    0.000
+#> 
+#> Intercepts:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     int1             1.014      0.022    46.96    0.000
+#>     int2             1.012      0.020    50.41    0.000
+#>     int3             1.005      0.018    54.80    0.000
+#>     att1             1.014      0.024    42.01    0.000
+#>     att2             1.007      0.021    46.97    0.000
+#>     att3             1.016      0.020    51.45    0.000
+#>     att4             0.999      0.018    55.65    0.000
+#>     att5             0.992      0.022    45.67    0.000
+#>     sn1              1.006      0.024    41.66    0.000
+#>     sn2              1.010      0.022    46.71    0.000
+#>     pbc1             0.998      0.024    42.41    0.000
+#>     pbc2             0.985      0.022    44.93    0.000
+#>     pbc3             0.991      0.020    50.45    0.000
+#>     b1               0.999      0.023    42.64    0.000
+#>     b2               1.017      0.022    46.25    0.000
+#>     BEH              0.000                             
+#>     INT              0.000                             
+#>     ATT              0.000                             
+#>     SN               0.000                             
+#>     PBC              0.000                             
+#> 
+#> Covariances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   PBC ~~ 
+#>     ATT              0.678      0.029    23.45    0.000
+#>     SN               0.678      0.029    23.08    0.000
+#>   ATT ~~ 
+#>     SN               0.629      0.029    21.70    0.000
+#> 
+#> Variances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     int1             0.158      0.009    18.22    0.000
+#>     int2             0.160      0.008    20.38    0.000
+#>     int3             0.168      0.007    23.63    0.000
+#>     att1             0.167      0.007    23.53    0.000
+#>     att2             0.150      0.006    24.71    0.000
+#>     att3             0.160      0.006    26.38    0.000
+#>     att4             0.162      0.006    27.64    0.000
+#>     att5             0.159      0.006    24.93    0.000
+#>     sn1              0.178      0.015    12.09    0.000
+#>     sn2              0.157      0.012    13.26    0.000
+#>     pbc1             0.145      0.008    18.44    0.000
+#>     pbc2             0.160      0.007    21.42    0.000
+#>     pbc3             0.154      0.006    23.80    0.000
+#>     b1               0.185      0.020     9.42    0.000
+#>     b2               0.135      0.018     7.60    0.000
+#>     BEH              0.475      0.024    19.74    0.000
+#>     PBC              0.962      0.036    27.04    0.000
+#>     ATT              0.998      0.037    26.93    0.000
+#>     SN               0.988      0.039    25.23    0.000
+#>     INT              0.488      0.020    24.59    0.000
+
+
+
+ + + +
+

Developed by Kjell Solem Slupphaug.

+
+ +
+

Site built with pkgdown 2.1.0.

+
+ +
+
+ + + + + + + diff --git a/articles/interaction_two_etas_files/accessible-code-block-0.0.1/empty-anchor.js b/articles/interaction_two_etas_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 0000000..ca349fd --- /dev/null +++ b/articles/interaction_two_etas_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/articles/lavaan.html b/articles/lavaan.html new file mode 100644 index 0000000..e332a56 --- /dev/null +++ b/articles/lavaan.html @@ -0,0 +1,120 @@ + + + + + + + +using lavaan functions • modsem + + + + + + + + Skip to contents + + +
+ + + + +
+
+ +

using lavaan functions

+ + + Source: vignettes/lavaan.Rmd +
lavaan.Rmd
+
+ + + + +

If you’re using one of the product indicator approaches, you might +want to use some lavaan functions on top of the estimated lavaan-object. +To do so you can extract the lavaan-object using the +extract_lavaan() function.

+
+library(lavaan)
+#> This is lavaan 0.6-18
+#> lavaan is FREE software! Please report any bugs.
+
+m1 <- '
+# Outer Model
+X =~ x1 + x2 + x3
+Y =~ y1 + y2 + y3
+Z =~ z1 + z2 + z3
+
+# Inner model
+Y ~ X + Z + X:Z
+'
+
+est <- modsem(m1, oneInt)
+lav_object <- extract_lavaan(est)
+bootstrap <- bootstrapLavaan(lav_object, R = 500)
+
+
+ + + +
+

Developed by Kjell Solem Slupphaug.

+
+ +
+

Site built with pkgdown 2.1.0.

+
+ +
+
+ + + + + + + diff --git a/articles/lavaan_files/accessible-code-block-0.0.1/empty-anchor.js b/articles/lavaan_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 0000000..ca349fd --- /dev/null +++ b/articles/lavaan_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/articles/lms_qml.html b/articles/lms_qml.html new file mode 100644 index 0000000..ea75efa --- /dev/null +++ b/articles/lms_qml.html @@ -0,0 +1,606 @@ + + + + + + + +LMS and QML approaches • modsem + + + + + + + + Skip to contents + + +
+ + + + +
+
+ +

LMS and QML approaches

+ + + Source: vignettes/lms_qml.Rmd +
lms_qml.Rmd
+
+ + + + +
+

The Latent Moderated Structural Equations (LMS) and the Quasi +Maximum Likelihood (QML) approach +

+

Both the LMS- and QML approach works on most models, but interaction +effects with endogenous can be a bit tricky to estimate (see the vignette. +Both approaches (particularily the LMS approach) are quite +computationally intensive, and are thus partly implemented in C++ (using +Rcpp and RcppArmadillo). Additionally starting parameters are estimated +using the double centering approach (and the means of the observed +variables) are used to generate good starting parameters for faster +convergence. If you want to see the progress of the estimation process +you can use ´verbose = TRUE´.

+
+

A Simple Example +

+

Here you can see an example of the LMS approach for a simple model. +By default the summary function calculates fit measures compared to a +null model (i.e., the same model without an interaction term).

+
+library(modsem)
+m1 <- '
+# Outer Model
+  X =~ x1
+  X =~ x2 + x3
+  Z =~ z1 + z2 + z3
+  Y =~ y1 + y2 + y3
+
+# Inner model
+  Y ~ X + Z
+  Y ~ X:Z
+'
+
+lms1 <- modsem(m1, oneInt, method = "lms")
+summary(lms1, standardized = TRUE) # standardized estimates 
+#> Estimating null model
+#> EM: Iteration =     1, LogLik =   -17831.87, Change = -17831.875
+#> EM: Iteration =     2, LogLik =   -17831.87, Change =      0.000
+#> 
+#> modsem (version 1.0.3):
+#>   Estimator                                         LMS
+#>   Optimization method                         EM-NLMINB
+#>   Number of observations                           2000
+#>   Number of iterations                               92
+#>   Loglikelihood                               -14687.85
+#>   Akaike (AIC)                                 29437.71
+#>   Bayesian (BIC)                               29611.34
+#>  
+#> Numerical Integration:
+#>   Points of integration (per dim)                    24
+#>   Dimensions                                          1
+#>   Total points of integration                        24
+#>  
+#> Fit Measures for H0:
+#>   Loglikelihood                                  -17832
+#>   Akaike (AIC)                                 35723.75
+#>   Bayesian (BIC)                               35891.78
+#>   Chi-square                                      17.52
+#>   Degrees of Freedom (Chi-square)                    24
+#>   P-value (Chi-square)                            0.826
+#>   RMSEA                                           0.000
+#>  
+#> Comparative fit to H0 (no interaction effect)
+#>   Loglikelihood change                          3144.02
+#>   Difference test (D)                           6288.04
+#>   Degrees of freedom (D)                              1
+#>   P-value (D)                                     0.000
+#>  
+#> R-Squared:
+#>   Y                                               0.596
+#> R-Squared Null-Model (H0):
+#>   Y                                               0.395
+#> R-Squared Change:
+#>   Y                                               0.201
+#> 
+#> Parameter Estimates:
+#>   Coefficients                             standardized
+#>   Information                                  expected
+#>   Standard errors                              standard
+#>  
+#> Latent Variables:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   X =~ 
+#>     x1               0.926                             
+#>     x2               0.891      0.014    64.39    0.000
+#>     x3               0.912      0.013    67.69    0.000
+#>   Z =~ 
+#>     z1               0.927                             
+#>     z2               0.898      0.014    64.59    0.000
+#>     z3               0.913      0.013    67.87    0.000
+#>   Y =~ 
+#>     y1               0.969                             
+#>     y2               0.954      0.009   105.92    0.000
+#>     y3               0.961      0.009   111.95    0.000
+#> 
+#> Regressions:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   Y ~ 
+#>     X                0.427      0.020    21.79    0.000
+#>     Z                0.370      0.018    20.16    0.000
+#>     X:Z              0.454      0.017    26.28    0.000
+#> 
+#> Covariances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   X ~~ 
+#>     Z                0.199      0.024     8.43    0.000
+#> 
+#> Variances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     x1               0.142      0.007    19.27    0.000
+#>     x2               0.206      0.009    23.86    0.000
+#>     x3               0.169      0.008    21.31    0.000
+#>     z1               0.141      0.008    18.34    0.000
+#>     z2               0.193      0.009    22.39    0.000
+#>     z3               0.167      0.008    20.52    0.000
+#>     y1               0.061      0.003    17.93    0.000
+#>     y2               0.090      0.004    22.72    0.000
+#>     y3               0.077      0.004    20.69    0.000
+#>     X                1.000      0.016    61.06    0.000
+#>     Z                1.000      0.018    55.21    0.000
+#>     Y                0.404      0.015    26.54    0.000
+

Here you can see the same example using the QML approach.

+
+qml1 <- modsem(m1, oneInt, method = "qml")
+summary(qml1)
+#> Estimating null model
+#> Starting M-step
+#> 
+#> modsem (version 1.0.3):
+#>   Estimator                                         QML
+#>   Optimization method                            NLMINB
+#>   Number of observations                           2000
+#>   Number of iterations                              111
+#>   Loglikelihood                               -17496.22
+#>   Akaike (AIC)                                 35054.43
+#>   Bayesian (BIC)                               35228.06
+#>  
+#> Fit Measures for H0:
+#>   Loglikelihood                                  -17832
+#>   Akaike (AIC)                                 35723.75
+#>   Bayesian (BIC)                               35891.78
+#>   Chi-square                                      17.52
+#>   Degrees of Freedom (Chi-square)                    24
+#>   P-value (Chi-square)                            0.826
+#>   RMSEA                                           0.000
+#>  
+#> Comparative fit to H0 (no interaction effect)
+#>   Loglikelihood change                           335.66
+#>   Difference test (D)                            671.32
+#>   Degrees of freedom (D)                              1
+#>   P-value (D)                                     0.000
+#>  
+#> R-Squared:
+#>   Y                                               0.607
+#> R-Squared Null-Model (H0):
+#>   Y                                               0.395
+#> R-Squared Change:
+#>   Y                                               0.211
+#> 
+#> Parameter Estimates:
+#>   Coefficients                           unstandardized
+#>   Information                                  observed
+#>   Standard errors                              standard
+#>  
+#> Latent Variables:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   X =~ 
+#>     x1               1.000                             
+#>     x2               0.803      0.013    63.96    0.000
+#>     x3               0.914      0.013    67.80    0.000
+#>   Z =~ 
+#>     z1               1.000                             
+#>     z2               0.810      0.012    65.12    0.000
+#>     z3               0.881      0.013    67.62    0.000
+#>   Y =~ 
+#>     y1               1.000                             
+#>     y2               0.798      0.007   107.57    0.000
+#>     y3               0.899      0.008   112.55    0.000
+#> 
+#> Regressions:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   Y ~ 
+#>     X                0.674      0.032    20.94    0.000
+#>     Z                0.566      0.030    18.96    0.000
+#>     X:Z              0.712      0.028    25.45    0.000
+#> 
+#> Intercepts:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     x1               1.023      0.024    42.89    0.000
+#>     x2               1.215      0.020    60.99    0.000
+#>     x3               0.919      0.022    41.48    0.000
+#>     z1               1.012      0.024    41.57    0.000
+#>     z2               1.206      0.020    59.27    0.000
+#>     z3               0.916      0.022    42.06    0.000
+#>     y1               1.038      0.033    31.45    0.000
+#>     y2               1.221      0.027    45.49    0.000
+#>     y3               0.955      0.030    31.86    0.000
+#>     Y                0.000                             
+#>     X                0.000                             
+#>     Z                0.000                             
+#> 
+#> Covariances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   X ~~ 
+#>     Z                0.200      0.024     8.24    0.000
+#> 
+#> Variances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     x1               0.158      0.009    18.14    0.000
+#>     x2               0.162      0.007    23.19    0.000
+#>     x3               0.165      0.008    20.82    0.000
+#>     z1               0.166      0.009    18.34    0.000
+#>     z2               0.159      0.007    22.62    0.000
+#>     z3               0.158      0.008    20.71    0.000
+#>     y1               0.159      0.009    17.98    0.000
+#>     y2               0.154      0.007    22.67    0.000
+#>     y3               0.164      0.008    20.71    0.000
+#>     X                0.983      0.036    26.99    0.000
+#>     Z                1.019      0.038    26.95    0.000
+#>     Y                0.943      0.038    24.87    0.000
+
+
+

A more complicated example +

+

Here you can see an example of a more complicated example using the +model from the theory of planned behaviour (TPB), where there are two +endogenous variables, where there is an interaction between an +endogenous and exogenous variable. When estimating more complicated +models with the LMS-approach, it is recommended that you increase the +number of nodes used for numerical integration. By default the number of +nodes is set to 16, and can be increased using the nodes argument. The +argument has no effect on the QML approach. When there is an interaction +effect between an endogenous and exogenous variable, it is recommended +that you use at least 32 nodes for the LMS-approach. You can also get +robust standard errors by setting robust.se = TRUE in the +modsem() function.

+

Note: If you want the lms-approach to give as similar results as +possible to mplus, you would have to increase the number of nodes (e.g., +nodes = 100).

+
+# ATT = Attitude, 
+# PBC = Perceived Behavioural Control 
+# INT = Intention 
+# SN = Subjective Norms
+# BEH = Behaviour
+tpb <- ' 
+# Outer Model (Based on Hagger et al., 2007)
+  ATT =~ att1 + att2 + att3 + att4 + att5
+  SN =~ sn1 + sn2
+  PBC =~ pbc1 + pbc2 + pbc3
+  INT =~ int1 + int2 + int3
+  BEH =~ b1 + b2
+
+# Inner Model (Based on Steinmetz et al., 2011)
+  INT ~ ATT + SN + PBC
+  BEH ~ INT + PBC 
+  BEH ~ INT:PBC  
+'
+
+lms2 <- modsem(tpb, TPB, method = "lms", nodes = 32)
+summary(lms2)
+#> Estimating null model
+#> EM: Iteration =     1, LogLik =   -26393.22, Change = -26393.223
+#> EM: Iteration =     2, LogLik =   -26393.22, Change =      0.000
+#> 
+#> modsem (version 1.0.3):
+#>   Estimator                                         LMS
+#>   Optimization method                         EM-NLMINB
+#>   Number of observations                           2000
+#>   Number of iterations                               70
+#>   Loglikelihood                               -23439.02
+#>   Akaike (AIC)                                 46986.04
+#>   Bayesian (BIC)                               47288.49
+#>  
+#> Numerical Integration:
+#>   Points of integration (per dim)                    32
+#>   Dimensions                                          1
+#>   Total points of integration                        32
+#>  
+#> Fit Measures for H0:
+#>   Loglikelihood                                  -26393
+#>   Akaike (AIC)                                 52892.45
+#>   Bayesian (BIC)                               53189.29
+#>   Chi-square                                      66.27
+#>   Degrees of Freedom (Chi-square)                    82
+#>   P-value (Chi-square)                            0.897
+#>   RMSEA                                           0.000
+#>  
+#> Comparative fit to H0 (no interaction effect)
+#>   Loglikelihood change                          2954.20
+#>   Difference test (D)                           5908.41
+#>   Degrees of freedom (D)                              1
+#>   P-value (D)                                     0.000
+#>  
+#> R-Squared:
+#>   INT                                             0.364
+#>   BEH                                             0.259
+#> R-Squared Null-Model (H0):
+#>   INT                                             0.367
+#>   BEH                                             0.210
+#> R-Squared Change:
+#>   INT                                            -0.003
+#>   BEH                                             0.049
+#> 
+#> Parameter Estimates:
+#>   Coefficients                           unstandardized
+#>   Information                                  expected
+#>   Standard errors                              standard
+#>  
+#> Latent Variables:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   PBC =~ 
+#>     pbc1             1.000                             
+#>     pbc2             0.914      0.013    68.52    0.000
+#>     pbc3             0.802      0.012    65.02    0.000
+#>   ATT =~ 
+#>     att1             1.000                             
+#>     att2             0.878      0.012    70.81    0.000
+#>     att3             0.789      0.012    65.77    0.000
+#>     att4             0.695      0.011    61.09    0.000
+#>     att5             0.887      0.013    70.26    0.000
+#>   SN =~ 
+#>     sn1              1.000                             
+#>     sn2              0.889      0.017    52.00    0.000
+#>   INT =~ 
+#>     int1             1.000                             
+#>     int2             0.913      0.016    58.38    0.000
+#>     int3             0.807      0.015    55.37    0.000
+#>   BEH =~ 
+#>     b1               1.000                             
+#>     b2               0.959      0.033    29.28    0.000
+#> 
+#> Regressions:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   INT ~ 
+#>     PBC              0.218      0.030     7.36    0.000
+#>     ATT              0.214      0.026     8.19    0.000
+#>     SN               0.176      0.027     6.43    0.000
+#>   BEH ~ 
+#>     PBC              0.233      0.022    10.35    0.000
+#>     INT              0.188      0.025     7.62    0.000
+#>     PBC:INT          0.205      0.019    10.90    0.000
+#> 
+#> Intercepts:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     pbc1             0.990      0.022    45.57    0.000
+#>     pbc2             0.978      0.020    48.28    0.000
+#>     pbc3             0.985      0.018    53.86    0.000
+#>     att1             1.009      0.023    43.19    0.000
+#>     att2             1.002      0.021    48.19    0.000
+#>     att3             1.012      0.019    53.21    0.000
+#>     att4             0.995      0.017    56.95    0.000
+#>     att5             0.988      0.021    46.75    0.000
+#>     sn1              1.001      0.023    42.73    0.000
+#>     sn2              1.006      0.021    48.06    0.000
+#>     int1             1.010      0.021    47.81    0.000
+#>     int2             1.009      0.020    51.14    0.000
+#>     int3             1.002      0.018    56.02    0.000
+#>     b1               0.999      0.021    47.31    0.000
+#>     b2               1.017      0.020    51.50    0.000
+#>     INT              0.000                             
+#>     BEH              0.000                             
+#>     PBC              0.000                             
+#>     ATT              0.000                             
+#>     SN               0.000                             
+#> 
+#> Covariances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   PBC ~~ 
+#>     ATT              0.668      0.021    31.78    0.000
+#>     SN               0.668      0.022    30.52    0.000
+#>   ATT ~~ 
+#>     SN               0.623      0.019    32.90    0.000
+#> 
+#> Variances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     pbc1             0.148      0.008    18.81    0.000
+#>     pbc2             0.159      0.007    21.62    0.000
+#>     pbc3             0.155      0.007    23.64    0.000
+#>     att1             0.167      0.007    23.64    0.000
+#>     att2             0.150      0.006    24.73    0.000
+#>     att3             0.159      0.006    26.68    0.000
+#>     att4             0.162      0.006    27.71    0.000
+#>     att5             0.159      0.006    25.11    0.000
+#>     sn1              0.178      0.015    11.97    0.000
+#>     sn2              0.156      0.012    13.20    0.000
+#>     int1             0.157      0.009    18.25    0.000
+#>     int2             0.160      0.008    20.48    0.000
+#>     int3             0.168      0.007    24.27    0.000
+#>     b1               0.185      0.020     9.46    0.000
+#>     b2               0.136      0.018     7.71    0.000
+#>     PBC              0.947      0.017    55.23    0.000
+#>     ATT              0.992      0.014    69.87    0.000
+#>     SN               0.981      0.015    64.37    0.000
+#>     INT              0.491      0.020    24.97    0.000
+#>     BEH              0.456      0.023    19.46    0.000
+
+qml2 <- modsem(tpb, TPB, method = "qml")
+summary(qml2, standardized = TRUE) # standardized estimates
+#> Estimating null model
+#> Starting M-step
+#> 
+#> modsem (version 1.0.3):
+#>   Estimator                                         QML
+#>   Optimization method                            NLMINB
+#>   Number of observations                           2000
+#>   Number of iterations                               73
+#>   Loglikelihood                               -26326.25
+#>   Akaike (AIC)                                  52760.5
+#>   Bayesian (BIC)                               53062.95
+#>  
+#> Fit Measures for H0:
+#>   Loglikelihood                                  -26393
+#>   Akaike (AIC)                                 52892.45
+#>   Bayesian (BIC)                               53189.29
+#>   Chi-square                                      66.27
+#>   Degrees of Freedom (Chi-square)                    82
+#>   P-value (Chi-square)                            0.897
+#>   RMSEA                                           0.000
+#>  
+#> Comparative fit to H0 (no interaction effect)
+#>   Loglikelihood change                            66.97
+#>   Difference test (D)                            133.95
+#>   Degrees of freedom (D)                              1
+#>   P-value (D)                                     0.000
+#>  
+#> R-Squared:
+#>   INT                                             0.366
+#>   BEH                                             0.263
+#> R-Squared Null-Model (H0):
+#>   INT                                             0.367
+#>   BEH                                             0.210
+#> R-Squared Change:
+#>   INT                                             0.000
+#>   BEH                                             0.053
+#> 
+#> Parameter Estimates:
+#>   Coefficients                             standardized
+#>   Information                                  observed
+#>   Standard errors                              standard
+#>  
+#> Latent Variables:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   PBC =~ 
+#>     pbc1             0.933                             
+#>     pbc2             0.913      0.013    69.47    0.000
+#>     pbc3             0.894      0.014    66.10    0.000
+#>   ATT =~ 
+#>     att1             0.925                             
+#>     att2             0.915      0.013    71.56    0.000
+#>     att3             0.892      0.013    66.38    0.000
+#>     att4             0.865      0.014    61.00    0.000
+#>     att5             0.912      0.013    70.85    0.000
+#>   SN =~ 
+#>     sn1              0.921                             
+#>     sn2              0.913      0.017    52.61    0.000
+#>   INT =~ 
+#>     int1             0.912                             
+#>     int2             0.895      0.015    59.05    0.000
+#>     int3             0.866      0.016    55.73    0.000
+#>   BEH =~ 
+#>     b1               0.877                             
+#>     b2               0.900      0.028    31.71    0.000
+#> 
+#> Regressions:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   INT ~ 
+#>     PBC              0.243      0.033     7.35    0.000
+#>     ATT              0.242      0.030     8.16    0.000
+#>     SN               0.199      0.031     6.37    0.000
+#>   BEH ~ 
+#>     PBC              0.289      0.028    10.37    0.000
+#>     INT              0.212      0.028     7.69    0.000
+#>     PBC:INT          0.227      0.020    11.32    0.000
+#> 
+#> Covariances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   PBC ~~ 
+#>     ATT              0.692      0.030    23.45    0.000
+#>     SN               0.695      0.030    23.08    0.000
+#>   ATT ~~ 
+#>     SN               0.634      0.029    21.70    0.000
+#> 
+#> Variances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     pbc1             0.130      0.007    18.39    0.000
+#>     pbc2             0.166      0.008    21.43    0.000
+#>     pbc3             0.201      0.008    23.89    0.000
+#>     att1             0.144      0.006    23.53    0.000
+#>     att2             0.164      0.007    24.71    0.000
+#>     att3             0.204      0.008    26.38    0.000
+#>     att4             0.252      0.009    27.65    0.000
+#>     att5             0.168      0.007    24.93    0.000
+#>     sn1              0.153      0.013    12.09    0.000
+#>     sn2              0.167      0.013    13.26    0.000
+#>     int1             0.168      0.009    18.11    0.000
+#>     int2             0.199      0.010    20.41    0.000
+#>     int3             0.249      0.011    23.55    0.000
+#>     b1               0.231      0.023    10.12    0.000
+#>     b2               0.191      0.024     8.10    0.000
+#>     PBC              1.000      0.037    27.07    0.000
+#>     ATT              1.000      0.037    26.93    0.000
+#>     SN               1.000      0.040    25.22    0.000
+#>     INT              0.634      0.026    24.64    0.000
+#>     BEH              0.737      0.037    20.17    0.000
+
+
+
+
+ + + +
+

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methods

+ + + Source: vignettes/methods.Rmd +
methods.Rmd
+
+ + + + +

There are a number of approaches for estimating interaction effects +in SEM. In modsem(), the method = “method” argument allows you to choose +which to use.

+
    +
  • +"ca" = constrained approach (Algina & Moulder, +2001)
    • +
  • +"uca" = unconstrained approach (Marsh, 2004)
    • +
  • +"rca" = residual centering approach (Little et al., +2006) +
      +
    • default
      • +
    +
    • +
  • +"dblcent"= double centering approach (Marsh., +2013)
    • +
  • +"pind" = basic product indicator approach (not +recommended)
    • +
  • +"lms" = The Latent Moderated Structural equations +approach +
      +
    • note: there can not be an interaction between two endogenous +variables.
      • +
    +
    • +
  • +"qml" = The Quasi Maximum Likelihood approach. +
      +
    • note: can only be done if you have a single endogenous (dependent) +variable.
      • +
    +
    • +
  • +"mplus" +
      +
    • estimates model through Mplus, if it is installed
      • +
    +
    • +
+
+
+m1 <- '
+# Outer Model
+X =~ x1 + x2 + x3
+Y =~ y1 + y2 + y3
+Z =~ z1 + z2 + z3
+
+# Inner model
+Y ~ X + Z + X:Z 
+'
+
+modsem(m1, data = oneInt, method = "ca")
+modsem(m1, data = oneInt, method = "uca")
+modsem(m1, data = oneInt, method = "rca")
+modsem(m1, data = oneInt, method = "dblcent")
+modsem(m1, data = oneInt, method = "mplus")
+modsem(m1, data = oneInt, method = "lms")
+modsem(m1, data = oneInt, method = "qml")
+
+
+ + + +
+

Developed by Kjell Solem Slupphaug.

+
+ +
+

Site built with pkgdown 2.1.0.

+
+ +
+
+ + + + + + + diff --git a/articles/methods_files/accessible-code-block-0.0.1/empty-anchor.js b/articles/methods_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 0000000..ca349fd --- /dev/null +++ b/articles/methods_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/articles/modsem.html b/articles/modsem.html new file mode 100644 index 0000000..dd11837 --- /dev/null +++ b/articles/modsem.html @@ -0,0 +1,953 @@ + + + + + + + +modsem • modsem + + + + + + + + Skip to contents + + +
+ + + + +
+
+ +

modsem

+ + + Source: vignettes/modsem.Rmd +
modsem.Rmd
+
+ + + + +
+

The Basic Syntax +

+

modsem basically introduces a new feature to the lavaan-syntax – the +semicolon operator (“:”). The semicolon operator works the same way as +in the lm()-function. In order to specify an interaction effect between +two variables, you join them by Var1:Var2, Models can either be +estimated using the one of the product indicator approaches (“ca”, +“rca”, “dblcent”, “pind”) or by using the latent moderated structural +equations approach (“lms”), or the quasi maximum likelihood approach +(“qml”). The product indicator approaches are estimated via lavaan, +whilst the lms and qml approaches are estimated via modsem itself.

+
+

A Simple Example +

+

Here we can see a simple example of how to specify an interaction +effect between two latent variables in lavaan.

+
+m1 <- '
+  # Outer Model
+  X =~ x1 + x2 +x3
+  Y =~ y1 + y2 + y3
+  Z =~ z1 + z2 + z3
+  
+  # Inner model
+  Y ~ X + Z + X:Z 
+'
+
+est1 <- modsem(m1, oneInt)
+summary(est1)
+#> modsem: 
+#> Method = dblcent
+#> lavaan 0.6-18 ended normally after 159 iterations
+#> 
+#>   Estimator                                         ML
+#>   Optimization method                           NLMINB
+#>   Number of model parameters                        60
+#> 
+#>   Number of observations                          2000
+#> 
+#> Model Test User Model:
+#>                                                       
+#>   Test statistic                               122.924
+#>   Degrees of freedom                               111
+#>   P-value (Chi-square)                           0.207
+#> 
+#> Parameter Estimates:
+#> 
+#>   Standard errors                             Standard
+#>   Information                                 Expected
+#>   Information saturated (h1) model          Structured
+#> 
+#> Latent Variables:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>   X =~                                                
+#>     x1                1.000                           
+#>     x2                0.804    0.013   63.612    0.000
+#>     x3                0.916    0.014   67.144    0.000
+#>   Y =~                                                
+#>     y1                1.000                           
+#>     y2                0.798    0.007  107.428    0.000
+#>     y3                0.899    0.008  112.453    0.000
+#>   Z =~                                                
+#>     z1                1.000                           
+#>     z2                0.812    0.013   64.763    0.000
+#>     z3                0.882    0.013   67.014    0.000
+#>   XZ =~                                               
+#>     x1z1              1.000                           
+#>     x2z1              0.805    0.013   60.636    0.000
+#>     x3z1              0.877    0.014   62.680    0.000
+#>     x1z2              0.793    0.013   59.343    0.000
+#>     x2z2              0.646    0.015   43.672    0.000
+#>     x3z2              0.706    0.016   44.292    0.000
+#>     x1z3              0.887    0.014   63.700    0.000
+#>     x2z3              0.716    0.016   45.645    0.000
+#>     x3z3              0.781    0.017   45.339    0.000
+#> 
+#> Regressions:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>   Y ~                                                 
+#>     X                 0.675    0.027   25.379    0.000
+#>     Z                 0.561    0.026   21.606    0.000
+#>     XZ                0.702    0.027   26.360    0.000
+#> 
+#> Covariances:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>  .x1z1 ~~                                             
+#>    .x2z2              0.000                           
+#>    .x2z3              0.000                           
+#>    .x3z2              0.000                           
+#>    .x3z3              0.000                           
+#>  .x2z1 ~~                                             
+#>    .x1z2              0.000                           
+#>  .x1z2 ~~                                             
+#>    .x2z3              0.000                           
+#>  .x3z1 ~~                                             
+#>    .x1z2              0.000                           
+#>  .x1z2 ~~                                             
+#>    .x3z3              0.000                           
+#>  .x2z1 ~~                                             
+#>    .x1z3              0.000                           
+#>  .x2z2 ~~                                             
+#>    .x1z3              0.000                           
+#>  .x3z1 ~~                                             
+#>    .x1z3              0.000                           
+#>  .x3z2 ~~                                             
+#>    .x1z3              0.000                           
+#>  .x2z1 ~~                                             
+#>    .x3z2              0.000                           
+#>    .x3z3              0.000                           
+#>  .x3z1 ~~                                             
+#>    .x2z2              0.000                           
+#>  .x2z2 ~~                                             
+#>    .x3z3              0.000                           
+#>  .x3z1 ~~                                             
+#>    .x2z3              0.000                           
+#>  .x3z2 ~~                                             
+#>    .x2z3              0.000                           
+#>  .x1z1 ~~                                             
+#>    .x1z2              0.115    0.008   14.802    0.000
+#>    .x1z3              0.114    0.008   13.947    0.000
+#>    .x2z1              0.125    0.008   16.095    0.000
+#>    .x3z1              0.140    0.009   16.135    0.000
+#>  .x1z2 ~~                                             
+#>    .x1z3              0.103    0.007   14.675    0.000
+#>    .x2z2              0.128    0.006   20.850    0.000
+#>    .x3z2              0.146    0.007   21.243    0.000
+#>  .x1z3 ~~                                             
+#>    .x2z3              0.116    0.007   17.818    0.000
+#>    .x3z3              0.135    0.007   18.335    0.000
+#>  .x2z1 ~~                                             
+#>    .x2z2              0.135    0.006   20.905    0.000
+#>    .x2z3              0.145    0.007   21.145    0.000
+#>    .x3z1              0.114    0.007   16.058    0.000
+#>  .x2z2 ~~                                             
+#>    .x2z3              0.117    0.006   20.419    0.000
+#>    .x3z2              0.116    0.006   20.586    0.000
+#>  .x2z3 ~~                                             
+#>    .x3z3              0.109    0.006   18.059    0.000
+#>  .x3z1 ~~                                             
+#>    .x3z2              0.138    0.007   19.331    0.000
+#>    .x3z3              0.158    0.008   20.269    0.000
+#>  .x3z2 ~~                                             
+#>    .x3z3              0.131    0.007   19.958    0.000
+#>   X ~~                                                
+#>     Z                 0.201    0.024    8.271    0.000
+#>     XZ                0.016    0.025    0.628    0.530
+#>   Z ~~                                                
+#>     XZ                0.062    0.025    2.449    0.014
+#> 
+#> Variances:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>    .x1                0.160    0.009   17.871    0.000
+#>    .x2                0.162    0.007   22.969    0.000
+#>    .x3                0.163    0.008   20.161    0.000
+#>    .y1                0.159    0.009   17.896    0.000
+#>    .y2                0.154    0.007   22.640    0.000
+#>    .y3                0.164    0.008   20.698    0.000
+#>    .z1                0.168    0.009   18.143    0.000
+#>    .z2                0.158    0.007   22.264    0.000
+#>    .z3                0.158    0.008   20.389    0.000
+#>    .x1z1              0.311    0.014   22.227    0.000
+#>    .x2z1              0.292    0.011   27.287    0.000
+#>    .x3z1              0.327    0.012   26.275    0.000
+#>    .x1z2              0.290    0.011   26.910    0.000
+#>    .x2z2              0.239    0.008   29.770    0.000
+#>    .x3z2              0.270    0.009   29.117    0.000
+#>    .x1z3              0.272    0.012   23.586    0.000
+#>    .x2z3              0.245    0.009   27.979    0.000
+#>    .x3z3              0.297    0.011   28.154    0.000
+#>     X                 0.981    0.036   26.895    0.000
+#>    .Y                 0.990    0.038   25.926    0.000
+#>     Z                 1.016    0.038   26.856    0.000
+#>     XZ                1.045    0.044   24.004    0.000
+

By default the model is estimated using the “dblcent” method. If you +want to use another method, but the method can be changed using the +method argument.

+
+est1 <- modsem(m1, oneInt, method = "lms")
+summary(est1)
+#> Estimating null model
+#> EM: Iteration =     1, LogLik =   -17831.87, Change = -17831.875
+#> EM: Iteration =     2, LogLik =   -17831.87, Change =      0.000
+#> 
+#> modsem (version 1.0.3):
+#>   Estimator                                         LMS
+#>   Optimization method                         EM-NLMINB
+#>   Number of observations                           2000
+#>   Number of iterations                               92
+#>   Loglikelihood                               -14687.85
+#>   Akaike (AIC)                                 29437.71
+#>   Bayesian (BIC)                               29611.34
+#>  
+#> Numerical Integration:
+#>   Points of integration (per dim)                    24
+#>   Dimensions                                          1
+#>   Total points of integration                        24
+#>  
+#> Fit Measures for H0:
+#>   Loglikelihood                                  -17832
+#>   Akaike (AIC)                                 35723.75
+#>   Bayesian (BIC)                               35891.78
+#>   Chi-square                                      17.52
+#>   Degrees of Freedom (Chi-square)                    24
+#>   P-value (Chi-square)                            0.826
+#>   RMSEA                                           0.000
+#>  
+#> Comparative fit to H0 (no interaction effect)
+#>   Loglikelihood change                          3144.02
+#>   Difference test (D)                           6288.04
+#>   Degrees of freedom (D)                              1
+#>   P-value (D)                                     0.000
+#>  
+#> R-Squared:
+#>   Y                                               0.596
+#> R-Squared Null-Model (H0):
+#>   Y                                               0.395
+#> R-Squared Change:
+#>   Y                                               0.201
+#> 
+#> Parameter Estimates:
+#>   Coefficients                           unstandardized
+#>   Information                                  expected
+#>   Standard errors                              standard
+#>  
+#> Latent Variables:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   X =~ 
+#>     x1               1.000                             
+#>     x2               0.804      0.012    64.39    0.000
+#>     x3               0.915      0.014    67.69    0.000
+#>   Z =~ 
+#>     z1               1.000                             
+#>     z2               0.810      0.013    64.59    0.000
+#>     z3               0.881      0.013    67.87    0.000
+#>   Y =~ 
+#>     y1               1.000                             
+#>     y2               0.799      0.008   105.92    0.000
+#>     y3               0.899      0.008   111.95    0.000
+#> 
+#> Regressions:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   Y ~ 
+#>     X                0.676      0.031    21.79    0.000
+#>     Z                0.572      0.028    20.16    0.000
+#>     X:Z              0.712      0.027    26.28    0.000
+#> 
+#> Intercepts:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     x1               1.025      0.019    52.75    0.000
+#>     x2               1.218      0.017    73.47    0.000
+#>     x3               0.922      0.018    50.64    0.000
+#>     z1               1.016      0.024    41.94    0.000
+#>     z2               1.209      0.020    59.65    0.000
+#>     z3               0.920      0.022    42.33    0.000
+#>     y1               1.046      0.031    33.47    0.000
+#>     y2               1.227      0.025    48.20    0.000
+#>     y3               0.962      0.028    33.81    0.000
+#>     Y                0.000                             
+#>     X                0.000                             
+#>     Z                0.000                             
+#> 
+#> Covariances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   X ~~ 
+#>     Z                0.198      0.023     8.43    0.000
+#> 
+#> Variances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     x1               0.160      0.008    19.27    0.000
+#>     x2               0.163      0.007    23.86    0.000
+#>     x3               0.165      0.008    21.31    0.000
+#>     z1               0.166      0.009    18.34    0.000
+#>     z2               0.160      0.007    22.39    0.000
+#>     z3               0.158      0.008    20.52    0.000
+#>     y1               0.160      0.009    17.93    0.000
+#>     y2               0.154      0.007    22.72    0.000
+#>     y3               0.163      0.008    20.69    0.000
+#>     X                0.972      0.016    61.06    0.000
+#>     Z                1.017      0.018    55.21    0.000
+#>     Y                0.984      0.037    26.54    0.000
+
+
+

Interactions Between two Observed Variables +

+

modsem does not only allow you to estimate interactions between +latent variables, but also interactions between observed variables. Here +we first run a regression with only observed variables, where there is +an interaction between x1 and z2, and then run an equivalent model using +modsem().

+

Regression

+
+reg1 <- lm(y1 ~ x1*z1, oneInt)
+summary(reg1)
+#> 
+#> Call:
+#> lm(formula = y1 ~ x1 * z1, data = oneInt)
+#> 
+#> Residuals:
+#>     Min      1Q  Median      3Q     Max 
+#> -3.7155 -0.8087 -0.0367  0.8078  4.6531 
+#> 
+#> Coefficients:
+#>             Estimate Std. Error t value Pr(>|t|)    
+#> (Intercept)  0.51422    0.04618  11.135   <2e-16 ***
+#> x1           0.05477    0.03387   1.617   0.1060    
+#> z1          -0.06575    0.03461  -1.900   0.0576 .  
+#> x1:z1        0.54361    0.02272  23.926   <2e-16 ***
+#> ---
+#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+#> 
+#> Residual standard error: 1.184 on 1996 degrees of freedom
+#> Multiple R-squared:  0.4714, Adjusted R-squared:  0.4706 
+#> F-statistic: 593.3 on 3 and 1996 DF,  p-value: < 2.2e-16
+

Using modsem() In general, when you have +interactions between observed variables it is recommended that you use +method = “pind”. Interaction effects with observed variables is not +supported by the LMS- and QML-approach. In certain circumstances, you +can define a latent variabale with a single indicator to estimate the +interaction effect between two observed variables, in the LMS and QML +approach, but it is generally not recommended.

+
+# Here we use "pind" as the method (see chapter 3)
+est2 <- modsem('y1 ~ x1 + z1 + x1:z1', data = oneInt, method = "pind")
+summary(est2)
+#> modsem: 
+#> Method = pind
+#> lavaan 0.6-18 ended normally after 1 iteration
+#> 
+#>   Estimator                                         ML
+#>   Optimization method                           NLMINB
+#>   Number of model parameters                         4
+#> 
+#>   Number of observations                          2000
+#> 
+#> Model Test User Model:
+#>                                                       
+#>   Test statistic                                 0.000
+#>   Degrees of freedom                                 0
+#> 
+#> Parameter Estimates:
+#> 
+#>   Standard errors                             Standard
+#>   Information                                 Expected
+#>   Information saturated (h1) model          Structured
+#> 
+#> Regressions:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>   y1 ~                                                
+#>     x1                0.055    0.034    1.619    0.105
+#>     z1               -0.066    0.035   -1.902    0.057
+#>     x1z1              0.544    0.023   23.950    0.000
+#> 
+#> Variances:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>    .y1                1.399    0.044   31.623    0.000
+
+
+

Interactions between Latent and Observed Variables +

+

modsem also allows you to estimate interaction effects between latent +and observed variables. To do so, you just join a latent and an observed +variable by a colon, e.g., ‘latent:observer’. As with interactions +between observed variables, it is generally recommended that you use +method = “pind” for estimating the effect between observed x latent

+
+m3 <- '
+  # Outer Model
+  X =~ x1 + x2 +x3
+  Y =~ y1 + y2 + y3
+  
+  # Inner model
+  Y ~ X + z1 + X:z1 
+'
+
+est3 <- modsem(m3, oneInt, method = "pind")
+summary(est3)
+#> modsem: 
+#> Method = pind
+#> lavaan 0.6-18 ended normally after 45 iterations
+#> 
+#>   Estimator                                         ML
+#>   Optimization method                           NLMINB
+#>   Number of model parameters                        22
+#> 
+#>   Number of observations                          2000
+#> 
+#> Model Test User Model:
+#>                                                       
+#>   Test statistic                              4468.171
+#>   Degrees of freedom                                32
+#>   P-value (Chi-square)                           0.000
+#> 
+#> Parameter Estimates:
+#> 
+#>   Standard errors                             Standard
+#>   Information                                 Expected
+#>   Information saturated (h1) model          Structured
+#> 
+#> Latent Variables:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>   X =~                                                
+#>     x1                1.000                           
+#>     x2                0.803    0.013   63.697    0.000
+#>     x3                0.915    0.014   67.548    0.000
+#>   Y =~                                                
+#>     y1                1.000                           
+#>     y2                0.798    0.007  115.375    0.000
+#>     y3                0.899    0.007  120.783    0.000
+#>   Xz1 =~                                              
+#>     x1z1              1.000                           
+#>     x2z1              0.947    0.010   96.034    0.000
+#>     x3z1              0.902    0.009   99.512    0.000
+#> 
+#> Regressions:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>   Y ~                                                 
+#>     X                 0.021    0.034    0.614    0.540
+#>     z1               -0.185    0.023   -8.096    0.000
+#>     Xz1               0.646    0.017   37.126    0.000
+#> 
+#> Covariances:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>   X ~~                                                
+#>     Xz1               1.243    0.055   22.523    0.000
+#> 
+#> Variances:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>    .x1                0.158    0.009   17.976    0.000
+#>    .x2                0.164    0.007   23.216    0.000
+#>    .x3                0.162    0.008   20.325    0.000
+#>    .y1                0.158    0.009   17.819    0.000
+#>    .y2                0.154    0.007   22.651    0.000
+#>    .y3                0.164    0.008   20.744    0.000
+#>    .x1z1              0.315    0.017   18.328    0.000
+#>    .x2z1              0.428    0.019   22.853    0.000
+#>    .x3z1              0.337    0.016   21.430    0.000
+#>     X                 0.982    0.036   26.947    0.000
+#>    .Y                 1.112    0.040   27.710    0.000
+#>     Xz1               3.965    0.136   29.217    0.000
+
+
+

Quadratic Effects +

+

In essence, quadratic effects are just a special case of interaction +effects. Thus modsem can also be used to estimate quadratic effects.

+
+
+m4 <- '
+# Outer Model
+X =~ x1 + x2 + x3
+Y =~ y1 + y2 + y3
+Z =~ z1 + z2 + z3
+
+# Inner model
+Y ~ X + Z + Z:X + X:X
+'
+
+est4 <- modsem(m4, oneInt, "qml")
+summary(est4)
+#> Estimating null model
+#> Starting M-step
+#> 
+#> modsem (version 1.0.3):
+#>   Estimator                                         QML
+#>   Optimization method                            NLMINB
+#>   Number of observations                           2000
+#>   Number of iterations                              123
+#>   Loglikelihood                                -17496.2
+#>   Akaike (AIC)                                  35056.4
+#>   Bayesian (BIC)                               35235.63
+#>  
+#> Fit Measures for H0:
+#>   Loglikelihood                                  -17832
+#>   Akaike (AIC)                                 35723.75
+#>   Bayesian (BIC)                               35891.78
+#>   Chi-square                                      17.52
+#>   Degrees of Freedom (Chi-square)                    24
+#>   P-value (Chi-square)                            0.826
+#>   RMSEA                                           0.000
+#>  
+#> Comparative fit to H0 (no interaction effect)
+#>   Loglikelihood change                           335.68
+#>   Difference test (D)                            671.35
+#>   Degrees of freedom (D)                              2
+#>   P-value (D)                                     0.000
+#>  
+#> R-Squared:
+#>   Y                                               0.607
+#> R-Squared Null-Model (H0):
+#>   Y                                               0.395
+#> R-Squared Change:
+#>   Y                                               0.212
+#> 
+#> Parameter Estimates:
+#>   Coefficients                           unstandardized
+#>   Information                                  observed
+#>   Standard errors                              standard
+#>  
+#> Latent Variables:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   X =~ 
+#>     x1               1.000                             
+#>     x2               0.803      0.013   63.961    0.000
+#>     x3               0.914      0.013   67.797    0.000
+#>   Z =~ 
+#>     z1               1.000                             
+#>     z2               0.810      0.012   65.124    0.000
+#>     z3               0.881      0.013   67.621    0.000
+#>   Y =~ 
+#>     y1               1.000                             
+#>     y2               0.798      0.007  107.567    0.000
+#>     y3               0.899      0.008  112.542    0.000
+#> 
+#> Regressions:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   Y ~ 
+#>     X                0.674      0.032   20.888    0.000
+#>     Z                0.566      0.030   18.948    0.000
+#>     X:X             -0.005      0.023   -0.207    0.836
+#>     X:Z              0.713      0.029   24.554    0.000
+#> 
+#> Intercepts:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     x1               1.023      0.024   42.894    0.000
+#>     x2               1.216      0.020   60.996    0.000
+#>     x3               0.919      0.022   41.484    0.000
+#>     z1               1.012      0.024   41.576    0.000
+#>     z2               1.206      0.020   59.271    0.000
+#>     z3               0.916      0.022   42.063    0.000
+#>     y1               1.042      0.038   27.684    0.000
+#>     y2               1.224      0.030   40.159    0.000
+#>     y3               0.958      0.034   28.101    0.000
+#>     Y                0.000                             
+#>     X                0.000                             
+#>     Z                0.000                             
+#> 
+#> Covariances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   X ~~ 
+#>     Z                0.200      0.024    8.239    0.000
+#> 
+#> Variances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     x1               0.158      0.009   18.145    0.000
+#>     x2               0.162      0.007   23.188    0.000
+#>     x3               0.165      0.008   20.821    0.000
+#>     z1               0.166      0.009   18.341    0.000
+#>     z2               0.159      0.007   22.622    0.000
+#>     z3               0.158      0.008   20.714    0.000
+#>     y1               0.159      0.009   17.975    0.000
+#>     y2               0.154      0.007   22.670    0.000
+#>     y3               0.164      0.008   20.711    0.000
+#>     X                0.983      0.036   26.994    0.000
+#>     Z                1.019      0.038   26.951    0.000
+#>     Y                0.943      0.038   24.820    0.000
+
+
+

More Complicated Examples +

+

Here we can see a more complicated example using the model for the +theory of planned behaviour.

+
+
+tpb <- ' 
+# Outer Model (Based on Hagger et al., 2007)
+  ATT =~ att1 + att2 + att3 + att4 + att5
+  SN =~ sn1 + sn2
+  PBC =~ pbc1 + pbc2 + pbc3
+  INT =~ int1 + int2 + int3
+  BEH =~ b1 + b2
+
+# Inner Model (Based on Steinmetz et al., 2011)
+  INT ~ ATT + SN + PBC
+  BEH ~ INT + PBC + INT:PBC  
+'
+# the double centering apporach
+est_tpb <- modsem(tpb, TPB)
+
+# using the lms approach
+est_tpb_lms <- modsem(tpb, TPB, method = "lms")
+#> Warning: It is recommended that you have at least 32 nodes for interaction
+#> effects between exogenous and endogenous variables in the lms approach 'nodes =
+#> 24'
+summary(est_tpb_lms)
+#> Estimating null model
+#> EM: Iteration =     1, LogLik =   -26393.22, Change = -26393.223
+#> EM: Iteration =     2, LogLik =   -26393.22, Change =      0.000
+#> 
+#> modsem (version 1.0.3):
+#>   Estimator                                         LMS
+#>   Optimization method                         EM-NLMINB
+#>   Number of observations                           2000
+#>   Number of iterations                              103
+#>   Loglikelihood                               -23463.37
+#>   Akaike (AIC)                                 47034.74
+#>   Bayesian (BIC)                               47337.19
+#>  
+#> Numerical Integration:
+#>   Points of integration (per dim)                    24
+#>   Dimensions                                          1
+#>   Total points of integration                        24
+#>  
+#> Fit Measures for H0:
+#>   Loglikelihood                                  -26393
+#>   Akaike (AIC)                                 52892.45
+#>   Bayesian (BIC)                               53189.29
+#>   Chi-square                                      66.27
+#>   Degrees of Freedom (Chi-square)                    82
+#>   P-value (Chi-square)                            0.897
+#>   RMSEA                                           0.000
+#>  
+#> Comparative fit to H0 (no interaction effect)
+#>   Loglikelihood change                          2929.85
+#>   Difference test (D)                           5859.70
+#>   Degrees of freedom (D)                              1
+#>   P-value (D)                                     0.000
+#>  
+#> R-Squared:
+#>   INT                                             0.361
+#>   BEH                                             0.248
+#> R-Squared Null-Model (H0):
+#>   INT                                             0.367
+#>   BEH                                             0.210
+#> R-Squared Change:
+#>   INT                                            -0.006
+#>   BEH                                             0.038
+#> 
+#> Parameter Estimates:
+#>   Coefficients                           unstandardized
+#>   Information                                  expected
+#>   Standard errors                              standard
+#>  
+#> Latent Variables:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   PBC =~ 
+#>     pbc1             1.000                             
+#>     pbc2             0.911      0.014    67.47    0.000
+#>     pbc3             0.802      0.012    65.29    0.000
+#>   ATT =~ 
+#>     att1             1.000                             
+#>     att2             0.877      0.012    71.30    0.000
+#>     att3             0.789      0.012    65.67    0.000
+#>     att4             0.695      0.011    60.83    0.000
+#>     att5             0.887      0.013    70.47    0.000
+#>   SN =~ 
+#>     sn1              1.000                             
+#>     sn2              0.889      0.017    51.65    0.000
+#>   INT =~ 
+#>     int1             1.000                             
+#>     int2             0.913      0.016    58.82    0.000
+#>     int3             0.807      0.015    55.32    0.000
+#>   BEH =~ 
+#>     b1               1.000                             
+#>     b2               0.961      0.033    29.34    0.000
+#> 
+#> Regressions:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   INT ~ 
+#>     PBC              0.217      0.030     7.30    0.000
+#>     ATT              0.213      0.026     8.29    0.000
+#>     SN               0.177      0.028     6.35    0.000
+#>   BEH ~ 
+#>     PBC              0.228      0.022    10.16    0.000
+#>     INT              0.182      0.025     7.38    0.000
+#>     PBC:INT          0.204      0.019    10.79    0.000
+#> 
+#> Intercepts:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     pbc1             0.959      0.018    52.11    0.000
+#>     pbc2             0.950      0.017    54.90    0.000
+#>     pbc3             0.960      0.016    61.08    0.000
+#>     att1             0.987      0.022    45.68    0.000
+#>     att2             0.983      0.019    51.10    0.000
+#>     att3             0.995      0.018    56.12    0.000
+#>     att4             0.980      0.016    60.13    0.000
+#>     att5             0.969      0.019    49.85    0.000
+#>     sn1              0.979      0.022    44.67    0.000
+#>     sn2              0.987      0.020    50.00    0.000
+#>     int1             0.995      0.020    48.93    0.000
+#>     int2             0.995      0.019    52.40    0.000
+#>     int3             0.990      0.017    56.69    0.000
+#>     b1               0.989      0.021    47.79    0.000
+#>     b2               1.008      0.019    51.98    0.000
+#>     INT              0.000                             
+#>     BEH              0.000                             
+#>     PBC              0.000                             
+#>     ATT              0.000                             
+#>     SN               0.000                             
+#> 
+#> Covariances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   PBC ~~ 
+#>     ATT              0.658      0.020    32.58    0.000
+#>     SN               0.657      0.021    31.11    0.000
+#>   ATT ~~ 
+#>     SN               0.616      0.019    32.97    0.000
+#> 
+#> Variances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     pbc1             0.147      0.008    19.28    0.000
+#>     pbc2             0.164      0.007    22.15    0.000
+#>     pbc3             0.154      0.006    24.09    0.000
+#>     att1             0.167      0.007    23.37    0.000
+#>     att2             0.150      0.006    24.30    0.000
+#>     att3             0.159      0.006    26.67    0.000
+#>     att4             0.163      0.006    27.65    0.000
+#>     att5             0.159      0.006    24.77    0.000
+#>     sn1              0.178      0.015    12.09    0.000
+#>     sn2              0.156      0.012    12.97    0.000
+#>     int1             0.157      0.009    18.06    0.000
+#>     int2             0.160      0.008    20.12    0.000
+#>     int3             0.168      0.007    23.32    0.000
+#>     b1               0.186      0.020     9.51    0.000
+#>     b2               0.135      0.018     7.62    0.000
+#>     PBC              0.933      0.015    60.78    0.000
+#>     ATT              0.985      0.014    70.25    0.000
+#>     SN               0.974      0.015    63.87    0.000
+#>     INT              0.491      0.020    24.34    0.000
+#>     BEH              0.456      0.023    19.60    0.000
+

Here is an example included two quadratic- and one interaction +effect, using the included dataset jordan. The dataset is +subset of the PISA 2006 dataset.

+
+m2 <- '
+ENJ =~ enjoy1 + enjoy2 + enjoy3 + enjoy4 + enjoy5
+CAREER =~ career1 + career2 + career3 + career4
+SC =~ academic1 + academic2 + academic3 + academic4 + academic5 + academic6
+CAREER ~ ENJ + SC + ENJ:ENJ + SC:SC + ENJ:SC
+'
+
+est_jordan <- modsem(m2, data = jordan)
+est_jordan_qml <- modsem(m2, data = jordan, method = "qml")
+summary(est_jordan_qml)
+#> Estimating null model
+#> Starting M-step
+#> 
+#> modsem (version 1.0.3):
+#>   Estimator                                         QML
+#>   Optimization method                            NLMINB
+#>   Number of observations                           6038
+#>   Number of iterations                              101
+#>   Loglikelihood                              -110520.22
+#>   Akaike (AIC)                                221142.45
+#>   Bayesian (BIC)                              221484.44
+#>  
+#> Fit Measures for H0:
+#>   Loglikelihood                                 -110521
+#>   Akaike (AIC)                                221138.58
+#>   Bayesian (BIC)                              221460.46
+#>   Chi-square                                    1016.34
+#>   Degrees of Freedom (Chi-square)                    87
+#>   P-value (Chi-square)                            0.000
+#>   RMSEA                                           0.005
+#>  
+#> Comparative fit to H0 (no interaction effect)
+#>   Loglikelihood change                             1.07
+#>   Difference test (D)                              2.13
+#>   Degrees of freedom (D)                              3
+#>   P-value (D)                                     0.546
+#>  
+#> R-Squared:
+#>   CAREER                                          0.512
+#> R-Squared Null-Model (H0):
+#>   CAREER                                          0.510
+#> R-Squared Change:
+#>   CAREER                                          0.002
+#> 
+#> Parameter Estimates:
+#>   Coefficients                           unstandardized
+#>   Information                                  observed
+#>   Standard errors                              standard
+#>  
+#> Latent Variables:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   ENJ =~ 
+#>     enjoy1           1.000                             
+#>     enjoy2           1.002      0.020   50.587    0.000
+#>     enjoy3           0.894      0.020   43.669    0.000
+#>     enjoy4           0.999      0.021   48.227    0.000
+#>     enjoy5           1.047      0.021   50.400    0.000
+#>   SC =~ 
+#>     academic1        1.000                             
+#>     academic2        1.104      0.028   38.946    0.000
+#>     academic3        1.235      0.030   41.720    0.000
+#>     academic4        1.254      0.030   41.828    0.000
+#>     academic5        1.113      0.029   38.647    0.000
+#>     academic6        1.198      0.030   40.356    0.000
+#>   CAREER =~ 
+#>     career1          1.000                             
+#>     career2          1.040      0.016   65.180    0.000
+#>     career3          0.952      0.016   57.838    0.000
+#>     career4          0.818      0.017   48.358    0.000
+#> 
+#> Regressions:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   CAREER ~ 
+#>     ENJ              0.523      0.020   26.286    0.000
+#>     SC               0.467      0.023   19.884    0.000
+#>     ENJ:ENJ          0.026      0.022    1.206    0.228
+#>     ENJ:SC          -0.039      0.046   -0.851    0.395
+#>     SC:SC           -0.002      0.035   -0.058    0.953
+#> 
+#> Intercepts:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     enjoy1           0.000      0.013   -0.008    0.994
+#>     enjoy2           0.000      0.015    0.010    0.992
+#>     enjoy3           0.000      0.013   -0.023    0.982
+#>     enjoy4           0.000      0.014    0.008    0.993
+#>     enjoy5           0.000      0.014    0.025    0.980
+#>     academic1        0.000      0.016   -0.009    0.993
+#>     academic2        0.000      0.014   -0.009    0.993
+#>     academic3        0.000      0.015   -0.028    0.978
+#>     academic4        0.000      0.016   -0.015    0.988
+#>     academic5       -0.001      0.014   -0.044    0.965
+#>     academic6        0.001      0.015    0.048    0.962
+#>     career1         -0.004      0.017   -0.204    0.838
+#>     career2         -0.004      0.018   -0.248    0.804
+#>     career3         -0.004      0.017   -0.214    0.830
+#>     career4         -0.004      0.016   -0.232    0.816
+#>     CAREER           0.000                             
+#>     ENJ              0.000                             
+#>     SC               0.000                             
+#> 
+#> Covariances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   ENJ ~~ 
+#>     SC               0.218      0.009   25.477    0.000
+#> 
+#> Variances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     enjoy1           0.487      0.011   44.335    0.000
+#>     enjoy2           0.488      0.011   44.406    0.000
+#>     enjoy3           0.596      0.012   48.233    0.000
+#>     enjoy4           0.488      0.011   44.561    0.000
+#>     enjoy5           0.442      0.010   42.470    0.000
+#>     academic1        0.645      0.013   49.813    0.000
+#>     academic2        0.566      0.012   47.864    0.000
+#>     academic3        0.473      0.011   44.319    0.000
+#>     academic4        0.455      0.010   43.579    0.000
+#>     academic5        0.565      0.012   47.695    0.000
+#>     academic6        0.502      0.011   45.434    0.000
+#>     career1          0.373      0.009   40.392    0.000
+#>     career2          0.328      0.009   37.019    0.000
+#>     career3          0.436      0.010   43.272    0.000
+#>     career4          0.576      0.012   48.372    0.000
+#>     ENJ              0.500      0.017   29.547    0.000
+#>     SC               0.338      0.015   23.195    0.000
+#>     CAREER           0.302      0.010   29.711    0.000
+

Note: The other approaches work as well, but might be quite slow +depending on the number of interaction effects (particularly for the +LMS- and constrained approach).

+
+
+
+
+ + + +
+

Developed by Kjell Solem Slupphaug.

+
+ +
+

Site built with pkgdown 2.1.0.

+
+ +
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+ + + + +
+
+ +

observed variables in the LMS- and QML approach

+ + + Source: vignettes/observed_lms_qml.Rmd +
observed_lms_qml.Rmd
+
+ + + + +
+

The Latent Moderated Structural Equations (LMS) and the Quasi +Maximum Likelihood (QML) approach +

+

In contrast to the other approaches, the LMS and QML approaches are +designed to handle latent variables only. Thus observed variables cannot +be as easily used, as in the other approaches. One way of getting around +this is by specifying your observed variable as a latent variable with a +single indicator. modsem() will by default constrain the +factor loading to 1, and the residual variance of the +indicator to 0. Then, the only difference between the +latent variable and its indicator, is that (assuming that it is an +exogenous variable) it has a zero-mean. This will work for both the LMS- +and QML approach in most cases, except for two exceptions.

+
+

The LMS approach +

+

For the LMS approach you can use the above mentioned approach in +almost all cases, except in the case where you wish to use an observed +variable as a moderating variable. In the LMS approach, you will usually +select one variable in an interaction term as a moderator. The +interaction effect is then estimated via numerical integration, at +n quadrature nodes of the moderating variable. This process +however, requires that the moderating variable has an error-term, as the +distribution of the moderating variable is modelled as +X∼N(Az,ε)X \sim N(Az, \varepsilon), +where +AzAz +is the expected value of +XX +at quadrature point k, and +ε\varepsilon +is the error term. If the error-term is zero, the probability of +observing a given value of +XX +will not be computable. In most instances the first variable in the +interaction term, is chosen as the moderator. For example, if the +interaction term is "X:Z", "X" will usually be +chosen as the moderator. Thus if only one of the variables are latent, +you should put the latent variable first in the interaction term. If +both are observed, you have to specify a measurement error (e.g., “x1 ~~ +0.1 * x1”) for the indicator of the first variable in the interaction +term.

+
+library(modsem)
+
+# interaction effect between a latent and an observed variable
+m1 <- '
+# Outer Model
+  X =~ x1 # X is observed
+  Z =~ z1 + z2 # Z is latent
+  Y =~ y1 # Y is observed
+
+# Inner model
+  Y ~ X + Z
+  Y ~ Z:X
+'
+
+lms1 <- modsem(m1, oneInt, method = "lms")
+
+# interaction effect between two observed variables
+m2 <- '
+# Outer Model
+  X =~ x1 # X is observed
+  Z =~ z1 # Z is observed
+  Y =~ y1 # Y is observed
+  x1 ~~ 0.1 * x1 # specify a variance for the measurement error
+# Inner model
+  Y ~ X + Z
+  Y ~ X:Z
+'
+
+lms2 <- modsem(m1, oneInt, method = "lms")
+summary(lms2)
+#> Estimating null model
+#> EM: Iteration =     1, LogLik =   -10816.13, Change = -10816.126
+#> EM: Iteration =     2, LogLik =   -10816.13, Change =      0.000
+#> 
+#> modsem (version 1.0.3):
+#>   Estimator                                         LMS
+#>   Optimization method                         EM-NLMINB
+#>   Number of observations                           2000
+#>   Number of iterations                               58
+#>   Loglikelihood                                 -8087.2
+#>   Akaike (AIC)                                  16202.4
+#>   Bayesian (BIC)                               16280.81
+#>  
+#> Numerical Integration:
+#>   Points of integration (per dim)                    24
+#>   Dimensions                                          1
+#>   Total points of integration                        24
+#>  
+#> Fit Measures for H0:
+#>   Loglikelihood                                  -10816
+#>   Akaike (AIC)                                 21658.25
+#>   Bayesian (BIC)                               21731.06
+#>   Chi-square                                       0.01
+#>   Degrees of Freedom (Chi-square)                     1
+#>   P-value (Chi-square)                            0.917
+#>   RMSEA                                           0.000
+#>  
+#> Comparative fit to H0 (no interaction effect)
+#>   Loglikelihood change                          2728.93
+#>   Difference test (D)                           5457.85
+#>   Degrees of freedom (D)                              1
+#>   P-value (D)                                     0.000
+#>  
+#> R-Squared:
+#>   Y                                               0.510
+#> R-Squared Null-Model (H0):
+#>   Y                                               0.343
+#> R-Squared Change:
+#>   Y                                               0.167
+#> 
+#> Parameter Estimates:
+#>   Coefficients                           unstandardized
+#>   Information                                  expected
+#>   Standard errors                              standard
+#>  
+#> Latent Variables:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   Z =~ 
+#>     z1               1.000                             
+#>     z2               0.811      0.018    45.25    0.000
+#>   X =~ 
+#>     x1               1.000                             
+#>   Y =~ 
+#>     y1               1.000                             
+#> 
+#> Regressions:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   Y ~ 
+#>     Z                0.587      0.032    18.10    0.000
+#>     X                0.574      0.029    19.96    0.000
+#>     Z:X              0.627      0.026    23.76    0.000
+#> 
+#> Intercepts:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     z1               1.009      0.024    42.22    0.000
+#>     z2               1.203      0.020    60.50    0.000
+#>     x1               1.023      0.024    43.13    0.000
+#>     y1               1.046      0.033    31.33    0.000
+#>     Y                0.000                             
+#>     Z                0.000                             
+#>     X                0.000                             
+#> 
+#> Covariances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   Z ~~ 
+#>     X                0.211      0.025     8.56    0.000
+#> 
+#> Variances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     z1               0.170      0.018     9.23    0.000
+#>     z2               0.160      0.013    12.64    0.000
+#>     x1               0.000                             
+#>     y1               0.000                             
+#>     Z                1.010      0.020    50.04    0.000
+#>     X                1.141      0.016    69.82    0.000
+#>     Y                1.284      0.043    29.70    0.000
+
+
+

The QML approach +

+

The estimation of the QML approach is different from the LMS +approach, and you do not have the same issue as in the LMS approach. +Thus you don’t have to specify a measurement error for moderating +variables.

+
+m3 <- '
+# Outer Model
+  X =~ x1 # X is observed
+  Z =~ z1 # Z is observed
+  Y =~ y1 # Y is observed
+
+# Inner model
+  Y ~ X + Z
+  Y ~ X:Z
+'
+
+qml3 <- modsem(m3, oneInt, method = "qml")
+summary(qml3)
+#> Estimating null model
+#> Starting M-step
+#> 
+#> modsem (version 1.0.3):
+#>   Estimator                                         QML
+#>   Optimization method                            NLMINB
+#>   Number of observations                           2000
+#>   Number of iterations                               11
+#>   Loglikelihood                                -9117.07
+#>   Akaike (AIC)                                 18254.13
+#>   Bayesian (BIC)                               18310.14
+#>  
+#> Fit Measures for H0:
+#>   Loglikelihood                                   -9369
+#>   Akaike (AIC)                                 18756.46
+#>   Bayesian (BIC)                               18806.87
+#>   Chi-square                                       0.00
+#>   Degrees of Freedom (Chi-square)                     0
+#>   P-value (Chi-square)                            0.000
+#>   RMSEA                                           0.000
+#>  
+#> Comparative fit to H0 (no interaction effect)
+#>   Loglikelihood change                           252.17
+#>   Difference test (D)                            504.33
+#>   Degrees of freedom (D)                              1
+#>   P-value (D)                                     0.000
+#>  
+#> R-Squared:
+#>   Y                                               0.470
+#> R-Squared Null-Model (H0):
+#>   Y                                               0.320
+#> R-Squared Change:
+#>   Y                                               0.150
+#> 
+#> Parameter Estimates:
+#>   Coefficients                           unstandardized
+#>   Information                                  observed
+#>   Standard errors                              standard
+#>  
+#> Latent Variables:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   X =~ 
+#>     x1               1.000                             
+#>   Z =~ 
+#>     z1               1.000                             
+#>   Y =~ 
+#>     y1               1.000                             
+#> 
+#> Regressions:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   Y ~ 
+#>     X                0.605      0.028    21.26    0.000
+#>     Z                0.490      0.028    17.55    0.000
+#>     X:Z              0.544      0.023    23.95    0.000
+#> 
+#> Intercepts:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     x1               1.023      0.024    42.83    0.000
+#>     z1               1.011      0.024    41.56    0.000
+#>     y1               1.066      0.034    31.64    0.000
+#>     Y                0.000                             
+#>     X                0.000                             
+#>     Z                0.000                             
+#> 
+#> Covariances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   X ~~ 
+#>     Z                0.210      0.026     7.95    0.000
+#> 
+#> Variances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     x1               0.000                             
+#>     z1               0.000                             
+#>     y1               0.000                             
+#>     X                1.141      0.036    31.62    0.000
+#>     Z                1.184      0.037    31.62    0.000
+#>     Y                1.399      0.044    31.62    0.000
+
+
+
+
+ + + +
+

Developed by Kjell Solem Slupphaug.

+
+ +
+

Site built with pkgdown 2.1.0.

+
+ +
+
+ + + + + + + diff --git a/articles/observed_lms_qml_files/accessible-code-block-0.0.1/empty-anchor.js b/articles/observed_lms_qml_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 0000000..ca349fd --- /dev/null +++ b/articles/observed_lms_qml_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/articles/plot_interactions.html b/articles/plot_interactions.html new file mode 100644 index 0000000..201f05b --- /dev/null +++ b/articles/plot_interactions.html @@ -0,0 +1,150 @@ + + + + + + + +plotting interaction effects • modsem + + + + + + + + Skip to contents + + +
+ + + + +
+
+ +

plotting interaction effects

+ + + Source: vignettes/plot_interactions.Rmd +
plot_interactions.Rmd
+
+ + + + +
+

Plotting interaction effects +

+

Interaction effects can be plotted using the included +plot_interaction function. This function takes a fitted +model object and the names of the two variables that are interacting. +The function will plot the interaction effect of the two variables. The +x-variable is plotted on the x-axis and the y-variable is plotted on the +y-axis. And the z-variable decides at what points of z the effect of x +on y is plotted. The function will also plot the 95% confidence interval +of the interaction effect.

+

Here we can see a simple example using the double centering +approach.

+
+m1 <- "
+# Outer Model
+  X =~ x1
+  X =~ x2 + x3
+  Z =~ z1 + z2 + z3
+  Y =~ y1 + y2 + y3
+
+# Inner model
+  Y ~ X + Z + X:Z
+"
+est1 <- modsem(m1, data = oneInt)
+plot_interaction("X", "Z", "Y", "X:Z", -3:3, c(-0.2, 0), est1)
+

+Here we can see a different example using the LMS approach, in the +theory of planned behavior model.

+
+tpb <- "
+# Outer Model (Based on Hagger et al., 2007)
+  ATT =~ att1 + att2 + att3 + att4 + att5
+  SN =~ sn1 + sn2
+  PBC =~ pbc1 + pbc2 + pbc3
+  INT =~ int1 + int2 + int3
+  BEH =~ b1 + b2
+
+# Inner Model (Based on Steinmetz et al., 2011)
+  INT ~ ATT + SN + PBC
+  BEH ~ INT + PBC
+  BEH ~ PBC:INT
+"
+
+est2 <- modsem(tpb, TPB, method = "lms")
+#> Warning: It is recommended that you have at least 32 nodes for interaction
+#> effects between exogenous and endogenous variables in the lms approach 'nodes =
+#> 24'
+plot_interaction(x = "INT", z = "PBC", y = "BEH", xz = "PBC:INT", 
+                 vals_z = c(-0.5, 0.5), model = est2)
+

+
+
+
+ + + +
+

Developed by Kjell Solem Slupphaug.

+
+ +
+

Site built with pkgdown 2.1.0.

+
+ +
+
+ + + + + + + diff --git a/articles/plot_interactions_files/accessible-code-block-0.0.1/empty-anchor.js b/articles/plot_interactions_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 0000000..ca349fd --- /dev/null +++ b/articles/plot_interactions_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/articles/plot_interactions_files/figure-html/unnamed-chunk-2-1.png b/articles/plot_interactions_files/figure-html/unnamed-chunk-2-1.png new file mode 100644 index 0000000..566b13a Binary files /dev/null and b/articles/plot_interactions_files/figure-html/unnamed-chunk-2-1.png differ diff --git a/articles/plot_interactions_files/figure-html/unnamed-chunk-3-1.png b/articles/plot_interactions_files/figure-html/unnamed-chunk-3-1.png new file mode 100644 index 0000000..d7d3d76 Binary files /dev/null and b/articles/plot_interactions_files/figure-html/unnamed-chunk-3-1.png differ diff --git a/articles/quadratic.html b/articles/quadratic.html new file mode 100644 index 0000000..adb7a72 --- /dev/null +++ b/articles/quadratic.html @@ -0,0 +1,351 @@ + + + + + + + +quadratic effects • modsem + + + + + + + + Skip to contents + + +
+ + + + +
+
+ +

quadratic effects

+ + + Source: vignettes/quadratic.Rmd +
quadratic.Rmd
+
+ + + + +

In essence quadratic effects are just a special case of interaction +effects – where a variable has an interaction effect with itself. Thus, +all of the modsem methods can be used to estimate quadratic effects as +well.

+

Here you can see a very simple example using the LMS-approach.

+
+library(modsem)
+m1 <- '
+# Outer Model
+X =~ x1 + x2 + x3
+Y =~ y1 + y2 + y3
+Z =~ z1 + z2 + z3
+
+# Inner model
+Y ~ X + Z + Z:X + X:X
+'
+
+est1Lms <- modsem(m1, data = oneInt, method = "lms")
+summary(est1Lms)
+#> Estimating null model
+#> EM: Iteration =     1, LogLik =   -17831.87, Change = -17831.875
+#> EM: Iteration =     2, LogLik =   -17831.87, Change =      0.000
+#> 
+#> modsem (version 1.0.3):
+#>   Estimator                                         LMS
+#>   Optimization method                         EM-NLMINB
+#>   Number of observations                           2000
+#>   Number of iterations                              119
+#>   Loglikelihood                               -14687.61
+#>   Akaike (AIC)                                 29439.22
+#>   Bayesian (BIC)                               29618.45
+#>  
+#> Numerical Integration:
+#>   Points of integration (per dim)                    24
+#>   Dimensions                                          1
+#>   Total points of integration                        24
+#>  
+#> Fit Measures for H0:
+#>   Loglikelihood                                  -17832
+#>   Akaike (AIC)                                 35723.75
+#>   Bayesian (BIC)                               35891.78
+#>   Chi-square                                      17.52
+#>   Degrees of Freedom (Chi-square)                    24
+#>   P-value (Chi-square)                            0.826
+#>   RMSEA                                           0.000
+#>  
+#> Comparative fit to H0 (no interaction effect)
+#>   Loglikelihood change                          3144.26
+#>   Difference test (D)                           6288.52
+#>   Degrees of freedom (D)                              2
+#>   P-value (D)                                     0.000
+#>  
+#> R-Squared:
+#>   Y                                               0.596
+#> R-Squared Null-Model (H0):
+#>   Y                                               0.395
+#> R-Squared Change:
+#>   Y                                               0.200
+#> 
+#> Parameter Estimates:
+#>   Coefficients                           unstandardized
+#>   Information                                  expected
+#>   Standard errors                              standard
+#>  
+#> Latent Variables:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   X =~ 
+#>     x1               1.000                             
+#>     x2               0.804      0.013   63.648    0.000
+#>     x3               0.915      0.014   66.681    0.000
+#>   Z =~ 
+#>     z1               1.000                             
+#>     z2               0.810      0.012   65.547    0.000
+#>     z3               0.881      0.013   66.644    0.000
+#>   Y =~ 
+#>     y1               1.000                             
+#>     y2               0.798      0.008  105.935    0.000
+#>     y3               0.899      0.008  109.335    0.000
+#> 
+#> Regressions:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   Y ~ 
+#>     X                0.673      0.031   21.616    0.000
+#>     Z                0.570      0.028   20.006    0.000
+#>     X:X             -0.007      0.020   -0.364    0.716
+#>     X:Z              0.715      0.029   25.082    0.000
+#> 
+#> Intercepts:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     x1               1.023      0.019   52.606    0.000
+#>     x2               1.215      0.017   73.187    0.000
+#>     x3               0.919      0.018   50.599    0.000
+#>     z1               1.013      0.024   41.627    0.000
+#>     z2               1.207      0.020   59.429    0.000
+#>     z3               0.917      0.022   42.344    0.000
+#>     y1               1.046      0.036   29.466    0.000
+#>     y2               1.228      0.029   42.539    0.000
+#>     y3               0.962      0.032   29.921    0.000
+#>     Y                0.000                             
+#>     X                0.000                             
+#>     Z                0.000                             
+#> 
+#> Covariances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   X ~~ 
+#>     Z                0.199      0.024    8.301    0.000
+#> 
+#> Variances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     x1               0.160      0.008   18.929    0.000
+#>     x2               0.163      0.007   23.701    0.000
+#>     x3               0.165      0.008   21.078    0.000
+#>     z1               0.167      0.009   18.594    0.000
+#>     z2               0.160      0.007   22.969    0.000
+#>     z3               0.158      0.008   20.921    0.000
+#>     y1               0.160      0.009   18.034    0.000
+#>     y2               0.154      0.007   22.804    0.000
+#>     y3               0.163      0.008   20.824    0.000
+#>     X                0.972      0.016   60.080    0.000
+#>     Z                1.017      0.019   54.904    0.000
+#>     Y                0.983      0.038   26.163    0.000
+

In this example we have a simple model with two quadratic effects and +one interaction effect, using the QML- and double centering approach, +using the data from a subset of the PISA 2006 data.

+
+m2 <- '
+ENJ =~ enjoy1 + enjoy2 + enjoy3 + enjoy4 + enjoy5
+CAREER =~ career1 + career2 + career3 + career4
+SC =~ academic1 + academic2 + academic3 + academic4 + academic5 + academic6
+CAREER ~ ENJ + SC + ENJ:ENJ + SC:SC + ENJ:SC
+'
+
+est2Dblcent <- modsem(m2, data = jordan)
+est2Qml <- modsem(m2, data = jordan, method = "qml")
+summary(est2Qml)
+#> Estimating null model
+#> Starting M-step
+#> 
+#> modsem (version 1.0.3):
+#>   Estimator                                         QML
+#>   Optimization method                            NLMINB
+#>   Number of observations                           6038
+#>   Number of iterations                              101
+#>   Loglikelihood                              -110520.22
+#>   Akaike (AIC)                                221142.45
+#>   Bayesian (BIC)                              221484.44
+#>  
+#> Fit Measures for H0:
+#>   Loglikelihood                                 -110521
+#>   Akaike (AIC)                                221138.58
+#>   Bayesian (BIC)                              221460.46
+#>   Chi-square                                    1016.34
+#>   Degrees of Freedom (Chi-square)                    87
+#>   P-value (Chi-square)                            0.000
+#>   RMSEA                                           0.005
+#>  
+#> Comparative fit to H0 (no interaction effect)
+#>   Loglikelihood change                             1.07
+#>   Difference test (D)                              2.13
+#>   Degrees of freedom (D)                              3
+#>   P-value (D)                                     0.546
+#>  
+#> R-Squared:
+#>   CAREER                                          0.512
+#> R-Squared Null-Model (H0):
+#>   CAREER                                          0.510
+#> R-Squared Change:
+#>   CAREER                                          0.002
+#> 
+#> Parameter Estimates:
+#>   Coefficients                           unstandardized
+#>   Information                                  observed
+#>   Standard errors                              standard
+#>  
+#> Latent Variables:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   ENJ =~ 
+#>     enjoy1           1.000                             
+#>     enjoy2           1.002      0.020   50.587    0.000
+#>     enjoy3           0.894      0.020   43.669    0.000
+#>     enjoy4           0.999      0.021   48.227    0.000
+#>     enjoy5           1.047      0.021   50.400    0.000
+#>   SC =~ 
+#>     academic1        1.000                             
+#>     academic2        1.104      0.028   38.946    0.000
+#>     academic3        1.235      0.030   41.720    0.000
+#>     academic4        1.254      0.030   41.828    0.000
+#>     academic5        1.113      0.029   38.647    0.000
+#>     academic6        1.198      0.030   40.356    0.000
+#>   CAREER =~ 
+#>     career1          1.000                             
+#>     career2          1.040      0.016   65.180    0.000
+#>     career3          0.952      0.016   57.838    0.000
+#>     career4          0.818      0.017   48.358    0.000
+#> 
+#> Regressions:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   CAREER ~ 
+#>     ENJ              0.523      0.020   26.286    0.000
+#>     SC               0.467      0.023   19.884    0.000
+#>     ENJ:ENJ          0.026      0.022    1.206    0.228
+#>     ENJ:SC          -0.039      0.046   -0.851    0.395
+#>     SC:SC           -0.002      0.035   -0.058    0.953
+#> 
+#> Intercepts:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     enjoy1           0.000      0.013   -0.008    0.994
+#>     enjoy2           0.000      0.015    0.010    0.992
+#>     enjoy3           0.000      0.013   -0.023    0.982
+#>     enjoy4           0.000      0.014    0.008    0.993
+#>     enjoy5           0.000      0.014    0.025    0.980
+#>     academic1        0.000      0.016   -0.009    0.993
+#>     academic2        0.000      0.014   -0.009    0.993
+#>     academic3        0.000      0.015   -0.028    0.978
+#>     academic4        0.000      0.016   -0.015    0.988
+#>     academic5       -0.001      0.014   -0.044    0.965
+#>     academic6        0.001      0.015    0.048    0.962
+#>     career1         -0.004      0.017   -0.204    0.838
+#>     career2         -0.004      0.018   -0.248    0.804
+#>     career3         -0.004      0.017   -0.214    0.830
+#>     career4         -0.004      0.016   -0.232    0.816
+#>     CAREER           0.000                             
+#>     ENJ              0.000                             
+#>     SC               0.000                             
+#> 
+#> Covariances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>   ENJ ~~ 
+#>     SC               0.218      0.009   25.477    0.000
+#> 
+#> Variances:
+#>                   Estimate  Std.Error  z.value  P(>|z|)
+#>     enjoy1           0.487      0.011   44.335    0.000
+#>     enjoy2           0.488      0.011   44.406    0.000
+#>     enjoy3           0.596      0.012   48.233    0.000
+#>     enjoy4           0.488      0.011   44.561    0.000
+#>     enjoy5           0.442      0.010   42.470    0.000
+#>     academic1        0.645      0.013   49.813    0.000
+#>     academic2        0.566      0.012   47.864    0.000
+#>     academic3        0.473      0.011   44.319    0.000
+#>     academic4        0.455      0.010   43.579    0.000
+#>     academic5        0.565      0.012   47.695    0.000
+#>     academic6        0.502      0.011   45.434    0.000
+#>     career1          0.373      0.009   40.392    0.000
+#>     career2          0.328      0.009   37.019    0.000
+#>     career3          0.436      0.010   43.272    0.000
+#>     career4          0.576      0.012   48.372    0.000
+#>     ENJ              0.500      0.017   29.547    0.000
+#>     SC               0.338      0.015   23.195    0.000
+#>     CAREER           0.302      0.010   29.711    0.000
+

Note: The other approaches work as well, but might be quite slow +depending on the number of interaction effects (particularly for the +LMS- and constrained approach).

+
+
+ + + +
+

Developed by Kjell Solem Slupphaug.

+
+ +
+

Site built with pkgdown 2.1.0.

+
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+ + + + + + + diff --git a/articles/quadratic_files/accessible-code-block-0.0.1/empty-anchor.js b/articles/quadratic_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 0000000..ca349fd --- /dev/null +++ b/articles/quadratic_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/authors.html b/authors.html new file mode 100644 index 0000000..3ddb9b3 --- /dev/null +++ b/authors.html @@ -0,0 +1,90 @@ + +Authors and Citation • modsem + Skip to contents + + +
+
+
+ +

Authors and Citation

+
+ +
+

Authors

+ +
  • +

    Kjell Solem Slupphaug. Author, maintainer. +

    +
    • +
+ +
+

Citation

+

Source: DESCRIPTION

+ +

Solem Slupphaug K (2024). +modsem: Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM). +R package version 1.0.3, https://github.com/Kss2k/modsem. +

+ 
@Manual{,
+  title = {modsem: Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM)},
+  author = {Kjell {Solem Slupphaug}},
+  year = {2024},
+  note = {R package version 1.0.3},
+  url = {https://github.com/Kss2k/modsem},
+}
+
+ +
+ + +
+

Developed by Kjell Solem Slupphaug.

+
+ +
+

Site built with pkgdown 2.1.0.

+
+ +
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t}))){T=O,E=!1;break}A.set(O,N)}if(E)for(var P=function(t){var e=v.find((function(e){var i=A.get(e);if(i)return i.slice(0,t).every((function(t){return t}))}));if(e)return T=e,"break"},M=p?3:1;M>0&&"break"!==P(M);M--);e.placement!==T&&(e.modifiersData[n]._skip=!0,e.placement=T,e.reset=!0)}},requiresIfExists:["offset"],data:{_skip:!1}};function oi(t,e,i){return void 0===i&&(i={x:0,y:0}),{top:t.top-e.height-i.y,right:t.right-e.width+i.x,bottom:t.bottom-e.height+i.y,left:t.left-e.width-i.x}}function ri(t){return[zt,qt,Rt,Vt].some((function(e){return t[e]>=0}))}const ai={name:"hide",enabled:!0,phase:"main",requiresIfExists:["preventOverflow"],fn:function(t){var 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m?m(Object.assign({},e.rects,{placement:e.placement})):m,O="number"==typeof C?{mainAxis:C,altAxis:C}:Object.assign({mainAxis:0,altAxis:0},C),x=e.modifiersData.offset?e.modifiersData.offset[e.placement]:null,k={x:0,y:0};if(A){if(o){var L,S="y"===y?zt:Vt,D="y"===y?Rt:qt,$="y"===y?"height":"width",I=A[y],N=I+g[S],P=I-g[D],M=f?-T[$]/2:0,j=b===Xt?E[$]:T[$],F=b===Xt?-T[$]:-E[$],H=e.elements.arrow,W=f&&H?Ce(H):{width:0,height:0},B=e.modifiersData["arrow#persistent"]?e.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},z=B[S],R=B[D],q=Ne(0,E[$],W[$]),V=v?E[$]/2-M-q-z-O.mainAxis:j-q-z-O.mainAxis,K=v?-E[$]/2+M+q+R+O.mainAxis:F+q+R+O.mainAxis,Q=e.elements.arrow&&$e(e.elements.arrow),X=Q?"y"===y?Q.clientTop||0:Q.clientLeft||0:0,Y=null!=(L=null==x?void 0:x[y])?L:0,U=I+K-Y,G=Ne(f?ye(N,I+V-Y-X):N,I,f?ve(P,U):P);A[y]=G,k[y]=G-I}if(a){var J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;iNumber.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu 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e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const 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W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
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")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get 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e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))z.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of z.parents(t,".nav, .list-group"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(window,gs,(()=>{for(const t of z.find('[data-bs-spy="scroll"]'))Es.getOrCreateInstance(t)})),m(Es);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",Ms="Home",js="End",Fs="active",Hs="fade",Ws="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Rs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,qs=`.${Fs}[data-bs-toggle="tab"], .${Fs}[data-bs-toggle="pill"], .${Fs}[data-bs-toggle="list"]`;class Vs extends W{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),N.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?N.trigger(e,Cs,{relatedTarget:t}):null;N.trigger(t,xs,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Fs),this._activate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),N.trigger(t,ks,{relatedTarget:e})):t.classList.add(Ws)}),t,t.classList.contains(Hs)))}_deactivate(t,e){t&&(t.classList.remove(Fs),t.blur(),this._deactivate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),N.trigger(t,Os,{relatedTarget:e})):t.classList.remove(Ws)}),t,t.classList.contains(Hs)))}_keydown(t){if(![$s,Is,Ns,Ps,Ms,js].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!l(t)));let i;if([Ms,js].includes(t.key))i=e[t.key===Ms?0:e.length-1];else{const n=[Is,Ps].includes(t.key);i=b(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Vs.getOrCreateInstance(i).show())}_getChildren(){return z.find(Rs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const 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"${t}"`);e[t]()}}))}}N.on(document,Ls,zs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this)||Vs.getOrCreateInstance(this).show()})),N.on(window,Ds,(()=>{for(const t of z.find(qs))Vs.getOrCreateInstance(t)})),m(Vs);const Ks=".bs.toast",Qs=`mouseover${Ks}`,Xs=`mouseout${Ks}`,Ys=`focusin${Ks}`,Us=`focusout${Ks}`,Gs=`hide${Ks}`,Js=`hidden${Ks}`,Zs=`show${Ks}`,to=`shown${Ks}`,eo="hide",io="show",no="showing",so={animation:"boolean",autohide:"boolean",delay:"number"},oo={animation:!0,autohide:!0,delay:5e3};class ro extends W{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return oo}static get DefaultType(){return so}static get 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* --------------------------------------------------------------------------\n * Bootstrap dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\n\nconst elementMap = new Map()\n\nexport default {\n set(element, key, instance) {\n if (!elementMap.has(element)) {\n elementMap.set(element, new Map())\n }\n\n const instanceMap = elementMap.get(element)\n\n // make it clear we only want one instance per element\n // can be removed later when multiple key/instances are fine to be used\n if (!instanceMap.has(key) && instanceMap.size !== 0) {\n // eslint-disable-next-line no-console\n console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`)\n return\n }\n\n instanceMap.set(key, instance)\n },\n\n get(element, key) {\n if (elementMap.has(element)) {\n return elementMap.get(element).get(key) || null\n }\n\n return null\n },\n\n remove(element, key) {\n if (!elementMap.has(element)) {\n return\n }\n\n const instanceMap = elementMap.get(element)\n\n instanceMap.delete(key)\n\n // free up element references if there are no instances left for an element\n if (instanceMap.size === 0) {\n elementMap.delete(element)\n }\n }\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst MAX_UID = 1_000_000\nconst MILLISECONDS_MULTIPLIER = 1000\nconst TRANSITION_END = 'transitionend'\n\n/**\n * Properly escape IDs selectors to handle weird IDs\n * @param {string} selector\n * @returns {string}\n */\nconst parseSelector = selector => {\n if (selector && window.CSS && window.CSS.escape) {\n // document.querySelector needs escaping to handle IDs (html5+) containing for instance /\n selector = selector.replace(/#([^\\s\"#']+)/g, (match, id) => `#${CSS.escape(id)}`)\n }\n\n return selector\n}\n\n// Shout-out Angus Croll (https://goo.gl/pxwQGp)\nconst toType = object => {\n if (object === null || object === undefined) {\n return `${object}`\n }\n\n return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase()\n}\n\n/**\n * Public Util API\n */\n\nconst getUID = prefix => {\n do {\n prefix += Math.floor(Math.random() * MAX_UID)\n } while (document.getElementById(prefix))\n\n return prefix\n}\n\nconst getTransitionDurationFromElement = element => {\n if (!element) {\n return 0\n }\n\n // Get transition-duration of the element\n let { transitionDuration, transitionDelay } = window.getComputedStyle(element)\n\n const floatTransitionDuration = Number.parseFloat(transitionDuration)\n const floatTransitionDelay = Number.parseFloat(transitionDelay)\n\n // Return 0 if element or transition duration is not found\n if (!floatTransitionDuration && !floatTransitionDelay) {\n return 0\n }\n\n // If multiple durations are defined, take the first\n transitionDuration = transitionDuration.split(',')[0]\n transitionDelay = transitionDelay.split(',')[0]\n\n return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER\n}\n\nconst triggerTransitionEnd = element => {\n element.dispatchEvent(new Event(TRANSITION_END))\n}\n\nconst isElement = object => {\n if (!object || typeof object !== 'object') {\n return false\n }\n\n if (typeof object.jquery !== 'undefined') {\n object = object[0]\n }\n\n return typeof object.nodeType !== 'undefined'\n}\n\nconst getElement = object => {\n // it's a jQuery object or a node element\n if (isElement(object)) {\n return object.jquery ? object[0] : object\n }\n\n if (typeof object === 'string' && object.length > 0) {\n return document.querySelector(parseSelector(object))\n }\n\n return null\n}\n\nconst isVisible = element => {\n if (!isElement(element) || element.getClientRects().length === 0) {\n return false\n }\n\n const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible'\n // Handle `details` element as its content may falsie appear visible when it is closed\n const closedDetails = element.closest('details:not([open])')\n\n if (!closedDetails) {\n return elementIsVisible\n }\n\n if (closedDetails !== element) {\n const summary = element.closest('summary')\n if (summary && summary.parentNode !== closedDetails) {\n return false\n }\n\n if (summary === null) {\n return false\n }\n }\n\n return elementIsVisible\n}\n\nconst isDisabled = element => {\n if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n return true\n }\n\n if (element.classList.contains('disabled')) {\n return true\n }\n\n if (typeof element.disabled !== 'undefined') {\n return element.disabled\n }\n\n return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false'\n}\n\nconst findShadowRoot = element => {\n if (!document.documentElement.attachShadow) {\n return null\n }\n\n // Can find the shadow root otherwise it'll return the document\n if (typeof element.getRootNode === 'function') {\n const root = element.getRootNode()\n return root instanceof ShadowRoot ? root : null\n }\n\n if (element instanceof ShadowRoot) {\n return element\n }\n\n // when we don't find a shadow root\n if (!element.parentNode) {\n return null\n }\n\n return findShadowRoot(element.parentNode)\n}\n\nconst noop = () => {}\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n element.offsetHeight // eslint-disable-line no-unused-expressions\n}\n\nconst getjQuery = () => {\n if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n return window.jQuery\n }\n\n return null\n}\n\nconst DOMContentLoadedCallbacks = []\n\nconst onDOMContentLoaded = callback => {\n if (document.readyState === 'loading') {\n // add listener on the first call when the document is in loading state\n if (!DOMContentLoadedCallbacks.length) {\n document.addEventListener('DOMContentLoaded', () => {\n for (const callback of DOMContentLoadedCallbacks) {\n callback()\n }\n })\n }\n\n DOMContentLoadedCallbacks.push(callback)\n } else {\n callback()\n }\n}\n\nconst isRTL = () => document.documentElement.dir === 'rtl'\n\nconst defineJQueryPlugin = plugin => {\n onDOMContentLoaded(() => {\n const $ = getjQuery()\n /* istanbul ignore if */\n if ($) {\n const name = plugin.NAME\n const JQUERY_NO_CONFLICT = $.fn[name]\n $.fn[name] = plugin.jQueryInterface\n $.fn[name].Constructor = plugin\n $.fn[name].noConflict = () => {\n $.fn[name] = JQUERY_NO_CONFLICT\n return plugin.jQueryInterface\n }\n }\n })\n}\n\nconst execute = (possibleCallback, args = [], defaultValue = possibleCallback) => {\n return typeof possibleCallback === 'function' ? possibleCallback(...args) : defaultValue\n}\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n if (!waitForTransition) {\n execute(callback)\n return\n }\n\n const durationPadding = 5\n const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding\n\n let called = false\n\n const handler = ({ target }) => {\n if (target !== transitionElement) {\n return\n }\n\n called = true\n transitionElement.removeEventListener(TRANSITION_END, handler)\n execute(callback)\n }\n\n transitionElement.addEventListener(TRANSITION_END, handler)\n setTimeout(() => {\n if (!called) {\n triggerTransitionEnd(transitionElement)\n }\n }, emulatedDuration)\n}\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list The list of elements\n * @param activeElement The active element\n * @param shouldGetNext Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n const listLength = list.length\n let index = list.indexOf(activeElement)\n\n // if the element does not exist in the list return an element\n // depending on the direction and if cycle is allowed\n if (index === -1) {\n return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0]\n }\n\n index += shouldGetNext ? 1 : -1\n\n if (isCycleAllowed) {\n index = (index + listLength) % listLength\n }\n\n return list[Math.max(0, Math.min(index, listLength - 1))]\n}\n\nexport {\n defineJQueryPlugin,\n execute,\n executeAfterTransition,\n findShadowRoot,\n getElement,\n getjQuery,\n getNextActiveElement,\n getTransitionDurationFromElement,\n getUID,\n isDisabled,\n isElement,\n isRTL,\n isVisible,\n noop,\n onDOMContentLoaded,\n parseSelector,\n reflow,\n triggerTransitionEnd,\n toType\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { getjQuery } from '../util/index.js'\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/\nconst stripNameRegex = /\\..*/\nconst stripUidRegex = /::\\d+$/\nconst eventRegistry = {} // Events storage\nlet uidEvent = 1\nconst customEvents = {\n mouseenter: 'mouseover',\n mouseleave: 'mouseout'\n}\n\nconst nativeEvents = new Set([\n 'click',\n 'dblclick',\n 'mouseup',\n 'mousedown',\n 'contextmenu',\n 'mousewheel',\n 'DOMMouseScroll',\n 'mouseover',\n 'mouseout',\n 'mousemove',\n 'selectstart',\n 'selectend',\n 'keydown',\n 'keypress',\n 'keyup',\n 'orientationchange',\n 'touchstart',\n 'touchmove',\n 'touchend',\n 'touchcancel',\n 'pointerdown',\n 'pointermove',\n 'pointerup',\n 'pointerleave',\n 'pointercancel',\n 'gesturestart',\n 'gesturechange',\n 'gestureend',\n 'focus',\n 'blur',\n 'change',\n 'reset',\n 'select',\n 'submit',\n 'focusin',\n 'focusout',\n 'load',\n 'unload',\n 'beforeunload',\n 'resize',\n 'move',\n 'DOMContentLoaded',\n 'readystatechange',\n 'error',\n 'abort',\n 'scroll'\n])\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n return (uid && `${uid}::${uidEvent++}`) || element.uidEvent || uidEvent++\n}\n\nfunction getElementEvents(element) {\n const uid = makeEventUid(element)\n\n element.uidEvent = uid\n eventRegistry[uid] = eventRegistry[uid] || {}\n\n return eventRegistry[uid]\n}\n\nfunction bootstrapHandler(element, fn) {\n return function handler(event) {\n hydrateObj(event, { delegateTarget: element })\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, fn)\n }\n\n return fn.apply(element, [event])\n }\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n return function handler(event) {\n const domElements = element.querySelectorAll(selector)\n\n for (let { target } = event; target && target !== this; target = target.parentNode) {\n for (const domElement of domElements) {\n if (domElement !== target) {\n continue\n }\n\n hydrateObj(event, { delegateTarget: target })\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, selector, fn)\n }\n\n return fn.apply(target, [event])\n }\n }\n }\n}\n\nfunction findHandler(events, callable, delegationSelector = null) {\n return Object.values(events)\n .find(event => event.callable === callable && event.delegationSelector === delegationSelector)\n}\n\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n const isDelegated = typeof handler === 'string'\n // TODO: tooltip passes `false` instead of selector, so we need to check\n const callable = isDelegated ? delegationFunction : (handler || delegationFunction)\n let typeEvent = getTypeEvent(originalTypeEvent)\n\n if (!nativeEvents.has(typeEvent)) {\n typeEvent = originalTypeEvent\n }\n\n return [isDelegated, callable, typeEvent]\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return\n }\n\n let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n\n // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n if (originalTypeEvent in customEvents) {\n const wrapFunction = fn => {\n return function (event) {\n if (!event.relatedTarget || (event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget))) {\n return fn.call(this, event)\n }\n }\n }\n\n callable = wrapFunction(callable)\n }\n\n const events = getElementEvents(element)\n const handlers = events[typeEvent] || (events[typeEvent] = {})\n const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null)\n\n if (previousFunction) {\n previousFunction.oneOff = previousFunction.oneOff && oneOff\n\n return\n }\n\n const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''))\n const fn = isDelegated ?\n bootstrapDelegationHandler(element, handler, callable) :\n bootstrapHandler(element, callable)\n\n fn.delegationSelector = isDelegated ? handler : null\n fn.callable = callable\n fn.oneOff = oneOff\n fn.uidEvent = uid\n handlers[uid] = fn\n\n element.addEventListener(typeEvent, fn, isDelegated)\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n const fn = findHandler(events[typeEvent], handler, delegationSelector)\n\n if (!fn) {\n return\n }\n\n element.removeEventListener(typeEvent, fn, Boolean(delegationSelector))\n delete events[typeEvent][fn.uidEvent]\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n const storeElementEvent = events[typeEvent] || {}\n\n for (const [handlerKey, event] of Object.entries(storeElementEvent)) {\n if (handlerKey.includes(namespace)) {\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n }\n }\n}\n\nfunction getTypeEvent(event) {\n // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n event = event.replace(stripNameRegex, '')\n return customEvents[event] || event\n}\n\nconst EventHandler = {\n on(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, false)\n },\n\n one(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, true)\n },\n\n off(element, originalTypeEvent, handler, delegationFunction) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return\n }\n\n const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n const inNamespace = typeEvent !== originalTypeEvent\n const events = getElementEvents(element)\n const storeElementEvent = events[typeEvent] || {}\n const isNamespace = originalTypeEvent.startsWith('.')\n\n if (typeof callable !== 'undefined') {\n // Simplest case: handler is passed, remove that listener ONLY.\n if (!Object.keys(storeElementEvent).length) {\n return\n }\n\n removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null)\n return\n }\n\n if (isNamespace) {\n for (const elementEvent of Object.keys(events)) {\n removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1))\n }\n }\n\n for (const [keyHandlers, event] of Object.entries(storeElementEvent)) {\n const handlerKey = keyHandlers.replace(stripUidRegex, '')\n\n if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n }\n }\n },\n\n trigger(element, event, args) {\n if (typeof event !== 'string' || !element) {\n return null\n }\n\n const $ = getjQuery()\n const typeEvent = getTypeEvent(event)\n const inNamespace = event !== typeEvent\n\n let jQueryEvent = null\n let bubbles = true\n let nativeDispatch = true\n let defaultPrevented = false\n\n if (inNamespace && $) {\n jQueryEvent = $.Event(event, args)\n\n $(element).trigger(jQueryEvent)\n bubbles = !jQueryEvent.isPropagationStopped()\n nativeDispatch = !jQueryEvent.isImmediatePropagationStopped()\n defaultPrevented = jQueryEvent.isDefaultPrevented()\n }\n\n const evt = hydrateObj(new Event(event, { bubbles, cancelable: true }), args)\n\n if (defaultPrevented) {\n evt.preventDefault()\n }\n\n if (nativeDispatch) {\n element.dispatchEvent(evt)\n }\n\n if (evt.defaultPrevented && jQueryEvent) {\n jQueryEvent.preventDefault()\n }\n\n return evt\n }\n}\n\nfunction hydrateObj(obj, meta = {}) {\n for (const [key, value] of Object.entries(meta)) {\n try {\n obj[key] = value\n } catch {\n Object.defineProperty(obj, key, {\n configurable: true,\n get() {\n return value\n }\n })\n }\n }\n\n return obj\n}\n\nexport default EventHandler\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n if (value === 'true') {\n return true\n }\n\n if (value === 'false') {\n return false\n }\n\n if (value === Number(value).toString()) {\n return Number(value)\n }\n\n if (value === '' || value === 'null') {\n return null\n }\n\n if (typeof value !== 'string') {\n return value\n }\n\n try {\n return JSON.parse(decodeURIComponent(value))\n } catch {\n return value\n }\n}\n\nfunction normalizeDataKey(key) {\n return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`)\n}\n\nconst Manipulator = {\n setDataAttribute(element, key, value) {\n element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value)\n },\n\n removeDataAttribute(element, key) {\n element.removeAttribute(`data-bs-${normalizeDataKey(key)}`)\n },\n\n getDataAttributes(element) {\n if (!element) {\n return {}\n }\n\n const attributes = {}\n const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'))\n\n for (const key of bsKeys) {\n let pureKey = key.replace(/^bs/, '')\n pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length)\n attributes[pureKey] = normalizeData(element.dataset[key])\n }\n\n return attributes\n },\n\n getDataAttribute(element, key) {\n return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`))\n }\n}\n\nexport default Manipulator\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Manipulator from '../dom/manipulator.js'\nimport { isElement, toType } from './index.js'\n\n/**\n * Class definition\n */\n\nclass Config {\n // Getters\n static get Default() {\n return {}\n }\n\n static get DefaultType() {\n return {}\n }\n\n static get NAME() {\n throw new Error('You have to implement the static method \"NAME\", for each component!')\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config)\n config = this._configAfterMerge(config)\n this._typeCheckConfig(config)\n return config\n }\n\n _configAfterMerge(config) {\n return config\n }\n\n _mergeConfigObj(config, element) {\n const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {} // try to parse\n\n return {\n ...this.constructor.Default,\n ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n ...(typeof config === 'object' ? config : {})\n }\n }\n\n _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n for (const [property, expectedTypes] of Object.entries(configTypes)) {\n const value = config[property]\n const valueType = isElement(value) ? 'element' : toType(value)\n\n if (!new RegExp(expectedTypes).test(valueType)) {\n throw new TypeError(\n `${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`\n )\n }\n }\n }\n}\n\nexport default Config\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Data from './dom/data.js'\nimport EventHandler from './dom/event-handler.js'\nimport Config from './util/config.js'\nimport { executeAfterTransition, getElement } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst VERSION = '5.3.1'\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n constructor(element, config) {\n super()\n\n element = getElement(element)\n if (!element) {\n return\n }\n\n this._element = element\n this._config = this._getConfig(config)\n\n Data.set(this._element, this.constructor.DATA_KEY, this)\n }\n\n // Public\n dispose() {\n Data.remove(this._element, this.constructor.DATA_KEY)\n EventHandler.off(this._element, this.constructor.EVENT_KEY)\n\n for (const propertyName of Object.getOwnPropertyNames(this)) {\n this[propertyName] = null\n }\n }\n\n _queueCallback(callback, element, isAnimated = true) {\n executeAfterTransition(callback, element, isAnimated)\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config, this._element)\n config = this._configAfterMerge(config)\n this._typeCheckConfig(config)\n return config\n }\n\n // Static\n static getInstance(element) {\n return Data.get(getElement(element), this.DATA_KEY)\n }\n\n static getOrCreateInstance(element, config = {}) {\n return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null)\n }\n\n static get VERSION() {\n return VERSION\n }\n\n static get DATA_KEY() {\n return `bs.${this.NAME}`\n }\n\n static get EVENT_KEY() {\n return `.${this.DATA_KEY}`\n }\n\n static eventName(name) {\n return `${name}${this.EVENT_KEY}`\n }\n}\n\nexport default BaseComponent\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { isDisabled, isVisible, parseSelector } from '../util/index.js'\n\nconst getSelector = element => {\n let selector = element.getAttribute('data-bs-target')\n\n if (!selector || selector === '#') {\n let hrefAttribute = element.getAttribute('href')\n\n // The only valid content that could double as a selector are IDs or classes,\n // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n // `document.querySelector` will rightfully complain it is invalid.\n // See https://github.com/twbs/bootstrap/issues/32273\n if (!hrefAttribute || (!hrefAttribute.includes('#') && !hrefAttribute.startsWith('.'))) {\n return null\n }\n\n // Just in case some CMS puts out a full URL with the anchor appended\n if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n hrefAttribute = `#${hrefAttribute.split('#')[1]}`\n }\n\n selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null\n }\n\n return parseSelector(selector)\n}\n\nconst SelectorEngine = {\n find(selector, element = document.documentElement) {\n return [].concat(...Element.prototype.querySelectorAll.call(element, selector))\n },\n\n findOne(selector, element = document.documentElement) {\n return Element.prototype.querySelector.call(element, selector)\n },\n\n children(element, selector) {\n return [].concat(...element.children).filter(child => child.matches(selector))\n },\n\n parents(element, selector) {\n const parents = []\n let ancestor = element.parentNode.closest(selector)\n\n while (ancestor) {\n parents.push(ancestor)\n ancestor = ancestor.parentNode.closest(selector)\n }\n\n return parents\n },\n\n prev(element, selector) {\n let previous = element.previousElementSibling\n\n while (previous) {\n if (previous.matches(selector)) {\n return [previous]\n }\n\n previous = previous.previousElementSibling\n }\n\n return []\n },\n // TODO: this is now unused; remove later along with prev()\n next(element, selector) {\n let next = element.nextElementSibling\n\n while (next) {\n if (next.matches(selector)) {\n return [next]\n }\n\n next = next.nextElementSibling\n }\n\n return []\n },\n\n focusableChildren(element) {\n const focusables = [\n 'a',\n 'button',\n 'input',\n 'textarea',\n 'select',\n 'details',\n '[tabindex]',\n '[contenteditable=\"true\"]'\n ].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',')\n\n return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el))\n },\n\n getSelectorFromElement(element) {\n const selector = getSelector(element)\n\n if (selector) {\n return SelectorEngine.findOne(selector) ? selector : null\n }\n\n return null\n },\n\n getElementFromSelector(element) {\n const selector = getSelector(element)\n\n return selector ? SelectorEngine.findOne(selector) : null\n },\n\n getMultipleElementsFromSelector(element) {\n const selector = getSelector(element)\n\n return selector ? SelectorEngine.find(selector) : []\n }\n}\n\nexport default SelectorEngine\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport { isDisabled } from './index.js'\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n const clickEvent = `click.dismiss${component.EVENT_KEY}`\n const name = component.NAME\n\n EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault()\n }\n\n if (isDisabled(this)) {\n return\n }\n\n const target = SelectorEngine.getElementFromSelector(this) || this.closest(`.${name}`)\n const instance = component.getOrCreateInstance(target)\n\n // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n instance[method]()\n })\n}\n\nexport {\n enableDismissTrigger\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'alert'\nconst DATA_KEY = 'bs.alert'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_CLOSE = `close${EVENT_KEY}`\nconst EVENT_CLOSED = `closed${EVENT_KEY}`\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME\n }\n\n // Public\n close() {\n const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE)\n\n if (closeEvent.defaultPrevented) {\n return\n }\n\n this._element.classList.remove(CLASS_NAME_SHOW)\n\n const isAnimated = this._element.classList.contains(CLASS_NAME_FADE)\n this._queueCallback(() => this._destroyElement(), this._element, isAnimated)\n }\n\n // Private\n _destroyElement() {\n this._element.remove()\n EventHandler.trigger(this._element, EVENT_CLOSED)\n this.dispose()\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Alert.getOrCreateInstance(this)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config](this)\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close')\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert)\n\nexport default Alert\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'button'\nconst DATA_KEY = 'bs.button'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst CLASS_NAME_ACTIVE = 'active'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"button\"]'\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle() {\n // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE))\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Button.getOrCreateInstance(this)\n\n if (config === 'toggle') {\n data[config]()\n }\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, event => {\n event.preventDefault()\n\n const button = event.target.closest(SELECTOR_DATA_TOGGLE)\n const data = Button.getOrCreateInstance(button)\n\n data.toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button)\n\nexport default Button\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport Config from './config.js'\nimport { execute } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'swipe'\nconst EVENT_KEY = '.bs.swipe'\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY}`\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY}`\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY}`\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY}`\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY}`\nconst POINTER_TYPE_TOUCH = 'touch'\nconst POINTER_TYPE_PEN = 'pen'\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event'\nconst SWIPE_THRESHOLD = 40\n\nconst Default = {\n endCallback: null,\n leftCallback: null,\n rightCallback: null\n}\n\nconst DefaultType = {\n endCallback: '(function|null)',\n leftCallback: '(function|null)',\n rightCallback: '(function|null)'\n}\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n constructor(element, config) {\n super()\n this._element = element\n\n if (!element || !Swipe.isSupported()) {\n return\n }\n\n this._config = this._getConfig(config)\n this._deltaX = 0\n this._supportPointerEvents = Boolean(window.PointerEvent)\n this._initEvents()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n dispose() {\n EventHandler.off(this._element, EVENT_KEY)\n }\n\n // Private\n _start(event) {\n if (!this._supportPointerEvents) {\n this._deltaX = event.touches[0].clientX\n\n return\n }\n\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX\n }\n }\n\n _end(event) {\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX - this._deltaX\n }\n\n this._handleSwipe()\n execute(this._config.endCallback)\n }\n\n _move(event) {\n this._deltaX = event.touches && event.touches.length > 1 ?\n 0 :\n event.touches[0].clientX - this._deltaX\n }\n\n _handleSwipe() {\n const absDeltaX = Math.abs(this._deltaX)\n\n if (absDeltaX <= SWIPE_THRESHOLD) {\n return\n }\n\n const direction = absDeltaX / this._deltaX\n\n this._deltaX = 0\n\n if (!direction) {\n return\n }\n\n execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback)\n }\n\n _initEvents() {\n if (this._supportPointerEvents) {\n EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event))\n EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event))\n\n this._element.classList.add(CLASS_NAME_POINTER_EVENT)\n } else {\n EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event))\n EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event))\n EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event))\n }\n }\n\n _eventIsPointerPenTouch(event) {\n return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH)\n }\n\n // Static\n static isSupported() {\n return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0\n }\n}\n\nexport default Swipe\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n defineJQueryPlugin,\n getNextActiveElement,\n isRTL,\n isVisible,\n reflow,\n triggerTransitionEnd\n} from './util/index.js'\nimport Swipe from './util/swipe.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'carousel'\nconst DATA_KEY = 'bs.carousel'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ARROW_LEFT_KEY = 'ArrowLeft'\nconst ARROW_RIGHT_KEY = 'ArrowRight'\nconst TOUCHEVENT_COMPAT_WAIT = 500 // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next'\nconst ORDER_PREV = 'prev'\nconst DIRECTION_LEFT = 'left'\nconst DIRECTION_RIGHT = 'right'\n\nconst EVENT_SLIDE = `slide${EVENT_KEY}`\nconst EVENT_SLID = `slid${EVENT_KEY}`\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY}`\nconst EVENT_MOUSEENTER = `mouseenter${EVENT_KEY}`\nconst EVENT_MOUSELEAVE = `mouseleave${EVENT_KEY}`\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_CAROUSEL = 'carousel'\nconst CLASS_NAME_ACTIVE = 'active'\nconst CLASS_NAME_SLIDE = 'slide'\nconst CLASS_NAME_END = 'carousel-item-end'\nconst CLASS_NAME_START = 'carousel-item-start'\nconst CLASS_NAME_NEXT = 'carousel-item-next'\nconst CLASS_NAME_PREV = 'carousel-item-prev'\n\nconst SELECTOR_ACTIVE = '.active'\nconst SELECTOR_ITEM = '.carousel-item'\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM\nconst SELECTOR_ITEM_IMG = '.carousel-item img'\nconst SELECTOR_INDICATORS = '.carousel-indicators'\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]'\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]'\n\nconst KEY_TO_DIRECTION = {\n [ARROW_LEFT_KEY]: DIRECTION_RIGHT,\n [ARROW_RIGHT_KEY]: DIRECTION_LEFT\n}\n\nconst Default = {\n interval: 5000,\n keyboard: true,\n pause: 'hover',\n ride: false,\n touch: true,\n wrap: true\n}\n\nconst DefaultType = {\n interval: '(number|boolean)', // TODO:v6 remove boolean support\n keyboard: 'boolean',\n pause: '(string|boolean)',\n ride: '(boolean|string)',\n touch: 'boolean',\n wrap: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._interval = null\n this._activeElement = null\n this._isSliding = false\n this.touchTimeout = null\n this._swipeHelper = null\n\n this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element)\n this._addEventListeners()\n\n if (this._config.ride === CLASS_NAME_CAROUSEL) {\n this.cycle()\n }\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n next() {\n this._slide(ORDER_NEXT)\n }\n\n nextWhenVisible() {\n // FIXME TODO use `document.visibilityState`\n // Don't call next when the page isn't visible\n // or the carousel or its parent isn't visible\n if (!document.hidden && isVisible(this._element)) {\n this.next()\n }\n }\n\n prev() {\n this._slide(ORDER_PREV)\n }\n\n pause() {\n if (this._isSliding) {\n triggerTransitionEnd(this._element)\n }\n\n this._clearInterval()\n }\n\n cycle() {\n this._clearInterval()\n this._updateInterval()\n\n this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval)\n }\n\n _maybeEnableCycle() {\n if (!this._config.ride) {\n return\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.cycle())\n return\n }\n\n this.cycle()\n }\n\n to(index) {\n const items = this._getItems()\n if (index > items.length - 1 || index < 0) {\n return\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.to(index))\n return\n }\n\n const activeIndex = this._getItemIndex(this._getActive())\n if (activeIndex === index) {\n return\n }\n\n const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV\n\n this._slide(order, items[index])\n }\n\n dispose() {\n if (this._swipeHelper) {\n this._swipeHelper.dispose()\n }\n\n super.dispose()\n }\n\n // Private\n _configAfterMerge(config) {\n config.defaultInterval = config.interval\n return config\n }\n\n _addEventListeners() {\n if (this._config.keyboard) {\n EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event))\n }\n\n if (this._config.pause === 'hover') {\n EventHandler.on(this._element, EVENT_MOUSEENTER, () => this.pause())\n EventHandler.on(this._element, EVENT_MOUSELEAVE, () => this._maybeEnableCycle())\n }\n\n if (this._config.touch && Swipe.isSupported()) {\n this._addTouchEventListeners()\n }\n }\n\n _addTouchEventListeners() {\n for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault())\n }\n\n const endCallBack = () => {\n if (this._config.pause !== 'hover') {\n return\n }\n\n // If it's a touch-enabled device, mouseenter/leave are fired as\n // part of the mouse compatibility events on first tap - the carousel\n // would stop cycling until user tapped out of it;\n // here, we listen for touchend, explicitly pause the carousel\n // (as if it's the second time we tap on it, mouseenter compat event\n // is NOT fired) and after a timeout (to allow for mouse compatibility\n // events to fire) we explicitly restart cycling\n\n this.pause()\n if (this.touchTimeout) {\n clearTimeout(this.touchTimeout)\n }\n\n this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval)\n }\n\n const swipeConfig = {\n leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n endCallback: endCallBack\n }\n\n this._swipeHelper = new Swipe(this._element, swipeConfig)\n }\n\n _keydown(event) {\n if (/input|textarea/i.test(event.target.tagName)) {\n return\n }\n\n const direction = KEY_TO_DIRECTION[event.key]\n if (direction) {\n event.preventDefault()\n this._slide(this._directionToOrder(direction))\n }\n }\n\n _getItemIndex(element) {\n return this._getItems().indexOf(element)\n }\n\n _setActiveIndicatorElement(index) {\n if (!this._indicatorsElement) {\n return\n }\n\n const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement)\n\n activeIndicator.classList.remove(CLASS_NAME_ACTIVE)\n activeIndicator.removeAttribute('aria-current')\n\n const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement)\n\n if (newActiveIndicator) {\n newActiveIndicator.classList.add(CLASS_NAME_ACTIVE)\n newActiveIndicator.setAttribute('aria-current', 'true')\n }\n }\n\n _updateInterval() {\n const element = this._activeElement || this._getActive()\n\n if (!element) {\n return\n }\n\n const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10)\n\n this._config.interval = elementInterval || this._config.defaultInterval\n }\n\n _slide(order, element = null) {\n if (this._isSliding) {\n return\n }\n\n const activeElement = this._getActive()\n const isNext = order === ORDER_NEXT\n const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap)\n\n if (nextElement === activeElement) {\n return\n }\n\n const nextElementIndex = this._getItemIndex(nextElement)\n\n const triggerEvent = eventName => {\n return EventHandler.trigger(this._element, eventName, {\n relatedTarget: nextElement,\n direction: this._orderToDirection(order),\n from: this._getItemIndex(activeElement),\n to: nextElementIndex\n })\n }\n\n const slideEvent = triggerEvent(EVENT_SLIDE)\n\n if (slideEvent.defaultPrevented) {\n return\n }\n\n if (!activeElement || !nextElement) {\n // Some weirdness is happening, so we bail\n // TODO: change tests that use empty divs to avoid this check\n return\n }\n\n const isCycling = Boolean(this._interval)\n this.pause()\n\n this._isSliding = true\n\n this._setActiveIndicatorElement(nextElementIndex)\n this._activeElement = nextElement\n\n const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END\n const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV\n\n nextElement.classList.add(orderClassName)\n\n reflow(nextElement)\n\n activeElement.classList.add(directionalClassName)\n nextElement.classList.add(directionalClassName)\n\n const completeCallBack = () => {\n nextElement.classList.remove(directionalClassName, orderClassName)\n nextElement.classList.add(CLASS_NAME_ACTIVE)\n\n activeElement.classList.remove(CLASS_NAME_ACTIVE, orderClassName, directionalClassName)\n\n this._isSliding = false\n\n triggerEvent(EVENT_SLID)\n }\n\n this._queueCallback(completeCallBack, activeElement, this._isAnimated())\n\n if (isCycling) {\n this.cycle()\n }\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_SLIDE)\n }\n\n _getActive() {\n return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element)\n }\n\n _getItems() {\n return SelectorEngine.find(SELECTOR_ITEM, this._element)\n }\n\n _clearInterval() {\n if (this._interval) {\n clearInterval(this._interval)\n this._interval = null\n }\n }\n\n _directionToOrder(direction) {\n if (isRTL()) {\n return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT\n }\n\n return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV\n }\n\n _orderToDirection(order) {\n if (isRTL()) {\n return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT\n }\n\n return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Carousel.getOrCreateInstance(this, config)\n\n if (typeof config === 'number') {\n data.to(config)\n return\n }\n\n if (typeof config === 'string') {\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n }\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_SLIDE, function (event) {\n const target = SelectorEngine.getElementFromSelector(this)\n\n if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n return\n }\n\n event.preventDefault()\n\n const carousel = Carousel.getOrCreateInstance(target)\n const slideIndex = this.getAttribute('data-bs-slide-to')\n\n if (slideIndex) {\n carousel.to(slideIndex)\n carousel._maybeEnableCycle()\n return\n }\n\n if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n carousel.next()\n carousel._maybeEnableCycle()\n return\n }\n\n carousel.prev()\n carousel._maybeEnableCycle()\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE)\n\n for (const carousel of carousels) {\n Carousel.getOrCreateInstance(carousel)\n }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel)\n\nexport default Carousel\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n defineJQueryPlugin,\n getElement,\n reflow\n} from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'collapse'\nconst DATA_KEY = 'bs.collapse'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_COLLAPSE = 'collapse'\nconst CLASS_NAME_COLLAPSING = 'collapsing'\nconst CLASS_NAME_COLLAPSED = 'collapsed'\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal'\n\nconst WIDTH = 'width'\nconst HEIGHT = 'height'\n\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"collapse\"]'\n\nconst Default = {\n parent: null,\n toggle: true\n}\n\nconst DefaultType = {\n parent: '(null|element)',\n toggle: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._isTransitioning = false\n this._triggerArray = []\n\n const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE)\n\n for (const elem of toggleList) {\n const selector = SelectorEngine.getSelectorFromElement(elem)\n const filterElement = SelectorEngine.find(selector)\n .filter(foundElement => foundElement === this._element)\n\n if (selector !== null && filterElement.length) {\n this._triggerArray.push(elem)\n }\n }\n\n this._initializeChildren()\n\n if (!this._config.parent) {\n this._addAriaAndCollapsedClass(this._triggerArray, this._isShown())\n }\n\n if (this._config.toggle) {\n this.toggle()\n }\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle() {\n if (this._isShown()) {\n this.hide()\n } else {\n this.show()\n }\n }\n\n show() {\n if (this._isTransitioning || this._isShown()) {\n return\n }\n\n let activeChildren = []\n\n // find active children\n if (this._config.parent) {\n activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES)\n .filter(element => element !== this._element)\n .map(element => Collapse.getOrCreateInstance(element, { toggle: false }))\n }\n\n if (activeChildren.length && activeChildren[0]._isTransitioning) {\n return\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_SHOW)\n if (startEvent.defaultPrevented) {\n return\n }\n\n for (const activeInstance of activeChildren) {\n activeInstance.hide()\n }\n\n const dimension = this._getDimension()\n\n this._element.classList.remove(CLASS_NAME_COLLAPSE)\n this._element.classList.add(CLASS_NAME_COLLAPSING)\n\n this._element.style[dimension] = 0\n\n this._addAriaAndCollapsedClass(this._triggerArray, true)\n this._isTransitioning = true\n\n const complete = () => {\n this._isTransitioning = false\n\n this._element.classList.remove(CLASS_NAME_COLLAPSING)\n this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n this._element.style[dimension] = ''\n\n EventHandler.trigger(this._element, EVENT_SHOWN)\n }\n\n const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1)\n const scrollSize = `scroll${capitalizedDimension}`\n\n this._queueCallback(complete, this._element, true)\n this._element.style[dimension] = `${this._element[scrollSize]}px`\n }\n\n hide() {\n if (this._isTransitioning || !this._isShown()) {\n return\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n if (startEvent.defaultPrevented) {\n return\n }\n\n const dimension = this._getDimension()\n\n this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`\n\n reflow(this._element)\n\n this._element.classList.add(CLASS_NAME_COLLAPSING)\n this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n for (const trigger of this._triggerArray) {\n const element = SelectorEngine.getElementFromSelector(trigger)\n\n if (element && !this._isShown(element)) {\n this._addAriaAndCollapsedClass([trigger], false)\n }\n }\n\n this._isTransitioning = true\n\n const complete = () => {\n this._isTransitioning = false\n this._element.classList.remove(CLASS_NAME_COLLAPSING)\n this._element.classList.add(CLASS_NAME_COLLAPSE)\n EventHandler.trigger(this._element, EVENT_HIDDEN)\n }\n\n this._element.style[dimension] = ''\n\n this._queueCallback(complete, this._element, true)\n }\n\n _isShown(element = this._element) {\n return element.classList.contains(CLASS_NAME_SHOW)\n }\n\n // Private\n _configAfterMerge(config) {\n config.toggle = Boolean(config.toggle) // Coerce string values\n config.parent = getElement(config.parent)\n return config\n }\n\n _getDimension() {\n return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT\n }\n\n _initializeChildren() {\n if (!this._config.parent) {\n return\n }\n\n const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE)\n\n for (const element of children) {\n const selected = SelectorEngine.getElementFromSelector(element)\n\n if (selected) {\n this._addAriaAndCollapsedClass([element], this._isShown(selected))\n }\n }\n }\n\n _getFirstLevelChildren(selector) {\n const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent)\n // remove children if greater depth\n return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element))\n }\n\n _addAriaAndCollapsedClass(triggerArray, isOpen) {\n if (!triggerArray.length) {\n return\n }\n\n for (const element of triggerArray) {\n element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen)\n element.setAttribute('aria-expanded', isOpen)\n }\n }\n\n // Static\n static jQueryInterface(config) {\n const _config = {}\n if (typeof config === 'string' && /show|hide/.test(config)) {\n _config.toggle = false\n }\n\n return this.each(function () {\n const data = Collapse.getOrCreateInstance(this, _config)\n\n if (typeof config === 'string') {\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n }\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n // preventDefault only for elements (which change the URL) not inside the collapsible element\n if (event.target.tagName === 'A' || (event.delegateTarget && event.delegateTarget.tagName === 'A')) {\n event.preventDefault()\n }\n\n for (const element of SelectorEngine.getMultipleElementsFromSelector(this)) {\n Collapse.getOrCreateInstance(element, { toggle: false }).toggle()\n }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse)\n\nexport default Collapse\n","export var top = 'top';\nexport var bottom = 'bottom';\nexport var right = 'right';\nexport var left = 'left';\nexport var auto = 'auto';\nexport var basePlacements = [top, bottom, right, left];\nexport var start = 'start';\nexport var end = 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n if (node == null) {\n return window;\n }\n\n if (node.toString() !== '[object Window]') {\n var ownerDocument = node.ownerDocument;\n return ownerDocument ? ownerDocument.defaultView || window : window;\n }\n\n return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n var OwnElement = getWindow(node).Element;\n return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n var OwnElement = getWindow(node).HTMLElement;\n return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n // IE 11 has no ShadowRoot\n if (typeof ShadowRoot === 'undefined') {\n return false;\n }\n\n var OwnElement = getWindow(node).ShadowRoot;\n return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n var state = _ref.state;\n Object.keys(state.elements).forEach(function (name) {\n var style = state.styles[name] || {};\n var attributes = state.attributes[name] || {};\n var element = state.elements[name]; // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n } // Flow doesn't support to extend this property, but it's the most\n // effective way to apply styles to an HTMLElement\n // $FlowFixMe[cannot-write]\n\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (name) {\n var value = attributes[name];\n\n if (value === false) {\n element.removeAttribute(name);\n } else {\n element.setAttribute(name, value === true ? '' : value);\n }\n });\n });\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state;\n var initialStyles = {\n popper: {\n position: state.options.strategy,\n left: '0',\n top: '0',\n margin: '0'\n },\n arrow: {\n position: 'absolute'\n },\n reference: {}\n };\n Object.assign(state.elements.popper.style, initialStyles.popper);\n state.styles = initialStyles;\n\n if (state.elements.arrow) {\n Object.assign(state.elements.arrow.style, initialStyles.arrow);\n }\n\n return function () {\n Object.keys(state.elements).forEach(function (name) {\n var element = state.elements[name];\n var attributes = state.attributes[name] || {};\n var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n var style = styleProperties.reduce(function (style, property) {\n style[property] = '';\n return style;\n }, {}); // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n }\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (attribute) {\n element.removeAttribute(attribute);\n });\n });\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'applyStyles',\n enabled: true,\n phase: 'write',\n fn: applyStyles,\n effect: effect,\n requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n var uaData = navigator.userAgentData;\n\n if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n return uaData.brands.map(function (item) {\n return item.brand + \"/\" + item.version;\n }).join(' ');\n }\n\n return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n if (includeScale === void 0) {\n includeScale = false;\n }\n\n if (isFixedStrategy === void 0) {\n isFixedStrategy = false;\n }\n\n var clientRect = element.getBoundingClientRect();\n var scaleX = 1;\n var scaleY = 1;\n\n if (includeScale && isHTMLElement(element)) {\n scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n }\n\n var _ref = isElement(element) ? getWindow(element) : window,\n visualViewport = _ref.visualViewport;\n\n var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n var width = clientRect.width / scaleX;\n var height = clientRect.height / scaleY;\n return {\n width: width,\n height: height,\n top: y,\n right: x + width,\n bottom: y + height,\n left: x,\n x: x,\n y: y\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n var width = element.offsetWidth;\n var height = element.offsetHeight;\n\n if (Math.abs(clientRect.width - width) <= 1) {\n width = clientRect.width;\n }\n\n if (Math.abs(clientRect.height - height) <= 1) {\n height = clientRect.height;\n }\n\n return {\n x: element.offsetLeft,\n y: element.offsetTop,\n width: width,\n height: height\n };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n if (parent.contains(child)) {\n return true;\n } // then fallback to custom implementation with Shadow DOM support\n else if (rootNode && isShadowRoot(rootNode)) {\n var next = child;\n\n do {\n if (next && parent.isSameNode(next)) {\n return true;\n } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n next = next.parentNode || next.host;\n } while (next);\n } // Give up, the result is false\n\n\n return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n if (getNodeName(element) === 'html') {\n return element;\n }\n\n return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n // $FlowFixMe[incompatible-return]\n // $FlowFixMe[prop-missing]\n element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n element.parentNode || ( // DOM Element detected\n isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n getDocumentElement(element) // fallback\n\n );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n getComputedStyle(element).position === 'fixed') {\n return null;\n }\n\n return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n var isFirefox = /firefox/i.test(getUAString());\n var isIE = /Trident/i.test(getUAString());\n\n if (isIE && isHTMLElement(element)) {\n // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n var elementCss = getComputedStyle(element);\n\n if (elementCss.position === 'fixed') {\n return null;\n }\n }\n\n var currentNode = getParentNode(element);\n\n if (isShadowRoot(currentNode)) {\n currentNode = currentNode.host;\n }\n\n while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n // create a containing block.\n // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n return currentNode;\n } else {\n currentNode = currentNode.parentNode;\n }\n }\n\n return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n var window = getWindow(element);\n var offsetParent = getTrueOffsetParent(element);\n\n while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n offsetParent = getTrueOffsetParent(offsetParent);\n }\n\n if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n return window;\n }\n\n return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n var v = within(min, value, max);\n return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n return {\n top: 0,\n right: 0,\n bottom: 0,\n left: 0\n };\n}","export default function expandToHashMap(value, keys) {\n return keys.reduce(function (hashMap, key) {\n hashMap[key] = value;\n return hashMap;\n }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n placement: state.placement\n })) : padding;\n return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n var _state$modifiersData$;\n\n var state = _ref.state,\n name = _ref.name,\n options = _ref.options;\n var arrowElement = state.elements.arrow;\n var popperOffsets = state.modifiersData.popperOffsets;\n var basePlacement = getBasePlacement(state.placement);\n var axis = getMainAxisFromPlacement(basePlacement);\n var isVertical = [left, right].indexOf(basePlacement) >= 0;\n var len = isVertical ? 'height' : 'width';\n\n if (!arrowElement || !popperOffsets) {\n return;\n }\n\n var paddingObject = toPaddingObject(options.padding, state);\n var arrowRect = getLayoutRect(arrowElement);\n var minProp = axis === 'y' ? top : left;\n var maxProp = axis === 'y' ? bottom : right;\n var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n var arrowOffsetParent = getOffsetParent(arrowElement);\n var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n // outside of the popper bounds\n\n var min = paddingObject[minProp];\n var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n var axisProp = axis;\n state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state,\n options = _ref2.options;\n var _options$element = options.element,\n arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n if (arrowElement == null) {\n return;\n } // CSS selector\n\n\n if (typeof arrowElement === 'string') {\n arrowElement = state.elements.popper.querySelector(arrowElement);\n\n if (!arrowElement) {\n return;\n }\n }\n\n if (!contains(state.elements.popper, arrowElement)) {\n return;\n }\n\n state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'arrow',\n enabled: true,\n phase: 'main',\n fn: arrow,\n effect: effect,\n requires: ['popperOffsets'],\n requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n top: 'auto',\n right: 'auto',\n bottom: 'auto',\n left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n var x = _ref.x,\n y = _ref.y;\n var dpr = win.devicePixelRatio || 1;\n return {\n x: round(x * dpr) / dpr || 0,\n y: round(y * dpr) / dpr || 0\n };\n}\n\nexport function mapToStyles(_ref2) {\n var _Object$assign2;\n\n var popper = _ref2.popper,\n popperRect = _ref2.popperRect,\n placement = _ref2.placement,\n variation = _ref2.variation,\n offsets = _ref2.offsets,\n position = _ref2.position,\n gpuAcceleration = _ref2.gpuAcceleration,\n adaptive = _ref2.adaptive,\n roundOffsets = _ref2.roundOffsets,\n isFixed = _ref2.isFixed;\n var _offsets$x = offsets.x,\n x = _offsets$x === void 0 ? 0 : _offsets$x,\n _offsets$y = offsets.y,\n y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n x: x,\n y: y\n }) : {\n x: x,\n y: y\n };\n\n x = _ref3.x;\n y = _ref3.y;\n var hasX = offsets.hasOwnProperty('x');\n var hasY = offsets.hasOwnProperty('y');\n var sideX = left;\n var sideY = top;\n var win = window;\n\n if (adaptive) {\n var offsetParent = getOffsetParent(popper);\n var heightProp = 'clientHeight';\n var widthProp = 'clientWidth';\n\n if (offsetParent === getWindow(popper)) {\n offsetParent = getDocumentElement(popper);\n\n if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n heightProp = 'scrollHeight';\n widthProp = 'scrollWidth';\n }\n } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n offsetParent = offsetParent;\n\n if (placement === top || (placement === left || placement === right) && variation === end) {\n sideY = bottom;\n var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n offsetParent[heightProp];\n y -= offsetY - popperRect.height;\n y *= gpuAcceleration ? 1 : -1;\n }\n\n if (placement === left || (placement === top || placement === bottom) && variation === end) {\n sideX = right;\n var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n offsetParent[widthProp];\n x -= offsetX - popperRect.width;\n x *= gpuAcceleration ? 1 : -1;\n }\n }\n\n var commonStyles = Object.assign({\n position: position\n }, adaptive && unsetSides);\n\n var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n x: x,\n y: y\n }, getWindow(popper)) : {\n x: x,\n y: y\n };\n\n x = _ref4.x;\n y = _ref4.y;\n\n if (gpuAcceleration) {\n var _Object$assign;\n\n return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n }\n\n return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n var state = _ref5.state,\n options = _ref5.options;\n var _options$gpuAccelerat = options.gpuAcceleration,\n gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n _options$adaptive = options.adaptive,\n adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n _options$roundOffsets = options.roundOffsets,\n roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n var commonStyles = {\n placement: getBasePlacement(state.placement),\n variation: getVariation(state.placement),\n popper: state.elements.popper,\n popperRect: state.rects.popper,\n gpuAcceleration: gpuAcceleration,\n isFixed: state.options.strategy === 'fixed'\n };\n\n if (state.modifiersData.popperOffsets != null) {\n state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.popperOffsets,\n position: state.options.strategy,\n adaptive: adaptive,\n roundOffsets: roundOffsets\n })));\n }\n\n if (state.modifiersData.arrow != null) {\n state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.arrow,\n position: 'absolute',\n adaptive: false,\n roundOffsets: roundOffsets\n })));\n }\n\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-placement': state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'computeStyles',\n enabled: true,\n phase: 'beforeWrite',\n fn: computeStyles,\n data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n passive: true\n};\n\nfunction effect(_ref) {\n var state = _ref.state,\n instance = _ref.instance,\n options = _ref.options;\n var _options$scroll = options.scroll,\n scroll = _options$scroll === void 0 ? true : _options$scroll,\n _options$resize = options.resize,\n resize = _options$resize === void 0 ? true : _options$resize;\n var window = getWindow(state.elements.popper);\n var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.addEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.addEventListener('resize', instance.update, passive);\n }\n\n return function () {\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.removeEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.removeEventListener('resize', instance.update, passive);\n }\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'eventListeners',\n enabled: true,\n phase: 'write',\n fn: function fn() {},\n effect: effect,\n data: {}\n};","var hash = {\n left: 'right',\n right: 'left',\n bottom: 'top',\n top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n return placement.replace(/left|right|bottom|top/g, function (matched) {\n return hash[matched];\n });\n}","var hash = {\n start: 'end',\n end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n return placement.replace(/start|end/g, function (matched) {\n return hash[matched];\n });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n var win = getWindow(node);\n var scrollLeft = win.pageXOffset;\n var scrollTop = win.pageYOffset;\n return {\n scrollLeft: scrollLeft,\n scrollTop: scrollTop\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n // If has a CSS width greater than the viewport, then this will be\n // incorrect for RTL.\n // Popper 1 is broken in this case and never had a bug report so let's assume\n // it's not an issue. I don't think anyone ever specifies width on \n // anyway.\n // Browsers where the left scrollbar doesn't cause an issue report `0` for\n // this (e.g. Edge 2019, IE11, Safari)\n return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n // Firefox wants us to check `-x` and `-y` variations as well\n var _getComputedStyle = getComputedStyle(element),\n overflow = _getComputedStyle.overflow,\n overflowX = _getComputedStyle.overflowX,\n overflowY = _getComputedStyle.overflowY;\n\n return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return node.ownerDocument.body;\n }\n\n if (isHTMLElement(node) && isScrollParent(node)) {\n return node;\n }\n\n return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n var _element$ownerDocumen;\n\n if (list === void 0) {\n list = [];\n }\n\n var scrollParent = getScrollParent(element);\n var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n var win = getWindow(scrollParent);\n var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n var updatedList = list.concat(target);\n return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n return Object.assign({}, rect, {\n left: rect.x,\n top: rect.y,\n right: rect.x + rect.width,\n bottom: rect.y + rect.height\n });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n rect.top = rect.top + element.clientTop;\n rect.left = rect.left + element.clientLeft;\n rect.bottom = rect.top + element.clientHeight;\n rect.right = rect.left + element.clientWidth;\n rect.width = element.clientWidth;\n rect.height = element.clientHeight;\n rect.x = rect.left;\n rect.y = rect.top;\n return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n var clippingParents = listScrollParents(getParentNode(element));\n var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n if (!isElement(clipperElement)) {\n return [];\n } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n return clippingParents.filter(function (clippingParent) {\n return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n var firstClippingParent = clippingParents[0];\n var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n accRect.top = max(rect.top, accRect.top);\n accRect.right = min(rect.right, accRect.right);\n accRect.bottom = min(rect.bottom, accRect.bottom);\n accRect.left = max(rect.left, accRect.left);\n return accRect;\n }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n clippingRect.width = clippingRect.right - clippingRect.left;\n clippingRect.height = clippingRect.bottom - clippingRect.top;\n clippingRect.x = clippingRect.left;\n clippingRect.y = clippingRect.top;\n return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n var win = getWindow(element);\n var html = getDocumentElement(element);\n var visualViewport = win.visualViewport;\n var width = html.clientWidth;\n var height = html.clientHeight;\n var x = 0;\n var y = 0;\n\n if (visualViewport) {\n width = visualViewport.width;\n height = visualViewport.height;\n var layoutViewport = isLayoutViewport();\n\n if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n x = visualViewport.offsetLeft;\n y = visualViewport.offsetTop;\n }\n }\n\n return {\n width: width,\n height: height,\n x: x + getWindowScrollBarX(element),\n y: y\n };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `` and `` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n var _element$ownerDocumen;\n\n var html = getDocumentElement(element);\n var winScroll = getWindowScroll(element);\n var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n var y = -winScroll.scrollTop;\n\n if (getComputedStyle(body || html).direction === 'rtl') {\n x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n }\n\n return {\n width: width,\n height: height,\n x: x,\n y: y\n };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n var reference = _ref.reference,\n element = _ref.element,\n placement = _ref.placement;\n var basePlacement = placement ? getBasePlacement(placement) : null;\n var variation = placement ? getVariation(placement) : null;\n var commonX = reference.x + reference.width / 2 - element.width / 2;\n var commonY = reference.y + reference.height / 2 - element.height / 2;\n var offsets;\n\n switch (basePlacement) {\n case top:\n offsets = {\n x: commonX,\n y: reference.y - element.height\n };\n break;\n\n case bottom:\n offsets = {\n x: commonX,\n y: reference.y + reference.height\n };\n break;\n\n case right:\n offsets = {\n x: reference.x + reference.width,\n y: commonY\n };\n break;\n\n case left:\n offsets = {\n x: reference.x - element.width,\n y: commonY\n };\n break;\n\n default:\n offsets = {\n x: reference.x,\n y: reference.y\n };\n }\n\n var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n if (mainAxis != null) {\n var len = mainAxis === 'y' ? 'height' : 'width';\n\n switch (variation) {\n case start:\n offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n break;\n\n case end:\n offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n break;\n\n default:\n }\n }\n\n return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n _options$placement = _options.placement,\n placement = _options$placement === void 0 ? state.placement : _options$placement,\n _options$strategy = _options.strategy,\n strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n _options$boundary = _options.boundary,\n boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n _options$rootBoundary = _options.rootBoundary,\n rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n _options$elementConte = _options.elementContext,\n elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n _options$altBoundary = _options.altBoundary,\n altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n _options$padding = _options.padding,\n padding = _options$padding === void 0 ? 0 : _options$padding;\n var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n var altContext = elementContext === popper ? reference : popper;\n var popperRect = state.rects.popper;\n var element = state.elements[altBoundary ? altContext : elementContext];\n var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n var referenceClientRect = getBoundingClientRect(state.elements.reference);\n var popperOffsets = computeOffsets({\n reference: referenceClientRect,\n element: popperRect,\n strategy: 'absolute',\n placement: placement\n });\n var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n // 0 or negative = within the clipping rect\n\n var overflowOffsets = {\n top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n };\n var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n if (elementContext === popper && offsetData) {\n var offset = offsetData[placement];\n Object.keys(overflowOffsets).forEach(function (key) {\n var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n overflowOffsets[key] += offset[axis] * multiply;\n });\n }\n\n return overflowOffsets;\n}","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n placement = _options.placement,\n boundary = _options.boundary,\n rootBoundary = _options.rootBoundary,\n padding = _options.padding,\n flipVariations = _options.flipVariations,\n _options$allowedAutoP = _options.allowedAutoPlacements,\n allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n var variation = getVariation(placement);\n var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n return getVariation(placement) === variation;\n }) : basePlacements;\n var allowedPlacements = placements.filter(function (placement) {\n return allowedAutoPlacements.indexOf(placement) >= 0;\n });\n\n if (allowedPlacements.length === 0) {\n allowedPlacements = placements;\n } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n var overflows = allowedPlacements.reduce(function (acc, placement) {\n acc[placement] = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding\n })[getBasePlacement(placement)];\n return acc;\n }, {});\n return Object.keys(overflows).sort(function (a, b) {\n return overflows[a] - overflows[b];\n });\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n if (getBasePlacement(placement) === auto) {\n return [];\n }\n\n var oppositePlacement = getOppositePlacement(placement);\n return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n\n if (state.modifiersData[name]._skip) {\n return;\n }\n\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n specifiedFallbackPlacements = options.fallbackPlacements,\n padding = options.padding,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n _options$flipVariatio = options.flipVariations,\n flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n allowedAutoPlacements = options.allowedAutoPlacements;\n var preferredPlacement = state.options.placement;\n var basePlacement = getBasePlacement(preferredPlacement);\n var isBasePlacement = basePlacement === preferredPlacement;\n var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n flipVariations: flipVariations,\n allowedAutoPlacements: allowedAutoPlacements\n }) : placement);\n }, []);\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var checksMap = new Map();\n var makeFallbackChecks = true;\n var firstFittingPlacement = placements[0];\n\n for (var i = 0; i < placements.length; i++) {\n var placement = placements[i];\n\n var _basePlacement = getBasePlacement(placement);\n\n var isStartVariation = getVariation(placement) === start;\n var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n var len = isVertical ? 'width' : 'height';\n var overflow = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n altBoundary: altBoundary,\n padding: padding\n });\n var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n if (referenceRect[len] > popperRect[len]) {\n mainVariationSide = getOppositePlacement(mainVariationSide);\n }\n\n var altVariationSide = getOppositePlacement(mainVariationSide);\n var checks = [];\n\n if (checkMainAxis) {\n checks.push(overflow[_basePlacement] <= 0);\n }\n\n if (checkAltAxis) {\n checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n }\n\n if (checks.every(function (check) {\n return check;\n })) {\n firstFittingPlacement = placement;\n makeFallbackChecks = false;\n break;\n }\n\n checksMap.set(placement, checks);\n }\n\n if (makeFallbackChecks) {\n // `2` may be desired in some cases – research later\n var numberOfChecks = flipVariations ? 3 : 1;\n\n var _loop = function _loop(_i) {\n var fittingPlacement = placements.find(function (placement) {\n var checks = checksMap.get(placement);\n\n if (checks) {\n return checks.slice(0, _i).every(function (check) {\n return check;\n });\n }\n });\n\n if (fittingPlacement) {\n firstFittingPlacement = fittingPlacement;\n return \"break\";\n }\n };\n\n for (var _i = numberOfChecks; _i > 0; _i--) {\n var _ret = _loop(_i);\n\n if (_ret === \"break\") break;\n }\n }\n\n if (state.placement !== firstFittingPlacement) {\n state.modifiersData[name]._skip = true;\n state.placement = firstFittingPlacement;\n state.reset = true;\n }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'flip',\n enabled: true,\n phase: 'main',\n fn: flip,\n requiresIfExists: ['offset'],\n data: {\n _skip: false\n }\n};","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n if (preventedOffsets === void 0) {\n preventedOffsets = {\n x: 0,\n y: 0\n };\n }\n\n return {\n top: overflow.top - rect.height - preventedOffsets.y,\n right: overflow.right - rect.width + preventedOffsets.x,\n bottom: overflow.bottom - rect.height + preventedOffsets.y,\n left: overflow.left - rect.width - preventedOffsets.x\n };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n return [top, right, bottom, left].some(function (side) {\n return overflow[side] >= 0;\n });\n}\n\nfunction hide(_ref) {\n var state = _ref.state,\n name = _ref.name;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var preventedOffsets = state.modifiersData.preventOverflow;\n var referenceOverflow = detectOverflow(state, {\n elementContext: 'reference'\n });\n var popperAltOverflow = detectOverflow(state, {\n altBoundary: true\n });\n var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n state.modifiersData[name] = {\n referenceClippingOffsets: referenceClippingOffsets,\n popperEscapeOffsets: popperEscapeOffsets,\n isReferenceHidden: isReferenceHidden,\n hasPopperEscaped: hasPopperEscaped\n };\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-reference-hidden': isReferenceHidden,\n 'data-popper-escaped': hasPopperEscaped\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'hide',\n enabled: true,\n phase: 'main',\n requiresIfExists: ['preventOverflow'],\n fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n var basePlacement = getBasePlacement(placement);\n var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n placement: placement\n })) : offset,\n skidding = _ref[0],\n distance = _ref[1];\n\n skidding = skidding || 0;\n distance = (distance || 0) * invertDistance;\n return [left, right].indexOf(basePlacement) >= 0 ? {\n x: distance,\n y: skidding\n } : {\n x: skidding,\n y: distance\n };\n}\n\nfunction offset(_ref2) {\n var state = _ref2.state,\n options = _ref2.options,\n name = _ref2.name;\n var _options$offset = options.offset,\n offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n var data = placements.reduce(function (acc, placement) {\n acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n return acc;\n }, {});\n var _data$state$placement = data[state.placement],\n x = _data$state$placement.x,\n y = _data$state$placement.y;\n\n if (state.modifiersData.popperOffsets != null) {\n state.modifiersData.popperOffsets.x += x;\n state.modifiersData.popperOffsets.y += y;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'offset',\n enabled: true,\n phase: 'main',\n requires: ['popperOffsets'],\n fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n var state = _ref.state,\n name = _ref.name;\n // Offsets are the actual position the popper needs to have to be\n // properly positioned near its reference element\n // This is the most basic placement, and will be adjusted by\n // the modifiers in the next step\n state.modifiersData[name] = computeOffsets({\n reference: state.rects.reference,\n element: state.rects.popper,\n strategy: 'absolute',\n placement: state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'popperOffsets',\n enabled: true,\n phase: 'read',\n fn: popperOffsets,\n data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n padding = options.padding,\n _options$tether = options.tether,\n tether = _options$tether === void 0 ? true : _options$tether,\n _options$tetherOffset = options.tetherOffset,\n tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n var overflow = detectOverflow(state, {\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n altBoundary: altBoundary\n });\n var basePlacement = getBasePlacement(state.placement);\n var variation = getVariation(state.placement);\n var isBasePlacement = !variation;\n var mainAxis = getMainAxisFromPlacement(basePlacement);\n var altAxis = getAltAxis(mainAxis);\n var popperOffsets = state.modifiersData.popperOffsets;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n placement: state.placement\n })) : tetherOffset;\n var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n mainAxis: tetherOffsetValue,\n altAxis: tetherOffsetValue\n } : Object.assign({\n mainAxis: 0,\n altAxis: 0\n }, tetherOffsetValue);\n var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n var data = {\n x: 0,\n y: 0\n };\n\n if (!popperOffsets) {\n return;\n }\n\n if (checkMainAxis) {\n var _offsetModifierState$;\n\n var mainSide = mainAxis === 'y' ? top : left;\n var altSide = mainAxis === 'y' ? bottom : right;\n var len = mainAxis === 'y' ? 'height' : 'width';\n var offset = popperOffsets[mainAxis];\n var min = offset + overflow[mainSide];\n var max = offset - overflow[altSide];\n var additive = tether ? -popperRect[len] / 2 : 0;\n var minLen = variation === start ? referenceRect[len] : popperRect[len];\n var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n // outside the reference bounds\n\n var arrowElement = state.elements.arrow;\n var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n width: 0,\n height: 0\n };\n var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n var arrowPaddingMin = arrowPaddingObject[mainSide];\n var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n // to include its full size in the calculation. If the reference is small\n // and near the edge of a boundary, the popper can overflow even if the\n // reference is not overflowing as well (e.g. virtual elements with no\n // width or height)\n\n var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n var tetherMax = offset + maxOffset - offsetModifierValue;\n var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n popperOffsets[mainAxis] = preventedOffset;\n data[mainAxis] = preventedOffset - offset;\n }\n\n if (checkAltAxis) {\n var _offsetModifierState$2;\n\n var _mainSide = mainAxis === 'x' ? top : left;\n\n var _altSide = mainAxis === 'x' ? bottom : right;\n\n var _offset = popperOffsets[altAxis];\n\n var _len = altAxis === 'y' ? 'height' : 'width';\n\n var _min = _offset + overflow[_mainSide];\n\n var _max = _offset - overflow[_altSide];\n\n var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n popperOffsets[altAxis] = _preventedOffset;\n data[altAxis] = _preventedOffset - _offset;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'preventOverflow',\n enabled: true,\n phase: 'main',\n fn: preventOverflow,\n requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n var rect = element.getBoundingClientRect();\n var scaleX = round(rect.width) / element.offsetWidth || 1;\n var scaleY = round(rect.height) / element.offsetHeight || 1;\n return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n if (isFixed === void 0) {\n isFixed = false;\n }\n\n var isOffsetParentAnElement = isHTMLElement(offsetParent);\n var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n var documentElement = getDocumentElement(offsetParent);\n var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n var scroll = {\n scrollLeft: 0,\n scrollTop: 0\n };\n var offsets = {\n x: 0,\n y: 0\n };\n\n if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n isScrollParent(documentElement)) {\n scroll = getNodeScroll(offsetParent);\n }\n\n if (isHTMLElement(offsetParent)) {\n offsets = getBoundingClientRect(offsetParent, true);\n offsets.x += offsetParent.clientLeft;\n offsets.y += offsetParent.clientTop;\n } else if (documentElement) {\n offsets.x = getWindowScrollBarX(documentElement);\n }\n }\n\n return {\n x: rect.left + scroll.scrollLeft - offsets.x,\n y: rect.top + scroll.scrollTop - offsets.y,\n width: rect.width,\n height: rect.height\n };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n if (node === getWindow(node) || !isHTMLElement(node)) {\n return getWindowScroll(node);\n } else {\n return getHTMLElementScroll(node);\n }\n}","export default function getHTMLElementScroll(element) {\n return {\n scrollLeft: element.scrollLeft,\n scrollTop: element.scrollTop\n };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n var map = new Map();\n var visited = new Set();\n var result = [];\n modifiers.forEach(function (modifier) {\n map.set(modifier.name, modifier);\n }); // On visiting object, check for its dependencies and visit them recursively\n\n function sort(modifier) {\n visited.add(modifier.name);\n var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n requires.forEach(function (dep) {\n if (!visited.has(dep)) {\n var depModifier = map.get(dep);\n\n if (depModifier) {\n sort(depModifier);\n }\n }\n });\n result.push(modifier);\n }\n\n modifiers.forEach(function (modifier) {\n if (!visited.has(modifier.name)) {\n // check for visited object\n sort(modifier);\n }\n });\n return result;\n}\n\nexport default function orderModifiers(modifiers) {\n // order based on dependencies\n var orderedModifiers = order(modifiers); // order based on phase\n\n return modifierPhases.reduce(function (acc, phase) {\n return acc.concat(orderedModifiers.filter(function (modifier) {\n return modifier.phase === phase;\n }));\n }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nvar DEFAULT_OPTIONS = {\n placement: 'bottom',\n modifiers: [],\n strategy: 'absolute'\n};\n\nfunction areValidElements() {\n for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n args[_key] = arguments[_key];\n }\n\n return !args.some(function (element) {\n return !(element && typeof element.getBoundingClientRect === 'function');\n });\n}\n\nexport function popperGenerator(generatorOptions) {\n if (generatorOptions === void 0) {\n generatorOptions = {};\n }\n\n var _generatorOptions = generatorOptions,\n _generatorOptions$def = _generatorOptions.defaultModifiers,\n defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n _generatorOptions$def2 = _generatorOptions.defaultOptions,\n defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n return function createPopper(reference, popper, options) {\n if (options === void 0) {\n options = defaultOptions;\n }\n\n var state = {\n placement: 'bottom',\n orderedModifiers: [],\n options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n modifiersData: {},\n elements: {\n reference: reference,\n popper: popper\n },\n attributes: {},\n styles: {}\n };\n var effectCleanupFns = [];\n var isDestroyed = false;\n var instance = {\n state: state,\n setOptions: function setOptions(setOptionsAction) {\n var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n cleanupModifierEffects();\n state.options = Object.assign({}, defaultOptions, state.options, options);\n state.scrollParents = {\n reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n popper: listScrollParents(popper)\n }; // Orders the modifiers based on their dependencies and `phase`\n // properties\n\n var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n state.orderedModifiers = orderedModifiers.filter(function (m) {\n return m.enabled;\n });\n runModifierEffects();\n return instance.update();\n },\n // Sync update – it will always be executed, even if not necessary. This\n // is useful for low frequency updates where sync behavior simplifies the\n // logic.\n // For high frequency updates (e.g. `resize` and `scroll` events), always\n // prefer the async Popper#update method\n forceUpdate: function forceUpdate() {\n if (isDestroyed) {\n return;\n }\n\n var _state$elements = state.elements,\n reference = _state$elements.reference,\n popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n // anymore\n\n if (!areValidElements(reference, popper)) {\n return;\n } // Store the reference and popper rects to be read by modifiers\n\n\n state.rects = {\n reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n popper: getLayoutRect(popper)\n }; // Modifiers have the ability to reset the current update cycle. The\n // most common use case for this is the `flip` modifier changing the\n // placement, which then needs to re-run all the modifiers, because the\n // logic was previously ran for the previous placement and is therefore\n // stale/incorrect\n\n state.reset = false;\n state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n // is filled with the initial data specified by the modifier. This means\n // it doesn't persist and is fresh on each update.\n // To ensure persistent data, use `${name}#persistent`\n\n state.orderedModifiers.forEach(function (modifier) {\n return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n });\n\n for (var index = 0; index < state.orderedModifiers.length; index++) {\n if (state.reset === true) {\n state.reset = false;\n index = -1;\n continue;\n }\n\n var _state$orderedModifie = state.orderedModifiers[index],\n fn = _state$orderedModifie.fn,\n _state$orderedModifie2 = _state$orderedModifie.options,\n _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n name = _state$orderedModifie.name;\n\n if (typeof fn === 'function') {\n state = fn({\n state: state,\n options: _options,\n name: name,\n instance: instance\n }) || state;\n }\n }\n },\n // Async and optimistically optimized update – it will not be executed if\n // not necessary (debounced to run at most once-per-tick)\n update: debounce(function () {\n return new Promise(function (resolve) {\n instance.forceUpdate();\n resolve(state);\n });\n }),\n destroy: function destroy() {\n cleanupModifierEffects();\n isDestroyed = true;\n }\n };\n\n if (!areValidElements(reference, popper)) {\n return instance;\n }\n\n instance.setOptions(options).then(function (state) {\n if (!isDestroyed && options.onFirstUpdate) {\n options.onFirstUpdate(state);\n }\n }); // Modifiers have the ability to execute arbitrary code before the first\n // update cycle runs. They will be executed in the same order as the update\n // cycle. This is useful when a modifier adds some persistent data that\n // other modifiers need to use, but the modifier is run after the dependent\n // one.\n\n function runModifierEffects() {\n state.orderedModifiers.forEach(function (_ref) {\n var name = _ref.name,\n _ref$options = _ref.options,\n options = _ref$options === void 0 ? {} : _ref$options,\n effect = _ref.effect;\n\n if (typeof effect === 'function') {\n var cleanupFn = effect({\n state: state,\n name: name,\n instance: instance,\n options: options\n });\n\n var noopFn = function noopFn() {};\n\n effectCleanupFns.push(cleanupFn || noopFn);\n }\n });\n }\n\n function cleanupModifierEffects() {\n effectCleanupFns.forEach(function (fn) {\n return fn();\n });\n effectCleanupFns = [];\n }\n\n return instance;\n };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n var pending;\n return function () {\n if (!pending) {\n pending = new Promise(function (resolve) {\n Promise.resolve().then(function () {\n pending = undefined;\n resolve(fn());\n });\n });\n }\n\n return pending;\n };\n}","export default function mergeByName(modifiers) {\n var merged = modifiers.reduce(function (merged, current) {\n var existing = merged[current.name];\n merged[current.name] = existing ? Object.assign({}, existing, current, {\n options: Object.assign({}, existing.options, current.options),\n data: Object.assign({}, existing.data, current.data)\n }) : current;\n return merged;\n }, {}); // IE11 does not support Object.values\n\n return Object.keys(merged).map(function (key) {\n return merged[key];\n });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","/**\n * --------------------------------------------------------------------------\n * Bootstrap dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n defineJQueryPlugin,\n execute,\n getElement,\n getNextActiveElement,\n isDisabled,\n isElement,\n isRTL,\n isVisible,\n noop\n} from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'dropdown'\nconst DATA_KEY = 'bs.dropdown'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ESCAPE_KEY = 'Escape'\nconst TAB_KEY = 'Tab'\nconst ARROW_UP_KEY = 'ArrowUp'\nconst ARROW_DOWN_KEY = 'ArrowDown'\nconst RIGHT_MOUSE_BUTTON = 2 // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_DROPUP = 'dropup'\nconst CLASS_NAME_DROPEND = 'dropend'\nconst CLASS_NAME_DROPSTART = 'dropstart'\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center'\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center'\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)'\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE}.${CLASS_NAME_SHOW}`\nconst SELECTOR_MENU = '.dropdown-menu'\nconst SELECTOR_NAVBAR = '.navbar'\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav'\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)'\n\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start'\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end'\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start'\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end'\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start'\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start'\nconst PLACEMENT_TOPCENTER = 'top'\nconst PLACEMENT_BOTTOMCENTER = 'bottom'\n\nconst Default = {\n autoClose: true,\n boundary: 'clippingParents',\n display: 'dynamic',\n offset: [0, 2],\n popperConfig: null,\n reference: 'toggle'\n}\n\nconst DefaultType = {\n autoClose: '(boolean|string)',\n boundary: '(string|element)',\n display: 'string',\n offset: '(array|string|function)',\n popperConfig: '(null|object|function)',\n reference: '(string|element|object)'\n}\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._popper = null\n this._parent = this._element.parentNode // dropdown wrapper\n // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] ||\n SelectorEngine.prev(this._element, SELECTOR_MENU)[0] ||\n SelectorEngine.findOne(SELECTOR_MENU, this._parent)\n this._inNavbar = this._detectNavbar()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle() {\n return this._isShown() ? this.hide() : this.show()\n }\n\n show() {\n if (isDisabled(this._element) || this._isShown()) {\n return\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, relatedTarget)\n\n if (showEvent.defaultPrevented) {\n return\n }\n\n this._createPopper()\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop)\n }\n }\n\n this._element.focus()\n this._element.setAttribute('aria-expanded', true)\n\n this._menu.classList.add(CLASS_NAME_SHOW)\n this._element.classList.add(CLASS_NAME_SHOW)\n EventHandler.trigger(this._element, EVENT_SHOWN, relatedTarget)\n }\n\n hide() {\n if (isDisabled(this._element) || !this._isShown()) {\n return\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n }\n\n this._completeHide(relatedTarget)\n }\n\n dispose() {\n if (this._popper) {\n this._popper.destroy()\n }\n\n super.dispose()\n }\n\n update() {\n this._inNavbar = this._detectNavbar()\n if (this._popper) {\n this._popper.update()\n }\n }\n\n // Private\n _completeHide(relatedTarget) {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE, relatedTarget)\n if (hideEvent.defaultPrevented) {\n return\n }\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop)\n }\n }\n\n if (this._popper) {\n this._popper.destroy()\n }\n\n this._menu.classList.remove(CLASS_NAME_SHOW)\n this._element.classList.remove(CLASS_NAME_SHOW)\n this._element.setAttribute('aria-expanded', 'false')\n Manipulator.removeDataAttribute(this._menu, 'popper')\n EventHandler.trigger(this._element, EVENT_HIDDEN, relatedTarget)\n }\n\n _getConfig(config) {\n config = super._getConfig(config)\n\n if (typeof config.reference === 'object' && !isElement(config.reference) &&\n typeof config.reference.getBoundingClientRect !== 'function'\n ) {\n // Popper virtual elements require a getBoundingClientRect method\n throw new TypeError(`${NAME.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`)\n }\n\n return config\n }\n\n _createPopper() {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)')\n }\n\n let referenceElement = this._element\n\n if (this._config.reference === 'parent') {\n referenceElement = this._parent\n } else if (isElement(this._config.reference)) {\n referenceElement = getElement(this._config.reference)\n } else if (typeof this._config.reference === 'object') {\n referenceElement = this._config.reference\n }\n\n const popperConfig = this._getPopperConfig()\n this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig)\n }\n\n _isShown() {\n return this._menu.classList.contains(CLASS_NAME_SHOW)\n }\n\n _getPlacement() {\n const parentDropdown = this._parent\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n return PLACEMENT_RIGHT\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n return PLACEMENT_LEFT\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n return PLACEMENT_TOPCENTER\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n return PLACEMENT_BOTTOMCENTER\n }\n\n // We need to trim the value because custom properties can also include spaces\n const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end'\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP\n }\n\n return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM\n }\n\n _detectNavbar() {\n return this._element.closest(SELECTOR_NAVBAR) !== null\n }\n\n _getOffset() {\n const { offset } = this._config\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10))\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element)\n }\n\n return offset\n }\n\n _getPopperConfig() {\n const defaultBsPopperConfig = {\n placement: this._getPlacement(),\n modifiers: [{\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n },\n {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }]\n }\n\n // Disable Popper if we have a static display or Dropdown is in Navbar\n if (this._inNavbar || this._config.display === 'static') {\n Manipulator.setDataAttribute(this._menu, 'popper', 'static') // TODO: v6 remove\n defaultBsPopperConfig.modifiers = [{\n name: 'applyStyles',\n enabled: false\n }]\n }\n\n return {\n ...defaultBsPopperConfig,\n ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n }\n }\n\n _selectMenuItem({ key, target }) {\n const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element))\n\n if (!items.length) {\n return\n }\n\n // if target isn't included in items (e.g. when expanding the dropdown)\n // allow cycling to get the last item in case key equals ARROW_UP_KEY\n getNextActiveElement(items, target, key === ARROW_DOWN_KEY, !items.includes(target)).focus()\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Dropdown.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n })\n }\n\n static clearMenus(event) {\n if (event.button === RIGHT_MOUSE_BUTTON || (event.type === 'keyup' && event.key !== TAB_KEY)) {\n return\n }\n\n const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN)\n\n for (const toggle of openToggles) {\n const context = Dropdown.getInstance(toggle)\n if (!context || context._config.autoClose === false) {\n continue\n }\n\n const composedPath = event.composedPath()\n const isMenuTarget = composedPath.includes(context._menu)\n if (\n composedPath.includes(context._element) ||\n (context._config.autoClose === 'inside' && !isMenuTarget) ||\n (context._config.autoClose === 'outside' && isMenuTarget)\n ) {\n continue\n }\n\n // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n if (context._menu.contains(event.target) && ((event.type === 'keyup' && event.key === TAB_KEY) || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n continue\n }\n\n const relatedTarget = { relatedTarget: context._element }\n\n if (event.type === 'click') {\n relatedTarget.clickEvent = event\n }\n\n context._completeHide(relatedTarget)\n }\n }\n\n static dataApiKeydownHandler(event) {\n // If not an UP | DOWN | ESCAPE key => not a dropdown command\n // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n const isInput = /input|textarea/i.test(event.target.tagName)\n const isEscapeEvent = event.key === ESCAPE_KEY\n const isUpOrDownEvent = [ARROW_UP_KEY, ARROW_DOWN_KEY].includes(event.key)\n\n if (!isUpOrDownEvent && !isEscapeEvent) {\n return\n }\n\n if (isInput && !isEscapeEvent) {\n return\n }\n\n event.preventDefault()\n\n // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE) ?\n this :\n (SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE)[0] ||\n SelectorEngine.next(this, SELECTOR_DATA_TOGGLE)[0] ||\n SelectorEngine.findOne(SELECTOR_DATA_TOGGLE, event.delegateTarget.parentNode))\n\n const instance = Dropdown.getOrCreateInstance(getToggleButton)\n\n if (isUpOrDownEvent) {\n event.stopPropagation()\n instance.show()\n instance._selectMenuItem(event)\n return\n }\n\n if (instance._isShown()) { // else is escape and we check if it is shown\n event.stopPropagation()\n instance.hide()\n getToggleButton.focus()\n }\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_CLICK_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n event.preventDefault()\n Dropdown.getOrCreateInstance(this).toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown)\n\nexport default Dropdown\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport Config from './config.js'\nimport { execute, executeAfterTransition, getElement, reflow } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'backdrop'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME}`\n\nconst Default = {\n className: 'modal-backdrop',\n clickCallback: null,\n isAnimated: false,\n isVisible: true, // if false, we use the backdrop helper without adding any element to the dom\n rootElement: 'body' // give the choice to place backdrop under different elements\n}\n\nconst DefaultType = {\n className: 'string',\n clickCallback: '(function|null)',\n isAnimated: 'boolean',\n isVisible: 'boolean',\n rootElement: '(element|string)'\n}\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n constructor(config) {\n super()\n this._config = this._getConfig(config)\n this._isAppended = false\n this._element = null\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n show(callback) {\n if (!this._config.isVisible) {\n execute(callback)\n return\n }\n\n this._append()\n\n const element = this._getElement()\n if (this._config.isAnimated) {\n reflow(element)\n }\n\n element.classList.add(CLASS_NAME_SHOW)\n\n this._emulateAnimation(() => {\n execute(callback)\n })\n }\n\n hide(callback) {\n if (!this._config.isVisible) {\n execute(callback)\n return\n }\n\n this._getElement().classList.remove(CLASS_NAME_SHOW)\n\n this._emulateAnimation(() => {\n this.dispose()\n execute(callback)\n })\n }\n\n dispose() {\n if (!this._isAppended) {\n return\n }\n\n EventHandler.off(this._element, EVENT_MOUSEDOWN)\n\n this._element.remove()\n this._isAppended = false\n }\n\n // Private\n _getElement() {\n if (!this._element) {\n const backdrop = document.createElement('div')\n backdrop.className = this._config.className\n if (this._config.isAnimated) {\n backdrop.classList.add(CLASS_NAME_FADE)\n }\n\n this._element = backdrop\n }\n\n return this._element\n }\n\n _configAfterMerge(config) {\n // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n config.rootElement = getElement(config.rootElement)\n return config\n }\n\n _append() {\n if (this._isAppended) {\n return\n }\n\n const element = this._getElement()\n this._config.rootElement.append(element)\n\n EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n execute(this._config.clickCallback)\n })\n\n this._isAppended = true\n }\n\n _emulateAnimation(callback) {\n executeAfterTransition(callback, this._getElement(), this._config.isAnimated)\n }\n}\n\nexport default Backdrop\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport Config from './config.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'focustrap'\nconst DATA_KEY = 'bs.focustrap'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY}`\n\nconst TAB_KEY = 'Tab'\nconst TAB_NAV_FORWARD = 'forward'\nconst TAB_NAV_BACKWARD = 'backward'\n\nconst Default = {\n autofocus: true,\n trapElement: null // The element to trap focus inside of\n}\n\nconst DefaultType = {\n autofocus: 'boolean',\n trapElement: 'element'\n}\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n constructor(config) {\n super()\n this._config = this._getConfig(config)\n this._isActive = false\n this._lastTabNavDirection = null\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n activate() {\n if (this._isActive) {\n return\n }\n\n if (this._config.autofocus) {\n this._config.trapElement.focus()\n }\n\n EventHandler.off(document, EVENT_KEY) // guard against infinite focus loop\n EventHandler.on(document, EVENT_FOCUSIN, event => this._handleFocusin(event))\n EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event))\n\n this._isActive = true\n }\n\n deactivate() {\n if (!this._isActive) {\n return\n }\n\n this._isActive = false\n EventHandler.off(document, EVENT_KEY)\n }\n\n // Private\n _handleFocusin(event) {\n const { trapElement } = this._config\n\n if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n return\n }\n\n const elements = SelectorEngine.focusableChildren(trapElement)\n\n if (elements.length === 0) {\n trapElement.focus()\n } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n elements[elements.length - 1].focus()\n } else {\n elements[0].focus()\n }\n }\n\n _handleKeydown(event) {\n if (event.key !== TAB_KEY) {\n return\n }\n\n this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD\n }\n}\n\nexport default FocusTrap\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Manipulator from '../dom/manipulator.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport { isElement } from './index.js'\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top'\nconst SELECTOR_STICKY_CONTENT = '.sticky-top'\nconst PROPERTY_PADDING = 'padding-right'\nconst PROPERTY_MARGIN = 'margin-right'\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n constructor() {\n this._element = document.body\n }\n\n // Public\n getWidth() {\n // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n const documentWidth = document.documentElement.clientWidth\n return Math.abs(window.innerWidth - documentWidth)\n }\n\n hide() {\n const width = this.getWidth()\n this._disableOverFlow()\n // give padding to element to balance the hidden scrollbar width\n this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width)\n }\n\n reset() {\n this._resetElementAttributes(this._element, 'overflow')\n this._resetElementAttributes(this._element, PROPERTY_PADDING)\n this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING)\n this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN)\n }\n\n isOverflowing() {\n return this.getWidth() > 0\n }\n\n // Private\n _disableOverFlow() {\n this._saveInitialAttribute(this._element, 'overflow')\n this._element.style.overflow = 'hidden'\n }\n\n _setElementAttributes(selector, styleProperty, callback) {\n const scrollbarWidth = this.getWidth()\n const manipulationCallBack = element => {\n if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n return\n }\n\n this._saveInitialAttribute(element, styleProperty)\n const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty)\n element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`)\n }\n\n this._applyManipulationCallback(selector, manipulationCallBack)\n }\n\n _saveInitialAttribute(element, styleProperty) {\n const actualValue = element.style.getPropertyValue(styleProperty)\n if (actualValue) {\n Manipulator.setDataAttribute(element, styleProperty, actualValue)\n }\n }\n\n _resetElementAttributes(selector, styleProperty) {\n const manipulationCallBack = element => {\n const value = Manipulator.getDataAttribute(element, styleProperty)\n // We only want to remove the property if the value is `null`; the value can also be zero\n if (value === null) {\n element.style.removeProperty(styleProperty)\n return\n }\n\n Manipulator.removeDataAttribute(element, styleProperty)\n element.style.setProperty(styleProperty, value)\n }\n\n this._applyManipulationCallback(selector, manipulationCallBack)\n }\n\n _applyManipulationCallback(selector, callBack) {\n if (isElement(selector)) {\n callBack(selector)\n return\n }\n\n for (const sel of SelectorEngine.find(selector, this._element)) {\n callBack(sel)\n }\n }\n}\n\nexport default ScrollBarHelper\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport Backdrop from './util/backdrop.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport FocusTrap from './util/focustrap.js'\nimport { defineJQueryPlugin, isRTL, isVisible, reflow } from './util/index.js'\nimport ScrollBarHelper from './util/scrollbar.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'modal'\nconst DATA_KEY = 'bs.modal'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst ESCAPE_KEY = 'Escape'\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY}`\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_OPEN = 'modal-open'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_STATIC = 'modal-static'\n\nconst OPEN_SELECTOR = '.modal.show'\nconst SELECTOR_DIALOG = '.modal-dialog'\nconst SELECTOR_MODAL_BODY = '.modal-body'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"modal\"]'\n\nconst Default = {\n backdrop: true,\n focus: true,\n keyboard: true\n}\n\nconst DefaultType = {\n backdrop: '(boolean|string)',\n focus: 'boolean',\n keyboard: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element)\n this._backdrop = this._initializeBackDrop()\n this._focustrap = this._initializeFocusTrap()\n this._isShown = false\n this._isTransitioning = false\n this._scrollBar = new ScrollBarHelper()\n\n this._addEventListeners()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget)\n }\n\n show(relatedTarget) {\n if (this._isShown || this._isTransitioning) {\n return\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, {\n relatedTarget\n })\n\n if (showEvent.defaultPrevented) {\n return\n }\n\n this._isShown = true\n this._isTransitioning = true\n\n this._scrollBar.hide()\n\n document.body.classList.add(CLASS_NAME_OPEN)\n\n this._adjustDialog()\n\n this._backdrop.show(() => this._showElement(relatedTarget))\n }\n\n hide() {\n if (!this._isShown || this._isTransitioning) {\n return\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n if (hideEvent.defaultPrevented) {\n return\n }\n\n this._isShown = false\n this._isTransitioning = true\n this._focustrap.deactivate()\n\n this._element.classList.remove(CLASS_NAME_SHOW)\n\n this._queueCallback(() => this._hideModal(), this._element, this._isAnimated())\n }\n\n dispose() {\n EventHandler.off(window, EVENT_KEY)\n EventHandler.off(this._dialog, EVENT_KEY)\n\n this._backdrop.dispose()\n this._focustrap.deactivate()\n\n super.dispose()\n }\n\n handleUpdate() {\n this._adjustDialog()\n }\n\n // Private\n _initializeBackDrop() {\n return new Backdrop({\n isVisible: Boolean(this._config.backdrop), // 'static' option will be translated to true, and booleans will keep their value,\n isAnimated: this._isAnimated()\n })\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n })\n }\n\n _showElement(relatedTarget) {\n // try to append dynamic modal\n if (!document.body.contains(this._element)) {\n document.body.append(this._element)\n }\n\n this._element.style.display = 'block'\n this._element.removeAttribute('aria-hidden')\n this._element.setAttribute('aria-modal', true)\n this._element.setAttribute('role', 'dialog')\n this._element.scrollTop = 0\n\n const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog)\n if (modalBody) {\n modalBody.scrollTop = 0\n }\n\n reflow(this._element)\n\n this._element.classList.add(CLASS_NAME_SHOW)\n\n const transitionComplete = () => {\n if (this._config.focus) {\n this._focustrap.activate()\n }\n\n this._isTransitioning = false\n EventHandler.trigger(this._element, EVENT_SHOWN, {\n relatedTarget\n })\n }\n\n this._queueCallback(transitionComplete, this._dialog, this._isAnimated())\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return\n }\n\n if (this._config.keyboard) {\n this.hide()\n return\n }\n\n this._triggerBackdropTransition()\n })\n\n EventHandler.on(window, EVENT_RESIZE, () => {\n if (this._isShown && !this._isTransitioning) {\n this._adjustDialog()\n }\n })\n\n EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n if (this._element !== event.target || this._element !== event2.target) {\n return\n }\n\n if (this._config.backdrop === 'static') {\n this._triggerBackdropTransition()\n return\n }\n\n if (this._config.backdrop) {\n this.hide()\n }\n })\n })\n }\n\n _hideModal() {\n this._element.style.display = 'none'\n this._element.setAttribute('aria-hidden', true)\n this._element.removeAttribute('aria-modal')\n this._element.removeAttribute('role')\n this._isTransitioning = false\n\n this._backdrop.hide(() => {\n document.body.classList.remove(CLASS_NAME_OPEN)\n this._resetAdjustments()\n this._scrollBar.reset()\n EventHandler.trigger(this._element, EVENT_HIDDEN)\n })\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_FADE)\n }\n\n _triggerBackdropTransition() {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n if (hideEvent.defaultPrevented) {\n return\n }\n\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n const initialOverflowY = this._element.style.overflowY\n // return if the following background transition hasn't yet completed\n if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n return\n }\n\n if (!isModalOverflowing) {\n this._element.style.overflowY = 'hidden'\n }\n\n this._element.classList.add(CLASS_NAME_STATIC)\n this._queueCallback(() => {\n this._element.classList.remove(CLASS_NAME_STATIC)\n this._queueCallback(() => {\n this._element.style.overflowY = initialOverflowY\n }, this._dialog)\n }, this._dialog)\n\n this._element.focus()\n }\n\n /**\n * The following methods are used to handle overflowing modals\n */\n\n _adjustDialog() {\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n const scrollbarWidth = this._scrollBar.getWidth()\n const isBodyOverflowing = scrollbarWidth > 0\n\n if (isBodyOverflowing && !isModalOverflowing) {\n const property = isRTL() ? 'paddingLeft' : 'paddingRight'\n this._element.style[property] = `${scrollbarWidth}px`\n }\n\n if (!isBodyOverflowing && isModalOverflowing) {\n const property = isRTL() ? 'paddingRight' : 'paddingLeft'\n this._element.style[property] = `${scrollbarWidth}px`\n }\n }\n\n _resetAdjustments() {\n this._element.style.paddingLeft = ''\n this._element.style.paddingRight = ''\n }\n\n // Static\n static jQueryInterface(config, relatedTarget) {\n return this.each(function () {\n const data = Modal.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config](relatedTarget)\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n const target = SelectorEngine.getElementFromSelector(this)\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault()\n }\n\n EventHandler.one(target, EVENT_SHOW, showEvent => {\n if (showEvent.defaultPrevented) {\n // only register focus restorer if modal will actually get shown\n return\n }\n\n EventHandler.one(target, EVENT_HIDDEN, () => {\n if (isVisible(this)) {\n this.focus()\n }\n })\n })\n\n // avoid conflict when clicking modal toggler while another one is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n if (alreadyOpen) {\n Modal.getInstance(alreadyOpen).hide()\n }\n\n const data = Modal.getOrCreateInstance(target)\n\n data.toggle(this)\n})\n\nenableDismissTrigger(Modal)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal)\n\nexport default Modal\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport Backdrop from './util/backdrop.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport FocusTrap from './util/focustrap.js'\nimport {\n defineJQueryPlugin,\n isDisabled,\n isVisible\n} from './util/index.js'\nimport ScrollBarHelper from './util/scrollbar.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'offcanvas'\nconst DATA_KEY = 'bs.offcanvas'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst ESCAPE_KEY = 'Escape'\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_SHOWING = 'showing'\nconst CLASS_NAME_HIDING = 'hiding'\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop'\nconst OPEN_SELECTOR = '.offcanvas.show'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"offcanvas\"]'\n\nconst Default = {\n backdrop: true,\n keyboard: true,\n scroll: false\n}\n\nconst DefaultType = {\n backdrop: '(boolean|string)',\n keyboard: 'boolean',\n scroll: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._isShown = false\n this._backdrop = this._initializeBackDrop()\n this._focustrap = this._initializeFocusTrap()\n this._addEventListeners()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget)\n }\n\n show(relatedTarget) {\n if (this._isShown) {\n return\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, { relatedTarget })\n\n if (showEvent.defaultPrevented) {\n return\n }\n\n this._isShown = true\n this._backdrop.show()\n\n if (!this._config.scroll) {\n new ScrollBarHelper().hide()\n }\n\n this._element.setAttribute('aria-modal', true)\n this._element.setAttribute('role', 'dialog')\n this._element.classList.add(CLASS_NAME_SHOWING)\n\n const completeCallBack = () => {\n if (!this._config.scroll || this._config.backdrop) {\n this._focustrap.activate()\n }\n\n this._element.classList.add(CLASS_NAME_SHOW)\n this._element.classList.remove(CLASS_NAME_SHOWING)\n EventHandler.trigger(this._element, EVENT_SHOWN, { relatedTarget })\n }\n\n this._queueCallback(completeCallBack, this._element, true)\n }\n\n hide() {\n if (!this._isShown) {\n return\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n if (hideEvent.defaultPrevented) {\n return\n }\n\n this._focustrap.deactivate()\n this._element.blur()\n this._isShown = false\n this._element.classList.add(CLASS_NAME_HIDING)\n this._backdrop.hide()\n\n const completeCallback = () => {\n this._element.classList.remove(CLASS_NAME_SHOW, CLASS_NAME_HIDING)\n this._element.removeAttribute('aria-modal')\n this._element.removeAttribute('role')\n\n if (!this._config.scroll) {\n new ScrollBarHelper().reset()\n }\n\n EventHandler.trigger(this._element, EVENT_HIDDEN)\n }\n\n this._queueCallback(completeCallback, this._element, true)\n }\n\n dispose() {\n this._backdrop.dispose()\n this._focustrap.deactivate()\n super.dispose()\n }\n\n // Private\n _initializeBackDrop() {\n const clickCallback = () => {\n if (this._config.backdrop === 'static') {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n return\n }\n\n this.hide()\n }\n\n // 'static' option will be translated to true, and booleans will keep their value\n const isVisible = Boolean(this._config.backdrop)\n\n return new Backdrop({\n className: CLASS_NAME_BACKDROP,\n isVisible,\n isAnimated: true,\n rootElement: this._element.parentNode,\n clickCallback: isVisible ? clickCallback : null\n })\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n })\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return\n }\n\n if (this._config.keyboard) {\n this.hide()\n return\n }\n\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n })\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Offcanvas.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config](this)\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n const target = SelectorEngine.getElementFromSelector(this)\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault()\n }\n\n if (isDisabled(this)) {\n return\n }\n\n EventHandler.one(target, EVENT_HIDDEN, () => {\n // focus on trigger when it is closed\n if (isVisible(this)) {\n this.focus()\n }\n })\n\n // avoid conflict when clicking a toggler of an offcanvas, while another is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n if (alreadyOpen && alreadyOpen !== target) {\n Offcanvas.getInstance(alreadyOpen).hide()\n }\n\n const data = Offcanvas.getOrCreateInstance(target)\n data.toggle(this)\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n Offcanvas.getOrCreateInstance(selector).show()\n }\n})\n\nEventHandler.on(window, EVENT_RESIZE, () => {\n for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n if (getComputedStyle(element).position !== 'fixed') {\n Offcanvas.getOrCreateInstance(element).hide()\n }\n }\n})\n\nenableDismissTrigger(Offcanvas)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas)\n\nexport default Offcanvas\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n// js-docs-start allow-list\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i\n\nexport const DefaultAllowlist = {\n // Global attributes allowed on any supplied element below.\n '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n a: ['target', 'href', 'title', 'rel'],\n area: [],\n b: [],\n br: [],\n col: [],\n code: [],\n div: [],\n em: [],\n hr: [],\n h1: [],\n h2: [],\n h3: [],\n h4: [],\n h5: [],\n h6: [],\n i: [],\n img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n li: [],\n ol: [],\n p: [],\n pre: [],\n s: [],\n small: [],\n span: [],\n sub: [],\n sup: [],\n strong: [],\n u: [],\n ul: []\n}\n// js-docs-end allow-list\n\nconst uriAttributes = new Set([\n 'background',\n 'cite',\n 'href',\n 'itemtype',\n 'longdesc',\n 'poster',\n 'src',\n 'xlink:href'\n])\n\n/**\n * A pattern that recognizes URLs that are safe wrt. XSS in URL navigation\n * contexts.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/15.2.8/packages/core/src/sanitization/url_sanitizer.ts#L38\n */\n// eslint-disable-next-line unicorn/better-regex\nconst SAFE_URL_PATTERN = /^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i\n\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n const attributeName = attribute.nodeName.toLowerCase()\n\n if (allowedAttributeList.includes(attributeName)) {\n if (uriAttributes.has(attributeName)) {\n return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue))\n }\n\n return true\n }\n\n // Check if a regular expression validates the attribute.\n return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp)\n .some(regex => regex.test(attributeName))\n}\n\nexport function sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n if (!unsafeHtml.length) {\n return unsafeHtml\n }\n\n if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n return sanitizeFunction(unsafeHtml)\n }\n\n const domParser = new window.DOMParser()\n const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html')\n const elements = [].concat(...createdDocument.body.querySelectorAll('*'))\n\n for (const element of elements) {\n const elementName = element.nodeName.toLowerCase()\n\n if (!Object.keys(allowList).includes(elementName)) {\n element.remove()\n continue\n }\n\n const attributeList = [].concat(...element.attributes)\n const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || [])\n\n for (const attribute of attributeList) {\n if (!allowedAttribute(attribute, allowedAttributes)) {\n element.removeAttribute(attribute.nodeName)\n }\n }\n }\n\n return createdDocument.body.innerHTML\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport SelectorEngine from '../dom/selector-engine.js'\nimport Config from './config.js'\nimport { DefaultAllowlist, sanitizeHtml } from './sanitizer.js'\nimport { execute, getElement, isElement } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'TemplateFactory'\n\nconst Default = {\n allowList: DefaultAllowlist,\n content: {}, // { selector : text , selector2 : text2 , }\n extraClass: '',\n html: false,\n sanitize: true,\n sanitizeFn: null,\n template: '
'\n}\n\nconst DefaultType = {\n allowList: 'object',\n content: 'object',\n extraClass: '(string|function)',\n html: 'boolean',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n template: 'string'\n}\n\nconst DefaultContentType = {\n entry: '(string|element|function|null)',\n selector: '(string|element)'\n}\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n constructor(config) {\n super()\n this._config = this._getConfig(config)\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n getContent() {\n return Object.values(this._config.content)\n .map(config => this._resolvePossibleFunction(config))\n .filter(Boolean)\n }\n\n hasContent() {\n return this.getContent().length > 0\n }\n\n changeContent(content) {\n this._checkContent(content)\n this._config.content = { ...this._config.content, ...content }\n return this\n }\n\n toHtml() {\n const templateWrapper = document.createElement('div')\n templateWrapper.innerHTML = this._maybeSanitize(this._config.template)\n\n for (const [selector, text] of Object.entries(this._config.content)) {\n this._setContent(templateWrapper, text, selector)\n }\n\n const template = templateWrapper.children[0]\n const extraClass = this._resolvePossibleFunction(this._config.extraClass)\n\n if (extraClass) {\n template.classList.add(...extraClass.split(' '))\n }\n\n return template\n }\n\n // Private\n _typeCheckConfig(config) {\n super._typeCheckConfig(config)\n this._checkContent(config.content)\n }\n\n _checkContent(arg) {\n for (const [selector, content] of Object.entries(arg)) {\n super._typeCheckConfig({ selector, entry: content }, DefaultContentType)\n }\n }\n\n _setContent(template, content, selector) {\n const templateElement = SelectorEngine.findOne(selector, template)\n\n if (!templateElement) {\n return\n }\n\n content = this._resolvePossibleFunction(content)\n\n if (!content) {\n templateElement.remove()\n return\n }\n\n if (isElement(content)) {\n this._putElementInTemplate(getElement(content), templateElement)\n return\n }\n\n if (this._config.html) {\n templateElement.innerHTML = this._maybeSanitize(content)\n return\n }\n\n templateElement.textContent = content\n }\n\n _maybeSanitize(arg) {\n return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg\n }\n\n _resolvePossibleFunction(arg) {\n return execute(arg, [this])\n }\n\n _putElementInTemplate(element, templateElement) {\n if (this._config.html) {\n templateElement.innerHTML = ''\n templateElement.append(element)\n return\n }\n\n templateElement.textContent = element.textContent\n }\n}\n\nexport default TemplateFactory\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport { defineJQueryPlugin, execute, findShadowRoot, getElement, getUID, isRTL, noop } from './util/index.js'\nimport { DefaultAllowlist } from './util/sanitizer.js'\nimport TemplateFactory from './util/template-factory.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'tooltip'\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn'])\n\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_MODAL = 'modal'\nconst CLASS_NAME_SHOW = 'show'\n\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner'\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`\n\nconst EVENT_MODAL_HIDE = 'hide.bs.modal'\n\nconst TRIGGER_HOVER = 'hover'\nconst TRIGGER_FOCUS = 'focus'\nconst TRIGGER_CLICK = 'click'\nconst TRIGGER_MANUAL = 'manual'\n\nconst EVENT_HIDE = 'hide'\nconst EVENT_HIDDEN = 'hidden'\nconst EVENT_SHOW = 'show'\nconst EVENT_SHOWN = 'shown'\nconst EVENT_INSERTED = 'inserted'\nconst EVENT_CLICK = 'click'\nconst EVENT_FOCUSIN = 'focusin'\nconst EVENT_FOCUSOUT = 'focusout'\nconst EVENT_MOUSEENTER = 'mouseenter'\nconst EVENT_MOUSELEAVE = 'mouseleave'\n\nconst AttachmentMap = {\n AUTO: 'auto',\n TOP: 'top',\n RIGHT: isRTL() ? 'left' : 'right',\n BOTTOM: 'bottom',\n LEFT: isRTL() ? 'right' : 'left'\n}\n\nconst Default = {\n allowList: DefaultAllowlist,\n animation: true,\n boundary: 'clippingParents',\n container: false,\n customClass: '',\n delay: 0,\n fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n html: false,\n offset: [0, 6],\n placement: 'top',\n popperConfig: null,\n sanitize: true,\n sanitizeFn: null,\n selector: false,\n template: '
' +\n '
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',\n title: '',\n trigger: 'hover focus'\n}\n\nconst DefaultType = {\n allowList: 'object',\n animation: 'boolean',\n boundary: '(string|element)',\n container: '(string|element|boolean)',\n customClass: '(string|function)',\n delay: '(number|object)',\n fallbackPlacements: 'array',\n html: 'boolean',\n offset: '(array|string|function)',\n placement: '(string|function)',\n popperConfig: '(null|object|function)',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n selector: '(string|boolean)',\n template: 'string',\n title: '(string|element|function)',\n trigger: 'string'\n}\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n constructor(element, config) {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)')\n }\n\n super(element, config)\n\n // Private\n this._isEnabled = true\n this._timeout = 0\n this._isHovered = null\n this._activeTrigger = {}\n this._popper = null\n this._templateFactory = null\n this._newContent = null\n\n // Protected\n this.tip = null\n\n this._setListeners()\n\n if (!this._config.selector) {\n this._fixTitle()\n }\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n enable() {\n this._isEnabled = true\n }\n\n disable() {\n this._isEnabled = false\n }\n\n toggleEnabled() {\n this._isEnabled = !this._isEnabled\n }\n\n toggle() {\n if (!this._isEnabled) {\n return\n }\n\n this._activeTrigger.click = !this._activeTrigger.click\n if (this._isShown()) {\n this._leave()\n return\n }\n\n this._enter()\n }\n\n dispose() {\n clearTimeout(this._timeout)\n\n EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n\n if (this._element.getAttribute('data-bs-original-title')) {\n this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'))\n }\n\n this._disposePopper()\n super.dispose()\n }\n\n show() {\n if (this._element.style.display === 'none') {\n throw new Error('Please use show on visible elements')\n }\n\n if (!(this._isWithContent() && this._isEnabled)) {\n return\n }\n\n const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW))\n const shadowRoot = findShadowRoot(this._element)\n const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element)\n\n if (showEvent.defaultPrevented || !isInTheDom) {\n return\n }\n\n // TODO: v6 remove this or make it optional\n this._disposePopper()\n\n const tip = this._getTipElement()\n\n this._element.setAttribute('aria-describedby', tip.getAttribute('id'))\n\n const { container } = this._config\n\n if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n container.append(tip)\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED))\n }\n\n this._popper = this._createPopper(tip)\n\n tip.classList.add(CLASS_NAME_SHOW)\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop)\n }\n }\n\n const complete = () => {\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN))\n\n if (this._isHovered === false) {\n this._leave()\n }\n\n this._isHovered = false\n }\n\n this._queueCallback(complete, this.tip, this._isAnimated())\n }\n\n hide() {\n if (!this._isShown()) {\n return\n }\n\n const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE))\n if (hideEvent.defaultPrevented) {\n return\n }\n\n const tip = this._getTipElement()\n tip.classList.remove(CLASS_NAME_SHOW)\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop)\n }\n }\n\n this._activeTrigger[TRIGGER_CLICK] = false\n this._activeTrigger[TRIGGER_FOCUS] = false\n this._activeTrigger[TRIGGER_HOVER] = false\n this._isHovered = null // it is a trick to support manual triggering\n\n const complete = () => {\n if (this._isWithActiveTrigger()) {\n return\n }\n\n if (!this._isHovered) {\n this._disposePopper()\n }\n\n this._element.removeAttribute('aria-describedby')\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN))\n }\n\n this._queueCallback(complete, this.tip, this._isAnimated())\n }\n\n update() {\n if (this._popper) {\n this._popper.update()\n }\n }\n\n // Protected\n _isWithContent() {\n return Boolean(this._getTitle())\n }\n\n _getTipElement() {\n if (!this.tip) {\n this.tip = this._createTipElement(this._newContent || this._getContentForTemplate())\n }\n\n return this.tip\n }\n\n _createTipElement(content) {\n const tip = this._getTemplateFactory(content).toHtml()\n\n // TODO: remove this check in v6\n if (!tip) {\n return null\n }\n\n tip.classList.remove(CLASS_NAME_FADE, CLASS_NAME_SHOW)\n // TODO: v6 the following can be achieved with CSS only\n tip.classList.add(`bs-${this.constructor.NAME}-auto`)\n\n const tipId = getUID(this.constructor.NAME).toString()\n\n tip.setAttribute('id', tipId)\n\n if (this._isAnimated()) {\n tip.classList.add(CLASS_NAME_FADE)\n }\n\n return tip\n }\n\n setContent(content) {\n this._newContent = content\n if (this._isShown()) {\n this._disposePopper()\n this.show()\n }\n }\n\n _getTemplateFactory(content) {\n if (this._templateFactory) {\n this._templateFactory.changeContent(content)\n } else {\n this._templateFactory = new TemplateFactory({\n ...this._config,\n // the `content` var has to be after `this._config`\n // to override config.content in case of popover\n content,\n extraClass: this._resolvePossibleFunction(this._config.customClass)\n })\n }\n\n return this._templateFactory\n }\n\n _getContentForTemplate() {\n return {\n [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n }\n }\n\n _getTitle() {\n return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title')\n }\n\n // Private\n _initializeOnDelegatedTarget(event) {\n return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig())\n }\n\n _isAnimated() {\n return this._config.animation || (this.tip && this.tip.classList.contains(CLASS_NAME_FADE))\n }\n\n _isShown() {\n return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW)\n }\n\n _createPopper(tip) {\n const placement = execute(this._config.placement, [this, tip, this._element])\n const attachment = AttachmentMap[placement.toUpperCase()]\n return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment))\n }\n\n _getOffset() {\n const { offset } = this._config\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10))\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element)\n }\n\n return offset\n }\n\n _resolvePossibleFunction(arg) {\n return execute(arg, [this._element])\n }\n\n _getPopperConfig(attachment) {\n const defaultBsPopperConfig = {\n placement: attachment,\n modifiers: [\n {\n name: 'flip',\n options: {\n fallbackPlacements: this._config.fallbackPlacements\n }\n },\n {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n },\n {\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n },\n {\n name: 'arrow',\n options: {\n element: `.${this.constructor.NAME}-arrow`\n }\n },\n {\n name: 'preSetPlacement',\n enabled: true,\n phase: 'beforeMain',\n fn: data => {\n // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n this._getTipElement().setAttribute('data-popper-placement', data.state.placement)\n }\n }\n ]\n }\n\n return {\n ...defaultBsPopperConfig,\n ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n }\n }\n\n _setListeners() {\n const triggers = this._config.trigger.split(' ')\n\n for (const trigger of triggers) {\n if (trigger === 'click') {\n EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK), this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event)\n context.toggle()\n })\n } else if (trigger !== TRIGGER_MANUAL) {\n const eventIn = trigger === TRIGGER_HOVER ?\n this.constructor.eventName(EVENT_MOUSEENTER) :\n this.constructor.eventName(EVENT_FOCUSIN)\n const eventOut = trigger === TRIGGER_HOVER ?\n this.constructor.eventName(EVENT_MOUSELEAVE) :\n this.constructor.eventName(EVENT_FOCUSOUT)\n\n EventHandler.on(this._element, eventIn, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event)\n context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true\n context._enter()\n })\n EventHandler.on(this._element, eventOut, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event)\n context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] =\n context._element.contains(event.relatedTarget)\n\n context._leave()\n })\n }\n }\n\n this._hideModalHandler = () => {\n if (this._element) {\n this.hide()\n }\n }\n\n EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n }\n\n _fixTitle() {\n const title = this._element.getAttribute('title')\n\n if (!title) {\n return\n }\n\n if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n this._element.setAttribute('aria-label', title)\n }\n\n this._element.setAttribute('data-bs-original-title', title) // DO NOT USE IT. Is only for backwards compatibility\n this._element.removeAttribute('title')\n }\n\n _enter() {\n if (this._isShown() || this._isHovered) {\n this._isHovered = true\n return\n }\n\n this._isHovered = true\n\n this._setTimeout(() => {\n if (this._isHovered) {\n this.show()\n }\n }, this._config.delay.show)\n }\n\n _leave() {\n if (this._isWithActiveTrigger()) {\n return\n }\n\n this._isHovered = false\n\n this._setTimeout(() => {\n if (!this._isHovered) {\n this.hide()\n }\n }, this._config.delay.hide)\n }\n\n _setTimeout(handler, timeout) {\n clearTimeout(this._timeout)\n this._timeout = setTimeout(handler, timeout)\n }\n\n _isWithActiveTrigger() {\n return Object.values(this._activeTrigger).includes(true)\n }\n\n _getConfig(config) {\n const dataAttributes = Manipulator.getDataAttributes(this._element)\n\n for (const dataAttribute of Object.keys(dataAttributes)) {\n if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n delete dataAttributes[dataAttribute]\n }\n }\n\n config = {\n ...dataAttributes,\n ...(typeof config === 'object' && config ? config : {})\n }\n config = this._mergeConfigObj(config)\n config = this._configAfterMerge(config)\n this._typeCheckConfig(config)\n return config\n }\n\n _configAfterMerge(config) {\n config.container = config.container === false ? document.body : getElement(config.container)\n\n if (typeof config.delay === 'number') {\n config.delay = {\n show: config.delay,\n hide: config.delay\n }\n }\n\n if (typeof config.title === 'number') {\n config.title = config.title.toString()\n }\n\n if (typeof config.content === 'number') {\n config.content = config.content.toString()\n }\n\n return config\n }\n\n _getDelegateConfig() {\n const config = {}\n\n for (const [key, value] of Object.entries(this._config)) {\n if (this.constructor.Default[key] !== value) {\n config[key] = value\n }\n }\n\n config.selector = false\n config.trigger = 'manual'\n\n // In the future can be replaced with:\n // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n // `Object.fromEntries(keysWithDifferentValues)`\n return config\n }\n\n _disposePopper() {\n if (this._popper) {\n this._popper.destroy()\n this._popper = null\n }\n\n if (this.tip) {\n this.tip.remove()\n this.tip = null\n }\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Tooltip.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n })\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip)\n\nexport default Tooltip\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Tooltip from './tooltip.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'popover'\n\nconst SELECTOR_TITLE = '.popover-header'\nconst SELECTOR_CONTENT = '.popover-body'\n\nconst Default = {\n ...Tooltip.Default,\n content: '',\n offset: [0, 8],\n placement: 'right',\n template: '
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' +\n '
' +\n '
',\n trigger: 'click'\n}\n\nconst DefaultType = {\n ...Tooltip.DefaultType,\n content: '(null|string|element|function)'\n}\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Overrides\n _isWithContent() {\n return this._getTitle() || this._getContent()\n }\n\n // Private\n _getContentForTemplate() {\n return {\n [SELECTOR_TITLE]: this._getTitle(),\n [SELECTOR_CONTENT]: this._getContent()\n }\n }\n\n _getContent() {\n return this._resolvePossibleFunction(this._config.content)\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Popover.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n })\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover)\n\nexport default Popover\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport { defineJQueryPlugin, getElement, isDisabled, isVisible } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'scrollspy'\nconst DATA_KEY = 'bs.scrollspy'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_ACTIVATE = `activate${EVENT_KEY}`\nconst EVENT_CLICK = `click${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item'\nconst CLASS_NAME_ACTIVE = 'active'\n\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]'\nconst SELECTOR_TARGET_LINKS = '[href]'\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group'\nconst SELECTOR_NAV_LINKS = '.nav-link'\nconst SELECTOR_NAV_ITEMS = '.nav-item'\nconst SELECTOR_LIST_ITEMS = '.list-group-item'\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`\nconst SELECTOR_DROPDOWN = '.dropdown'\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle'\n\nconst Default = {\n offset: null, // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: '0px 0px -25%',\n smoothScroll: false,\n target: null,\n threshold: [0.1, 0.5, 1]\n}\n\nconst DefaultType = {\n offset: '(number|null)', // TODO v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: 'string',\n smoothScroll: 'boolean',\n target: 'element',\n threshold: 'array'\n}\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n // this._element is the observablesContainer and config.target the menu links wrapper\n this._targetLinks = new Map()\n this._observableSections = new Map()\n this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element\n this._activeTarget = null\n this._observer = null\n this._previousScrollData = {\n visibleEntryTop: 0,\n parentScrollTop: 0\n }\n this.refresh() // initialize\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n refresh() {\n this._initializeTargetsAndObservables()\n this._maybeEnableSmoothScroll()\n\n if (this._observer) {\n this._observer.disconnect()\n } else {\n this._observer = this._getNewObserver()\n }\n\n for (const section of this._observableSections.values()) {\n this._observer.observe(section)\n }\n }\n\n dispose() {\n this._observer.disconnect()\n super.dispose()\n }\n\n // Private\n _configAfterMerge(config) {\n // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n config.target = getElement(config.target) || document.body\n\n // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin\n\n if (typeof config.threshold === 'string') {\n config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value))\n }\n\n return config\n }\n\n _maybeEnableSmoothScroll() {\n if (!this._config.smoothScroll) {\n return\n }\n\n // unregister any previous listeners\n EventHandler.off(this._config.target, EVENT_CLICK)\n\n EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n const observableSection = this._observableSections.get(event.target.hash)\n if (observableSection) {\n event.preventDefault()\n const root = this._rootElement || window\n const height = observableSection.offsetTop - this._element.offsetTop\n if (root.scrollTo) {\n root.scrollTo({ top: height, behavior: 'smooth' })\n return\n }\n\n // Chrome 60 doesn't support `scrollTo`\n root.scrollTop = height\n }\n })\n }\n\n _getNewObserver() {\n const options = {\n root: this._rootElement,\n threshold: this._config.threshold,\n rootMargin: this._config.rootMargin\n }\n\n return new IntersectionObserver(entries => this._observerCallback(entries), options)\n }\n\n // The logic of selection\n _observerCallback(entries) {\n const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`)\n const activate = entry => {\n this._previousScrollData.visibleEntryTop = entry.target.offsetTop\n this._process(targetElement(entry))\n }\n\n const parentScrollTop = (this._rootElement || document.documentElement).scrollTop\n const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop\n this._previousScrollData.parentScrollTop = parentScrollTop\n\n for (const entry of entries) {\n if (!entry.isIntersecting) {\n this._activeTarget = null\n this._clearActiveClass(targetElement(entry))\n\n continue\n }\n\n const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop\n // if we are scrolling down, pick the bigger offsetTop\n if (userScrollsDown && entryIsLowerThanPrevious) {\n activate(entry)\n // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n if (!parentScrollTop) {\n return\n }\n\n continue\n }\n\n // if we are scrolling up, pick the smallest offsetTop\n if (!userScrollsDown && !entryIsLowerThanPrevious) {\n activate(entry)\n }\n }\n }\n\n _initializeTargetsAndObservables() {\n this._targetLinks = new Map()\n this._observableSections = new Map()\n\n const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target)\n\n for (const anchor of targetLinks) {\n // ensure that the anchor has an id and is not disabled\n if (!anchor.hash || isDisabled(anchor)) {\n continue\n }\n\n const observableSection = SelectorEngine.findOne(decodeURI(anchor.hash), this._element)\n\n // ensure that the observableSection exists & is visible\n if (isVisible(observableSection)) {\n this._targetLinks.set(decodeURI(anchor.hash), anchor)\n this._observableSections.set(anchor.hash, observableSection)\n }\n }\n }\n\n _process(target) {\n if (this._activeTarget === target) {\n return\n }\n\n this._clearActiveClass(this._config.target)\n this._activeTarget = target\n target.classList.add(CLASS_NAME_ACTIVE)\n this._activateParents(target)\n\n EventHandler.trigger(this._element, EVENT_ACTIVATE, { relatedTarget: target })\n }\n\n _activateParents(target) {\n // Activate dropdown parents\n if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE, target.closest(SELECTOR_DROPDOWN))\n .classList.add(CLASS_NAME_ACTIVE)\n return\n }\n\n for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n // Set triggered links parents as active\n // With both
    and
')},createChildNavList:function(e){var t=this.createNavList();return e.append(t),t},generateNavEl:function(e,t){var n=a('');n.attr("href","#"+e),n.text(t);var r=a("
  • ");return r.append(n),r},generateNavItem:function(e){var t=this.generateAnchor(e),n=a(e),r=n.data("toc-text")||n.text();return this.generateNavEl(t,r)},getTopLevel:function(e){for(var t=1;t<=6;t++){if(1 + + + + + + + + + + + + diff --git a/deps/font-awesome-6.4.2/css/all.css b/deps/font-awesome-6.4.2/css/all.css new file mode 100644 index 0000000..bdb6e3a --- /dev/null +++ b/deps/font-awesome-6.4.2/css/all.css @@ -0,0 +1,7968 @@ +/*! + * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com + * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) + * Copyright 2023 Fonticons, Inc. + */ +.fa { + font-family: var(--fa-style-family, "Font Awesome 6 Free"); + font-weight: var(--fa-style, 900); } + +.fa, +.fa-classic, +.fa-sharp, +.fas, +.fa-solid, +.far, +.fa-regular, +.fab, +.fa-brands { + -moz-osx-font-smoothing: grayscale; + -webkit-font-smoothing: antialiased; + display: var(--fa-display, inline-block); + font-style: normal; + font-variant: normal; + line-height: 1; + text-rendering: auto; } + +.fas, +.fa-classic, +.fa-solid, +.far, +.fa-regular { + font-family: 'Font Awesome 6 Free'; } + +.fab, +.fa-brands { + font-family: 'Font Awesome 6 Brands'; } + +.fa-1x { + font-size: 1em; } + +.fa-2x { + font-size: 2em; } + +.fa-3x { + font-size: 3em; } + +.fa-4x { + font-size: 4em; } + +.fa-5x { + font-size: 5em; } + +.fa-6x { + font-size: 6em; } + +.fa-7x { + font-size: 7em; } + +.fa-8x { + font-size: 8em; } + +.fa-9x { + font-size: 9em; } + +.fa-10x { + font-size: 10em; } + +.fa-2xs { + font-size: 0.625em; + line-height: 0.1em; + vertical-align: 0.225em; } + +.fa-xs { + font-size: 0.75em; + line-height: 0.08333em; + vertical-align: 0.125em; } + +.fa-sm { + font-size: 0.875em; + line-height: 0.07143em; + vertical-align: 0.05357em; } + +.fa-lg { + font-size: 1.25em; + line-height: 0.05em; + vertical-align: -0.075em; } + +.fa-xl { + font-size: 1.5em; + line-height: 0.04167em; + vertical-align: -0.125em; } + +.fa-2xl { + font-size: 2em; + line-height: 0.03125em; + vertical-align: -0.1875em; } + +.fa-fw { + text-align: center; + width: 1.25em; } + +.fa-ul { + list-style-type: none; + margin-left: var(--fa-li-margin, 2.5em); + padding-left: 0; } + .fa-ul > li { + position: relative; } + +.fa-li { + left: calc(var(--fa-li-width, 2em) * -1); + position: absolute; + text-align: center; + width: var(--fa-li-width, 2em); + line-height: inherit; } + +.fa-border { + border-color: var(--fa-border-color, #eee); + border-radius: var(--fa-border-radius, 0.1em); + border-style: var(--fa-border-style, solid); + border-width: var(--fa-border-width, 0.08em); + padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); } + +.fa-pull-left { + float: left; + margin-right: var(--fa-pull-margin, 0.3em); } + +.fa-pull-right { + float: right; + margin-left: var(--fa-pull-margin, 0.3em); } + +.fa-beat { + -webkit-animation-name: fa-beat; + animation-name: fa-beat; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-bounce { + -webkit-animation-name: fa-bounce; + animation-name: fa-bounce; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); } + +.fa-fade { + -webkit-animation-name: fa-fade; + animation-name: fa-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-beat-fade { + -webkit-animation-name: fa-beat-fade; + animation-name: fa-beat-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-flip { + -webkit-animation-name: fa-flip; + animation-name: fa-flip; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-shake { + -webkit-animation-name: fa-shake; + animation-name: fa-shake; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 2s); + animation-duration: var(--fa-animation-duration, 2s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin-reverse { + --fa-animation-direction: reverse; } + +.fa-pulse, +.fa-spin-pulse { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, steps(8)); + animation-timing-function: var(--fa-animation-timing, steps(8)); } + +@media (prefers-reduced-motion: reduce) { + .fa-beat, + .fa-bounce, + .fa-fade, + .fa-beat-fade, + .fa-flip, + .fa-pulse, + .fa-shake, + .fa-spin, + .fa-spin-pulse { + -webkit-animation-delay: -1ms; + animation-delay: -1ms; + -webkit-animation-duration: 1ms; + animation-duration: 1ms; + -webkit-animation-iteration-count: 1; + animation-iteration-count: 1; + -webkit-transition-delay: 0s; + transition-delay: 0s; + -webkit-transition-duration: 0s; + transition-duration: 0s; } } + +@-webkit-keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@-webkit-keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@-webkit-keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@-webkit-keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@-webkit-keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@-webkit-keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@-webkit-keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +@keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +.fa-rotate-90 { + -webkit-transform: rotate(90deg); + transform: rotate(90deg); } + +.fa-rotate-180 { + -webkit-transform: rotate(180deg); + transform: rotate(180deg); } + +.fa-rotate-270 { + -webkit-transform: rotate(270deg); + transform: rotate(270deg); } + +.fa-flip-horizontal { + -webkit-transform: scale(-1, 1); + transform: scale(-1, 1); } + +.fa-flip-vertical { + -webkit-transform: scale(1, -1); + transform: scale(1, -1); } + +.fa-flip-both, +.fa-flip-horizontal.fa-flip-vertical { + -webkit-transform: scale(-1, -1); + transform: scale(-1, -1); } + +.fa-rotate-by { + -webkit-transform: rotate(var(--fa-rotate-angle, none)); + transform: rotate(var(--fa-rotate-angle, none)); } + +.fa-stack { + display: inline-block; + height: 2em; + line-height: 2em; + position: relative; + vertical-align: middle; + width: 2.5em; } + +.fa-stack-1x, +.fa-stack-2x { + left: 0; + position: absolute; + text-align: center; + width: 100%; + z-index: var(--fa-stack-z-index, auto); } + +.fa-stack-1x { + line-height: inherit; } + +.fa-stack-2x { + font-size: 2em; } + +.fa-inverse { + color: var(--fa-inverse, #fff); } + +/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen +readers do not read off random characters that represent icons */ + +.fa-0::before { + content: "\30"; } + +.fa-1::before { + content: "\31"; } + +.fa-2::before { + content: "\32"; } + +.fa-3::before { + content: "\33"; } + +.fa-4::before { + content: "\34"; } + +.fa-5::before { + content: "\35"; } + +.fa-6::before { + content: "\36"; } + +.fa-7::before { + content: "\37"; } + +.fa-8::before { + content: "\38"; } + +.fa-9::before { + content: "\39"; } + +.fa-fill-drip::before { + content: "\f576"; } + +.fa-arrows-to-circle::before { + content: "\e4bd"; } + +.fa-circle-chevron-right::before { + content: "\f138"; } + +.fa-chevron-circle-right::before { + content: "\f138"; } + +.fa-at::before { + content: "\40"; } + +.fa-trash-can::before { + content: "\f2ed"; } + +.fa-trash-alt::before { + content: "\f2ed"; } + +.fa-text-height::before { + content: "\f034"; } + +.fa-user-xmark::before { + content: "\f235"; } + +.fa-user-times::before { + content: "\f235"; } + +.fa-stethoscope::before { + content: "\f0f1"; } + +.fa-message::before { + content: "\f27a"; } + +.fa-comment-alt::before { + content: "\f27a"; } + +.fa-info::before { + content: "\f129"; } + +.fa-down-left-and-up-right-to-center::before { + content: "\f422"; } + +.fa-compress-alt::before { + content: "\f422"; } + +.fa-explosion::before { + content: "\e4e9"; } + +.fa-file-lines::before { + content: "\f15c"; } + +.fa-file-alt::before { + content: "\f15c"; } + +.fa-file-text::before { + content: "\f15c"; } + +.fa-wave-square::before { + content: "\f83e"; } + +.fa-ring::before { + content: "\f70b"; } + +.fa-building-un::before { + content: "\e4d9"; } + +.fa-dice-three::before { + content: "\f527"; } + +.fa-calendar-days::before { + content: "\f073"; } + +.fa-calendar-alt::before { + content: "\f073"; } + +.fa-anchor-circle-check::before { + content: "\e4aa"; } + +.fa-building-circle-arrow-right::before { + content: "\e4d1"; } + +.fa-volleyball::before { + content: "\f45f"; } + +.fa-volleyball-ball::before { + content: "\f45f"; } + +.fa-arrows-up-to-line::before { + content: "\e4c2"; } + +.fa-sort-down::before { + content: "\f0dd"; } + +.fa-sort-desc::before { + content: "\f0dd"; } + +.fa-circle-minus::before { + content: "\f056"; } + +.fa-minus-circle::before { + content: "\f056"; } + +.fa-door-open::before { + content: "\f52b"; } + +.fa-right-from-bracket::before { + content: "\f2f5"; } + +.fa-sign-out-alt::before { + content: "\f2f5"; } + +.fa-atom::before { + content: "\f5d2"; } + +.fa-soap::before { + content: "\e06e"; } + +.fa-icons::before { + content: "\f86d"; } + +.fa-heart-music-camera-bolt::before { + content: "\f86d"; } + +.fa-microphone-lines-slash::before { + content: "\f539"; } + +.fa-microphone-alt-slash::before { + content: "\f539"; } + +.fa-bridge-circle-check::before { + content: "\e4c9"; } + +.fa-pump-medical::before { + content: "\e06a"; } + +.fa-fingerprint::before { + content: "\f577"; } + +.fa-hand-point-right::before { + content: "\f0a4"; } + +.fa-magnifying-glass-location::before { + content: "\f689"; } + +.fa-search-location::before { + content: "\f689"; } + +.fa-forward-step::before { + content: "\f051"; } + +.fa-step-forward::before { + content: "\f051"; } + +.fa-face-smile-beam::before { + content: "\f5b8"; } + +.fa-smile-beam::before { + content: "\f5b8"; } + +.fa-flag-checkered::before { + content: "\f11e"; } + +.fa-football::before { + content: "\f44e"; } + +.fa-football-ball::before { + content: "\f44e"; } + +.fa-school-circle-exclamation::before { + content: "\e56c"; } + +.fa-crop::before { + content: "\f125"; } + +.fa-angles-down::before { + content: "\f103"; } + +.fa-angle-double-down::before { + content: "\f103"; } + +.fa-users-rectangle::before { + content: "\e594"; } + +.fa-people-roof::before { + content: "\e537"; } + +.fa-people-line::before { + content: "\e534"; } + +.fa-beer-mug-empty::before { + content: "\f0fc"; } + +.fa-beer::before { + content: "\f0fc"; } + +.fa-diagram-predecessor::before { + content: "\e477"; } + +.fa-arrow-up-long::before { + content: "\f176"; } + +.fa-long-arrow-up::before { + content: "\f176"; } + +.fa-fire-flame-simple::before { + content: "\f46a"; } + +.fa-burn::before { + content: "\f46a"; } + +.fa-person::before { + content: "\f183"; } + +.fa-male::before { + content: "\f183"; } + +.fa-laptop::before { + content: "\f109"; } + +.fa-file-csv::before { + content: "\f6dd"; } + +.fa-menorah::before { + content: "\f676"; } + +.fa-truck-plane::before { + content: "\e58f"; } + +.fa-record-vinyl::before { + content: "\f8d9"; } + +.fa-face-grin-stars::before { + content: "\f587"; } + +.fa-grin-stars::before { + content: "\f587"; } + +.fa-bong::before { + content: "\f55c"; } + +.fa-spaghetti-monster-flying::before { + content: "\f67b"; } + +.fa-pastafarianism::before { + content: "\f67b"; } + +.fa-arrow-down-up-across-line::before { + content: "\e4af"; } + +.fa-spoon::before { + content: "\f2e5"; } + +.fa-utensil-spoon::before { + content: "\f2e5"; } + +.fa-jar-wheat::before { + content: "\e517"; } + +.fa-envelopes-bulk::before { + content: "\f674"; } + +.fa-mail-bulk::before { + content: "\f674"; } + +.fa-file-circle-exclamation::before { + content: "\e4eb"; } + +.fa-circle-h::before { + content: "\f47e"; } + +.fa-hospital-symbol::before { + content: "\f47e"; } + +.fa-pager::before { + content: "\f815"; } + +.fa-address-book::before { + content: "\f2b9"; } + +.fa-contact-book::before { + content: "\f2b9"; } + +.fa-strikethrough::before { + content: "\f0cc"; } + +.fa-k::before { + content: "\4b"; } + +.fa-landmark-flag::before { + content: "\e51c"; } + +.fa-pencil::before { + content: "\f303"; } + +.fa-pencil-alt::before { + content: "\f303"; } + +.fa-backward::before { + content: "\f04a"; } + +.fa-caret-right::before { + content: "\f0da"; } + +.fa-comments::before { + content: "\f086"; } + +.fa-paste::before { + content: "\f0ea"; } + +.fa-file-clipboard::before { + content: "\f0ea"; } + +.fa-code-pull-request::before { + content: "\e13c"; } + +.fa-clipboard-list::before { + content: "\f46d"; } + +.fa-truck-ramp-box::before { + content: "\f4de"; } + +.fa-truck-loading::before { + content: "\f4de"; } + +.fa-user-check::before { + content: "\f4fc"; } + +.fa-vial-virus::before { + content: "\e597"; } + +.fa-sheet-plastic::before { + content: "\e571"; } + +.fa-blog::before { + content: "\f781"; } + +.fa-user-ninja::before { + content: "\f504"; } + +.fa-person-arrow-up-from-line::before { + content: "\e539"; } + +.fa-scroll-torah::before { + content: "\f6a0"; } + +.fa-torah::before { + content: "\f6a0"; } + +.fa-broom-ball::before { + content: "\f458"; } + +.fa-quidditch::before { + content: "\f458"; } + +.fa-quidditch-broom-ball::before { + content: "\f458"; } + +.fa-toggle-off::before { + content: "\f204"; } + +.fa-box-archive::before { + content: "\f187"; } + +.fa-archive::before { + content: "\f187"; } + +.fa-person-drowning::before { + content: "\e545"; } + +.fa-arrow-down-9-1::before { + content: "\f886"; } + +.fa-sort-numeric-desc::before { + content: "\f886"; } + +.fa-sort-numeric-down-alt::before { + content: "\f886"; } + +.fa-face-grin-tongue-squint::before { + content: "\f58a"; } + +.fa-grin-tongue-squint::before { + content: "\f58a"; } + +.fa-spray-can::before { + content: "\f5bd"; } + +.fa-truck-monster::before { + content: "\f63b"; } + +.fa-w::before { + content: "\57"; } + +.fa-earth-africa::before { + content: "\f57c"; } + +.fa-globe-africa::before { + content: "\f57c"; } + +.fa-rainbow::before { + content: "\f75b"; } + +.fa-circle-notch::before { + content: "\f1ce"; } + +.fa-tablet-screen-button::before { + content: "\f3fa"; } + +.fa-tablet-alt::before { + content: "\f3fa"; } + +.fa-paw::before { + content: "\f1b0"; } + +.fa-cloud::before { + content: "\f0c2"; } + +.fa-trowel-bricks::before { + content: "\e58a"; } + +.fa-face-flushed::before { + content: "\f579"; } + +.fa-flushed::before { + content: "\f579"; } + +.fa-hospital-user::before { + content: "\f80d"; } + +.fa-tent-arrow-left-right::before { + content: "\e57f"; } + +.fa-gavel::before { + content: "\f0e3"; } + +.fa-legal::before { + content: "\f0e3"; } + +.fa-binoculars::before { + content: "\f1e5"; } + +.fa-microphone-slash::before { + content: "\f131"; } + +.fa-box-tissue::before { + content: "\e05b"; } + +.fa-motorcycle::before { + content: "\f21c"; } + +.fa-bell-concierge::before { + content: "\f562"; } + +.fa-concierge-bell::before { + content: "\f562"; } + +.fa-pen-ruler::before { + content: "\f5ae"; } + +.fa-pencil-ruler::before { + content: "\f5ae"; } + +.fa-people-arrows::before { + content: "\e068"; } + +.fa-people-arrows-left-right::before { + content: "\e068"; } + +.fa-mars-and-venus-burst::before { + content: "\e523"; } + +.fa-square-caret-right::before { + content: "\f152"; } + +.fa-caret-square-right::before { + content: "\f152"; } + +.fa-scissors::before { + content: "\f0c4"; } + +.fa-cut::before { + content: "\f0c4"; } + +.fa-sun-plant-wilt::before { + content: "\e57a"; } + +.fa-toilets-portable::before { + content: "\e584"; } + +.fa-hockey-puck::before { + content: "\f453"; } + +.fa-table::before { + content: "\f0ce"; } + +.fa-magnifying-glass-arrow-right::before { + content: "\e521"; } + +.fa-tachograph-digital::before { + content: "\f566"; } + +.fa-digital-tachograph::before { + content: "\f566"; } + +.fa-users-slash::before { + content: "\e073"; } + +.fa-clover::before { + content: "\e139"; } + +.fa-reply::before { + content: "\f3e5"; } + +.fa-mail-reply::before { + content: "\f3e5"; } + +.fa-star-and-crescent::before { + content: "\f699"; } + +.fa-house-fire::before { + content: "\e50c"; } + +.fa-square-minus::before { + content: "\f146"; } + +.fa-minus-square::before { + content: "\f146"; } + +.fa-helicopter::before { + content: "\f533"; } + +.fa-compass::before { + content: "\f14e"; } + +.fa-square-caret-down::before { + content: "\f150"; } + +.fa-caret-square-down::before { + content: "\f150"; } + +.fa-file-circle-question::before { + content: "\e4ef"; } + +.fa-laptop-code::before { + content: "\f5fc"; } + +.fa-swatchbook::before { + content: "\f5c3"; } + +.fa-prescription-bottle::before { + content: "\f485"; } + +.fa-bars::before { + content: "\f0c9"; } + +.fa-navicon::before { + content: "\f0c9"; } + +.fa-people-group::before { + content: "\e533"; } + +.fa-hourglass-end::before { + content: "\f253"; } + +.fa-hourglass-3::before { + content: "\f253"; } + +.fa-heart-crack::before { + content: "\f7a9"; } + +.fa-heart-broken::before { + content: "\f7a9"; } + +.fa-square-up-right::before { + content: "\f360"; } + +.fa-external-link-square-alt::before { + content: "\f360"; } + +.fa-face-kiss-beam::before { + content: "\f597"; } + +.fa-kiss-beam::before { + content: "\f597"; } + +.fa-film::before { + content: "\f008"; } + +.fa-ruler-horizontal::before { + content: "\f547"; } + +.fa-people-robbery::before { + content: "\e536"; } + +.fa-lightbulb::before { + content: "\f0eb"; } + +.fa-caret-left::before { + content: "\f0d9"; } + +.fa-circle-exclamation::before { + content: "\f06a"; } + +.fa-exclamation-circle::before { + content: "\f06a"; } + +.fa-school-circle-xmark::before { + content: "\e56d"; } + +.fa-arrow-right-from-bracket::before { + content: "\f08b"; } + +.fa-sign-out::before { + content: "\f08b"; } + +.fa-circle-chevron-down::before { + content: "\f13a"; } + +.fa-chevron-circle-down::before { + content: "\f13a"; } + +.fa-unlock-keyhole::before { + content: "\f13e"; } + +.fa-unlock-alt::before { + content: "\f13e"; } + +.fa-cloud-showers-heavy::before { + content: "\f740"; } + +.fa-headphones-simple::before { + content: "\f58f"; } + +.fa-headphones-alt::before { + content: "\f58f"; } + +.fa-sitemap::before { + content: "\f0e8"; } + +.fa-circle-dollar-to-slot::before { + content: "\f4b9"; } + +.fa-donate::before { + content: "\f4b9"; } + +.fa-memory::before { + content: "\f538"; } + +.fa-road-spikes::before { + content: "\e568"; } + +.fa-fire-burner::before { + content: "\e4f1"; } + +.fa-flag::before { + content: "\f024"; } + +.fa-hanukiah::before { + content: "\f6e6"; } + +.fa-feather::before { + content: "\f52d"; } + +.fa-volume-low::before { + content: "\f027"; } + +.fa-volume-down::before { + content: "\f027"; } + +.fa-comment-slash::before { + content: "\f4b3"; } + +.fa-cloud-sun-rain::before { + content: "\f743"; } + +.fa-compress::before { + content: "\f066"; } + +.fa-wheat-awn::before { + content: "\e2cd"; } + +.fa-wheat-alt::before { + content: "\e2cd"; } + +.fa-ankh::before { + content: "\f644"; } + +.fa-hands-holding-child::before { + content: "\e4fa"; } + +.fa-asterisk::before { + content: "\2a"; } + +.fa-square-check::before { + content: "\f14a"; } + +.fa-check-square::before { + content: "\f14a"; } + +.fa-peseta-sign::before { + content: "\e221"; } + +.fa-heading::before { + content: "\f1dc"; } + +.fa-header::before { + content: "\f1dc"; } + +.fa-ghost::before { + content: "\f6e2"; } + +.fa-list::before { + content: "\f03a"; } + +.fa-list-squares::before { + content: "\f03a"; } + +.fa-square-phone-flip::before { + content: "\f87b"; } + +.fa-phone-square-alt::before { + content: "\f87b"; } + +.fa-cart-plus::before { + content: "\f217"; } + +.fa-gamepad::before { + content: "\f11b"; } + +.fa-circle-dot::before { + content: "\f192"; } + +.fa-dot-circle::before { + content: "\f192"; } + +.fa-face-dizzy::before { + content: "\f567"; } + +.fa-dizzy::before { + content: "\f567"; } + +.fa-egg::before { + content: "\f7fb"; } + +.fa-house-medical-circle-xmark::before { + content: "\e513"; } + +.fa-campground::before { + content: "\f6bb"; } + +.fa-folder-plus::before { + content: "\f65e"; } + +.fa-futbol::before { + content: "\f1e3"; } + +.fa-futbol-ball::before { + content: "\f1e3"; } + +.fa-soccer-ball::before { + content: "\f1e3"; } + +.fa-paintbrush::before { + content: "\f1fc"; } + +.fa-paint-brush::before { + content: "\f1fc"; } + +.fa-lock::before { + content: "\f023"; } + +.fa-gas-pump::before { + content: "\f52f"; } + +.fa-hot-tub-person::before { + content: "\f593"; } + +.fa-hot-tub::before { + content: "\f593"; } + +.fa-map-location::before { + content: "\f59f"; } + +.fa-map-marked::before { + content: "\f59f"; } + +.fa-house-flood-water::before { + content: "\e50e"; } + +.fa-tree::before { + content: "\f1bb"; } + +.fa-bridge-lock::before { + content: "\e4cc"; } + +.fa-sack-dollar::before { + content: "\f81d"; } + +.fa-pen-to-square::before { + content: "\f044"; } + +.fa-edit::before { + content: "\f044"; } + +.fa-car-side::before { + content: "\f5e4"; } + +.fa-share-nodes::before { + content: "\f1e0"; } + +.fa-share-alt::before { + content: "\f1e0"; } + +.fa-heart-circle-minus::before { + content: "\e4ff"; } + +.fa-hourglass-half::before { + content: "\f252"; } + +.fa-hourglass-2::before { + content: "\f252"; } + +.fa-microscope::before { + content: "\f610"; } + +.fa-sink::before { + content: "\e06d"; } + +.fa-bag-shopping::before { + content: "\f290"; } + +.fa-shopping-bag::before { + content: "\f290"; } + +.fa-arrow-down-z-a::before { + content: "\f881"; } + +.fa-sort-alpha-desc::before { + content: "\f881"; } + +.fa-sort-alpha-down-alt::before { + content: "\f881"; } + +.fa-mitten::before { + content: "\f7b5"; } + +.fa-person-rays::before { + content: "\e54d"; } + 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content: "\e509"; } + +.fa-angle-left::before { + content: "\f104"; } + +.fa-diagram-successor::before { + content: "\e47a"; } + +.fa-truck-arrow-right::before { + content: "\e58b"; } + +.fa-arrows-split-up-and-left::before { + content: "\e4bc"; } + +.fa-hand-fist::before { + content: "\f6de"; } + +.fa-fist-raised::before { + content: "\f6de"; } + +.fa-cloud-moon::before { + content: "\f6c3"; } + +.fa-briefcase::before { + content: "\f0b1"; } + +.fa-person-falling::before { + content: "\e546"; } + +.fa-image-portrait::before { + content: "\f3e0"; } + +.fa-portrait::before { + content: "\f3e0"; } + +.fa-user-tag::before { + content: "\f507"; } + +.fa-rug::before { + content: "\e569"; } + +.fa-earth-europe::before { + content: "\f7a2"; } + +.fa-globe-europe::before { + content: "\f7a2"; } + +.fa-cart-flatbed-suitcase::before { + content: "\f59d"; } + +.fa-luggage-cart::before { + content: "\f59d"; } + +.fa-rectangle-xmark::before { + content: "\f410"; } + +.fa-rectangle-times::before { + content: "\f410"; } + +.fa-times-rectangle::before { + content: "\f410"; } + +.fa-window-close::before { + content: "\f410"; } + +.fa-baht-sign::before { + content: "\e0ac"; } + +.fa-book-open::before { + content: "\f518"; } + +.fa-book-journal-whills::before { + content: "\f66a"; } + +.fa-journal-whills::before { + content: "\f66a"; } + +.fa-handcuffs::before { + content: "\e4f8"; } + +.fa-triangle-exclamation::before { + content: "\f071"; } + +.fa-exclamation-triangle::before { + content: "\f071"; } + +.fa-warning::before { + content: "\f071"; } + +.fa-database::before { + content: "\f1c0"; } + +.fa-share::before { + content: "\f064"; } + +.fa-arrow-turn-right::before { + content: "\f064"; } + +.fa-mail-forward::before { + content: "\f064"; } + +.fa-bottle-droplet::before { + content: "\e4c4"; } + +.fa-mask-face::before { + content: "\e1d7"; } + +.fa-hill-rockslide::before { + content: "\e508"; } + +.fa-right-left::before { + content: "\f362"; } + +.fa-exchange-alt::before { + content: "\f362"; } + +.fa-paper-plane::before { + content: "\f1d8"; } + +.fa-road-circle-exclamation::before { + content: "\e565"; } + +.fa-dungeon::before { + content: "\f6d9"; } + +.fa-align-right::before { + content: "\f038"; } + +.fa-money-bill-1-wave::before { + content: "\f53b"; } + +.fa-money-bill-wave-alt::before { + content: "\f53b"; } + +.fa-life-ring::before { + content: "\f1cd"; } + +.fa-hands::before { + content: "\f2a7"; } + +.fa-sign-language::before { + content: "\f2a7"; } + +.fa-signing::before { + content: "\f2a7"; } + +.fa-calendar-day::before { + content: "\f783"; } + +.fa-water-ladder::before { + content: "\f5c5"; } + +.fa-ladder-water::before { + content: "\f5c5"; } + +.fa-swimming-pool::before { + content: "\f5c5"; } + +.fa-arrows-up-down::before { + content: "\f07d"; } + +.fa-arrows-v::before { + content: "\f07d"; } + +.fa-face-grimace::before { + content: "\f57f"; } + +.fa-grimace::before { + content: "\f57f"; } + +.fa-wheelchair-move::before { + content: 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content: "\e5b4"; } + +.fa-user-nurse::before { + content: "\f82f"; } + +.fa-syringe::before { + content: "\f48e"; } + +.fa-cloud-sun::before { + content: "\f6c4"; } + +.fa-stopwatch-20::before { + content: "\e06f"; } + +.fa-square-full::before { + content: "\f45c"; } + +.fa-magnet::before { + content: "\f076"; } + +.fa-jar::before { + content: "\e516"; } + +.fa-note-sticky::before { + content: "\f249"; } + +.fa-sticky-note::before { + content: "\f249"; } + +.fa-bug-slash::before { + content: "\e490"; } + +.fa-arrow-up-from-water-pump::before { + content: "\e4b6"; } + +.fa-bone::before { + content: "\f5d7"; } + +.fa-user-injured::before { + content: "\f728"; } + +.fa-face-sad-tear::before { + content: "\f5b4"; } + +.fa-sad-tear::before { + content: "\f5b4"; } + +.fa-plane::before { + content: "\f072"; } + +.fa-tent-arrows-down::before { + content: "\e581"; } + +.fa-exclamation::before { + content: "\21"; } + +.fa-arrows-spin::before { + content: "\e4bb"; } + +.fa-print::before { + content: "\f02f"; } + +.fa-turkish-lira-sign::before { + content: "\e2bb"; } + +.fa-try::before { + content: "\e2bb"; } + +.fa-turkish-lira::before { + content: "\e2bb"; } + +.fa-dollar-sign::before { + content: "\24"; } + +.fa-dollar::before { + content: "\24"; } + +.fa-usd::before { + content: "\24"; } + +.fa-x::before { + content: "\58"; } + +.fa-magnifying-glass-dollar::before { + content: "\f688"; } + +.fa-search-dollar::before { + content: "\f688"; } + +.fa-users-gear::before { + content: "\f509"; } + +.fa-users-cog::before { + content: "\f509"; } + +.fa-person-military-pointing::before { + content: "\e54a"; } + +.fa-building-columns::before { + content: "\f19c"; } + +.fa-bank::before { + content: "\f19c"; } + +.fa-institution::before { + content: "\f19c"; } + +.fa-museum::before { + content: "\f19c"; } + +.fa-university::before { + content: "\f19c"; } + +.fa-umbrella::before { + content: "\f0e9"; } + +.fa-trowel::before { + content: "\e589"; } + +.fa-d::before { + content: 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"\e53e"; } + +.fa-turn-up::before { + content: "\f3bf"; } + +.fa-level-up-alt::before { + content: "\f3bf"; } + +.sr-only, +.fa-sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border-width: 0; } + +.sr-only-focusable:not(:focus), +.fa-sr-only-focusable:not(:focus) { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border-width: 0; } +:root, :host { + --fa-style-family-brands: 'Font Awesome 6 Brands'; + --fa-font-brands: normal 400 1em/1 'Font Awesome 6 Brands'; } + +@font-face { + font-family: 'Font Awesome 6 Brands'; + font-style: normal; + font-weight: 400; + font-display: block; + src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); } + +.fab, +.fa-brands { + font-weight: 400; } + +.fa-monero:before { + content: "\f3d0"; } + +.fa-hooli:before { + content: "\f427"; } + +.fa-yelp:before { + content: "\f1e9"; } + +.fa-cc-visa:before { + content: "\f1f0"; } + +.fa-lastfm:before { + content: "\f202"; } + +.fa-shopware:before { + content: "\f5b5"; } + +.fa-creative-commons-nc:before { + content: "\f4e8"; } + +.fa-aws:before { + content: "\f375"; } + +.fa-redhat:before { + content: "\f7bc"; } + +.fa-yoast:before { + content: "\f2b1"; } + +.fa-cloudflare:before { + content: "\e07d"; } + +.fa-ups:before { + content: "\f7e0"; } + +.fa-wpexplorer:before { + content: "\f2de"; } + +.fa-dyalog:before { + content: "\f399"; } + +.fa-bity:before { + content: "\f37a"; } + +.fa-stackpath:before { + content: "\f842"; } + +.fa-buysellads:before { + content: "\f20d"; } + +.fa-first-order:before { + content: "\f2b0"; } + +.fa-modx:before { + content: "\f285"; } + +.fa-guilded:before { + content: "\e07e"; } + +.fa-vnv:before { + content: "\f40b"; } + +.fa-square-js:before { + content: "\f3b9"; } + 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content: "\f7d3"; } + +.fa-google-plus:before { + content: "\f2b3"; } + +.fa-diaspora:before { + content: "\f791"; } + +.fa-foursquare:before { + content: "\f180"; } + +.fa-stack-overflow:before { + content: "\f16c"; } + +.fa-github-alt:before { + content: "\f113"; } + +.fa-phoenix-squadron:before { + content: "\f511"; } + +.fa-pagelines:before { + content: "\f18c"; } + +.fa-algolia:before { + content: "\f36c"; } + +.fa-red-river:before { + content: "\f3e3"; } + +.fa-creative-commons-sa:before { + content: "\f4ef"; } + +.fa-safari:before { + content: "\f267"; } + +.fa-google:before { + content: "\f1a0"; } + +.fa-square-font-awesome-stroke:before { + content: "\f35c"; } + +.fa-font-awesome-alt:before { + content: "\f35c"; } + +.fa-atlassian:before { + content: "\f77b"; } + +.fa-linkedin-in:before { + content: "\f0e1"; } + +.fa-digital-ocean:before { + content: "\f391"; } + +.fa-nimblr:before { + content: "\f5a8"; } + +.fa-chromecast:before { + content: "\f838"; } + +.fa-evernote:before 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+.fa.fa-external-link:before { + content: "\f35d"; } + +.fa.fa-sign-in:before { + content: "\f2f6"; } + +.fa.fa-github-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-github-square:before { + content: "\f092"; } + +.fa.fa-lemon-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-lemon-o:before { + content: "\f094"; } + +.fa.fa-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-square-o:before { + content: "\f0c8"; } + +.fa.fa-bookmark-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-bookmark-o:before { + content: "\f02e"; } + +.fa.fa-twitter { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook:before { + content: "\f39e"; } + +.fa.fa-facebook-f { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook-f:before { + content: "\f39e"; } + +.fa.fa-github { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-credit-card { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-feed:before { + content: "\f09e"; } + +.fa.fa-hdd-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hdd-o:before { + content: "\f0a0"; } + +.fa.fa-hand-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-right:before { + content: "\f0a4"; } + +.fa.fa-hand-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-left:before { + content: "\f0a5"; } + +.fa.fa-hand-o-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-up:before { + content: "\f0a6"; } + +.fa.fa-hand-o-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-down:before { + content: "\f0a7"; } + +.fa.fa-globe:before { + content: "\f57d"; } + +.fa.fa-tasks:before { + content: "\f828"; } + +.fa.fa-arrows-alt:before { + content: "\f31e"; } + +.fa.fa-group:before { + content: "\f0c0"; } + +.fa.fa-chain:before { + content: "\f0c1"; } + +.fa.fa-cut:before { + content: "\f0c4"; } + +.fa.fa-files-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-files-o:before { + content: "\f0c5"; } + +.fa.fa-floppy-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-floppy-o:before { + content: "\f0c7"; } + +.fa.fa-save { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-save:before { + content: "\f0c7"; } + +.fa.fa-navicon:before { + content: "\f0c9"; } + +.fa.fa-reorder:before { + content: "\f0c9"; } + +.fa.fa-magic:before { + content: "\e2ca"; } + +.fa.fa-pinterest { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pinterest-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pinterest-square:before { + content: "\f0d3"; } + +.fa.fa-google-plus-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus-square:before { + content: "\f0d4"; } + +.fa.fa-google-plus { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus:before { + content: "\f0d5"; } + +.fa.fa-money:before { + content: "\f3d1"; } + +.fa.fa-unsorted:before { + content: "\f0dc"; } + +.fa.fa-sort-desc:before { + content: "\f0dd"; } + +.fa.fa-sort-asc:before { + content: "\f0de"; } + +.fa.fa-linkedin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-linkedin:before { + content: "\f0e1"; } + +.fa.fa-rotate-left:before { + content: "\f0e2"; } + +.fa.fa-legal:before { + content: "\f0e3"; } + +.fa.fa-tachometer:before { + content: "\f625"; } + +.fa.fa-dashboard:before { + content: "\f625"; } + +.fa.fa-comment-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-comment-o:before { + content: "\f075"; } + +.fa.fa-comments-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-comments-o:before { + content: "\f086"; } + +.fa.fa-flash:before { + content: "\f0e7"; } + +.fa.fa-clipboard:before { + content: "\f0ea"; } + +.fa.fa-lightbulb-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-lightbulb-o:before { + content: "\f0eb"; } + +.fa.fa-exchange:before { + content: "\f362"; } + +.fa.fa-cloud-download:before { + content: "\f0ed"; } + +.fa.fa-cloud-upload:before { + content: "\f0ee"; } + +.fa.fa-bell-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-bell-o:before { + content: "\f0f3"; } + +.fa.fa-cutlery:before { + content: "\f2e7"; } + +.fa.fa-file-text-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-text-o:before { + content: "\f15c"; } + +.fa.fa-building-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-building-o:before { + content: "\f1ad"; } + +.fa.fa-hospital-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hospital-o:before { + content: "\f0f8"; } + +.fa.fa-tablet:before { + content: "\f3fa"; } + +.fa.fa-mobile:before { + content: "\f3cd"; } + +.fa.fa-mobile-phone:before { + content: "\f3cd"; } + +.fa.fa-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-circle-o:before { + content: "\f111"; } + +.fa.fa-mail-reply:before { + content: "\f3e5"; } + +.fa.fa-github-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-folder-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-folder-o:before { + content: "\f07b"; } + +.fa.fa-folder-open-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-folder-open-o:before { + content: "\f07c"; } + +.fa.fa-smile-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-smile-o:before { + content: "\f118"; } + +.fa.fa-frown-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-frown-o:before { + content: "\f119"; } + +.fa.fa-meh-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-meh-o:before { + content: "\f11a"; } + +.fa.fa-keyboard-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-keyboard-o:before { + content: "\f11c"; } + +.fa.fa-flag-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-flag-o:before { + content: "\f024"; } + +.fa.fa-mail-reply-all:before { + content: "\f122"; } + +.fa.fa-star-half-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-o:before { + content: "\f5c0"; } + +.fa.fa-star-half-empty { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-empty:before { + content: "\f5c0"; } + +.fa.fa-star-half-full { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-full:before { + content: "\f5c0"; } + +.fa.fa-code-fork:before { + content: "\f126"; } + +.fa.fa-chain-broken:before { + content: "\f127"; } + +.fa.fa-unlink:before { + content: "\f127"; } + +.fa.fa-calendar-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-o:before { + content: "\f133"; } + +.fa.fa-maxcdn { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-html5 { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-css3 { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-unlock-alt:before { + content: "\f09c"; } + +.fa.fa-minus-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-minus-square-o:before { + content: "\f146"; } + +.fa.fa-level-up:before { + content: "\f3bf"; } + +.fa.fa-level-down:before { + content: "\f3be"; } + +.fa.fa-pencil-square:before { + content: "\f14b"; } + +.fa.fa-external-link-square:before { + content: "\f360"; } + +.fa.fa-compass { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-down:before { + content: "\f150"; } + +.fa.fa-toggle-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-down:before { + content: "\f150"; } + +.fa.fa-caret-square-o-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-up:before { + content: "\f151"; } + +.fa.fa-toggle-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-up:before { + content: "\f151"; } + +.fa.fa-caret-square-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-right:before { + content: "\f152"; } + +.fa.fa-toggle-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-right:before { + content: "\f152"; } + +.fa.fa-eur:before { + content: "\f153"; } + +.fa.fa-euro:before { + content: "\f153"; } + +.fa.fa-gbp:before { + content: "\f154"; } + +.fa.fa-usd:before { + content: "\24"; } + +.fa.fa-dollar:before { + content: "\24"; } + +.fa.fa-inr:before { + content: "\e1bc"; } + +.fa.fa-rupee:before { + content: "\e1bc"; } + +.fa.fa-jpy:before { + content: "\f157"; } + +.fa.fa-cny:before { + content: "\f157"; } + +.fa.fa-rmb:before { + content: "\f157"; } + +.fa.fa-yen:before { + content: "\f157"; } + +.fa.fa-rub:before { + content: "\f158"; } + +.fa.fa-ruble:before { + content: "\f158"; } + +.fa.fa-rouble:before { + content: "\f158"; } + +.fa.fa-krw:before { + content: "\f159"; } + +.fa.fa-won:before { + content: "\f159"; } + +.fa.fa-btc { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitcoin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitcoin:before { + content: "\f15a"; } + +.fa.fa-file-text:before { + content: "\f15c"; } + +.fa.fa-sort-alpha-asc:before { + content: "\f15d"; } + +.fa.fa-sort-alpha-desc:before { + content: "\f881"; } + +.fa.fa-sort-amount-asc:before { + content: "\f884"; } + +.fa.fa-sort-amount-desc:before { + content: "\f160"; } + +.fa.fa-sort-numeric-asc:before { + content: "\f162"; } + +.fa.fa-sort-numeric-desc:before { + content: "\f886"; } + +.fa.fa-youtube-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-youtube-square:before { + content: "\f431"; } + +.fa.fa-youtube { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing-square:before { + content: "\f169"; } + +.fa.fa-youtube-play { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-youtube-play:before { + content: "\f167"; } + +.fa.fa-dropbox { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stack-overflow { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-instagram { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-flickr { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-adn { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket-square:before { + content: "\f171"; } + +.fa.fa-tumblr { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-tumblr-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-tumblr-square:before { + content: "\f174"; } + +.fa.fa-long-arrow-down:before { + content: "\f309"; } + +.fa.fa-long-arrow-up:before { + content: "\f30c"; } + +.fa.fa-long-arrow-left:before { + content: "\f30a"; } + +.fa.fa-long-arrow-right:before { + content: "\f30b"; } + +.fa.fa-apple { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-windows { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-android { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-linux { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-dribbble { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-skype { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-foursquare { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-trello { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gratipay { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gittip { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gittip:before { + content: "\f184"; } + +.fa.fa-sun-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-sun-o:before { + content: "\f185"; } + +.fa.fa-moon-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-moon-o:before { + content: "\f186"; } + +.fa.fa-vk { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-weibo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-renren { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pagelines { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stack-exchange { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-right:before { + content: "\f35a"; } + +.fa.fa-arrow-circle-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-left:before { + content: "\f359"; } + +.fa.fa-caret-square-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-left:before { + content: "\f191"; } + +.fa.fa-toggle-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-left:before { + content: "\f191"; } + +.fa.fa-dot-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-dot-circle-o:before { + content: "\f192"; } + +.fa.fa-vimeo-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-vimeo-square:before { + content: "\f194"; } + +.fa.fa-try:before { + content: "\e2bb"; } + +.fa.fa-turkish-lira:before { + content: "\e2bb"; } + +.fa.fa-plus-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-plus-square-o:before { + content: "\f0fe"; } + +.fa.fa-slack { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wordpress { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-openid { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-institution:before { + content: "\f19c"; } + +.fa.fa-bank:before { + content: "\f19c"; } + +.fa.fa-mortar-board:before { + content: "\f19d"; } + +.fa.fa-yahoo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-square:before { + content: "\f1a2"; } + +.fa.fa-stumbleupon-circle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stumbleupon { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-delicious { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-digg { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pied-piper-pp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pied-piper-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-drupal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-joomla { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance-square:before { + content: "\f1b5"; } + +.fa.fa-steam { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-steam-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-steam-square:before { + content: "\f1b7"; } + +.fa.fa-automobile:before { + content: "\f1b9"; } + +.fa.fa-cab:before { + content: "\f1ba"; } + +.fa.fa-spotify { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-deviantart { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-soundcloud { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-file-pdf-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-pdf-o:before { + content: "\f1c1"; } + +.fa.fa-file-word-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-word-o:before { + content: "\f1c2"; } + +.fa.fa-file-excel-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-excel-o:before { + content: "\f1c3"; } + +.fa.fa-file-powerpoint-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-powerpoint-o:before { + content: "\f1c4"; } + +.fa.fa-file-image-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-image-o:before { + content: "\f1c5"; } + +.fa.fa-file-photo-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-photo-o:before { + content: "\f1c5"; } + +.fa.fa-file-picture-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-picture-o:before { + content: "\f1c5"; } + +.fa.fa-file-archive-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-archive-o:before { + content: "\f1c6"; } + +.fa.fa-file-zip-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-zip-o:before { + content: "\f1c6"; } + +.fa.fa-file-audio-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-audio-o:before { + content: "\f1c7"; } + +.fa.fa-file-sound-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-sound-o:before { + content: "\f1c7"; } + +.fa.fa-file-video-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-video-o:before { + content: "\f1c8"; } + +.fa.fa-file-movie-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-movie-o:before { + content: "\f1c8"; } + +.fa.fa-file-code-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-code-o:before { + content: "\f1c9"; } + +.fa.fa-vine { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-codepen { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-jsfiddle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-life-bouy:before { + content: "\f1cd"; } + +.fa.fa-life-buoy:before { + content: "\f1cd"; } + +.fa.fa-life-saver:before { + content: "\f1cd"; } + +.fa.fa-support:before { + content: "\f1cd"; } + +.fa.fa-circle-o-notch:before { + content: "\f1ce"; } + +.fa.fa-rebel { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ra { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ra:before { + content: "\f1d0"; } + +.fa.fa-resistance { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-resistance:before { + content: "\f1d0"; } + +.fa.fa-empire { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ge { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ge:before { + content: "\f1d1"; } + +.fa.fa-git-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-git-square:before { + content: "\f1d2"; } + +.fa.fa-git { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-hacker-news { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator-square:before { + content: "\f1d4"; } + +.fa.fa-yc-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yc-square:before { + content: "\f1d4"; } + +.fa.fa-tencent-weibo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-qq { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-weixin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wechat { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wechat:before { + content: "\f1d7"; } + +.fa.fa-send:before { + content: "\f1d8"; } + +.fa.fa-paper-plane-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-paper-plane-o:before { + content: "\f1d8"; } + +.fa.fa-send-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-send-o:before { + content: "\f1d8"; } + +.fa.fa-circle-thin { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-circle-thin:before { + content: "\f111"; } + +.fa.fa-header:before { + content: "\f1dc"; } + +.fa.fa-futbol-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-futbol-o:before { + content: "\f1e3"; } + +.fa.fa-soccer-ball-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-soccer-ball-o:before { + content: "\f1e3"; } + +.fa.fa-slideshare { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-twitch { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yelp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-newspaper-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-newspaper-o:before { + content: "\f1ea"; } + +.fa.fa-paypal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-wallet { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-visa { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-mastercard { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-discover { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-amex { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-paypal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-stripe { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bell-slash-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-bell-slash-o:before { + content: "\f1f6"; } + +.fa.fa-trash:before { + content: "\f2ed"; } + +.fa.fa-copyright { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-eyedropper:before { + content: "\f1fb"; } + +.fa.fa-area-chart:before { + content: "\f1fe"; } + +.fa.fa-pie-chart:before { + content: "\f200"; } + +.fa.fa-line-chart:before { + content: "\f201"; } + +.fa.fa-lastfm { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-lastfm-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-lastfm-square:before { + content: "\f203"; } + +.fa.fa-ioxhost { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-angellist { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-cc:before { + content: "\f20a"; } + +.fa.fa-ils:before { + content: "\f20b"; } + +.fa.fa-shekel:before { + content: "\f20b"; } + +.fa.fa-sheqel:before { + content: "\f20b"; } + +.fa.fa-buysellads { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-connectdevelop { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-dashcube { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-forumbee { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-leanpub { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-sellsy { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-shirtsinbulk { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-simplybuilt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-skyatlas { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-diamond { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-diamond:before { + content: "\f3a5"; } + +.fa.fa-transgender:before { + content: "\f224"; } + +.fa.fa-intersex:before { + content: "\f224"; } + +.fa.fa-transgender-alt:before { + content: "\f225"; } + +.fa.fa-facebook-official { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook-official:before { + content: "\f09a"; } + +.fa.fa-pinterest-p { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-whatsapp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-hotel:before { + content: "\f236"; } + +.fa.fa-viacoin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-medium { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yc { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yc:before { + content: "\f23b"; } + +.fa.fa-optin-monster { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-opencart { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-expeditedssl { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-battery-4:before { + content: "\f240"; } + +.fa.fa-battery:before { + content: "\f240"; } + +.fa.fa-battery-3:before { + content: "\f241"; } + +.fa.fa-battery-2:before { + content: "\f242"; } + +.fa.fa-battery-1:before { + content: "\f243"; } + +.fa.fa-battery-0:before { + content: "\f244"; } + +.fa.fa-object-group { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-object-ungroup { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-sticky-note-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-sticky-note-o:before { + content: "\f249"; } + +.fa.fa-cc-jcb { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-diners-club { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-clone { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hourglass-o:before { + content: "\f254"; } + +.fa.fa-hourglass-1:before { + content: "\f251"; } + +.fa.fa-hourglass-2:before { + content: "\f252"; } + +.fa.fa-hourglass-3:before { + content: "\f253"; } + +.fa.fa-hand-rock-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-rock-o:before { + content: "\f255"; } + +.fa.fa-hand-grab-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-grab-o:before { + content: "\f255"; } + +.fa.fa-hand-paper-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-paper-o:before { + content: "\f256"; } + +.fa.fa-hand-stop-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-stop-o:before { + content: "\f256"; } + +.fa.fa-hand-scissors-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-scissors-o:before { + content: "\f257"; } + +.fa.fa-hand-lizard-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-lizard-o:before { + content: "\f258"; } + +.fa.fa-hand-spock-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-spock-o:before { + content: "\f259"; } + +.fa.fa-hand-pointer-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-pointer-o:before { + content: "\f25a"; } + +.fa.fa-hand-peace-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-peace-o:before { + content: "\f25b"; } + +.fa.fa-registered { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-creative-commons { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gg { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gg-circle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-odnoklassniki { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-odnoklassniki-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-odnoklassniki-square:before { + content: "\f264"; } + +.fa.fa-get-pocket { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wikipedia-w { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-safari { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-chrome { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-firefox { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-opera { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-internet-explorer { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-television:before { + content: "\f26c"; } + +.fa.fa-contao { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-500px { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-amazon { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-calendar-plus-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-plus-o:before { + content: "\f271"; } + +.fa.fa-calendar-minus-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-minus-o:before { + content: "\f272"; } + +.fa.fa-calendar-times-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-times-o:before { + content: "\f273"; } + +.fa.fa-calendar-check-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-check-o:before { + content: "\f274"; } + +.fa.fa-map-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-map-o:before { + content: "\f279"; } + +.fa.fa-commenting:before { + content: "\f4ad"; } + +.fa.fa-commenting-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-commenting-o:before { + content: "\f4ad"; } + +.fa.fa-houzz { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-vimeo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-vimeo:before { + content: "\f27d"; } + +.fa.fa-black-tie { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fonticons { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-alien { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-edge { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-credit-card-alt:before { + content: "\f09d"; } + +.fa.fa-codiepie { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-modx { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fort-awesome { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-usb { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-product-hunt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-mixcloud { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-scribd { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pause-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-pause-circle-o:before { + content: "\f28b"; } + +.fa.fa-stop-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-stop-circle-o:before { + content: "\f28d"; } + +.fa.fa-bluetooth { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bluetooth-b { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gitlab { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wpbeginner { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wpforms { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-envira { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wheelchair-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wheelchair-alt:before { + content: "\f368"; } + +.fa.fa-question-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-question-circle-o:before { + content: "\f059"; } + +.fa.fa-volume-control-phone:before { + content: "\f2a0"; } + +.fa.fa-asl-interpreting:before { + content: "\f2a3"; } + +.fa.fa-deafness:before { + content: "\f2a4"; } + +.fa.fa-hard-of-hearing:before { + content: "\f2a4"; } + +.fa.fa-glide { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-glide-g { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-signing:before { + content: "\f2a7"; } + +.fa.fa-viadeo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-viadeo-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-viadeo-square:before { + content: "\f2aa"; } + +.fa.fa-snapchat { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-snapchat-ghost { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-snapchat-ghost:before { + content: "\f2ab"; } + +.fa.fa-snapchat-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-snapchat-square:before { + content: "\f2ad"; } + +.fa.fa-pied-piper { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-first-order { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yoast { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; 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Hide your header until you need it + * Copyright (c) 2017 Nick Williams - http://wicky.nillia.ms/headroom.js + * License: MIT + */ + +!function(a){a&&(a.fn.headroom=function(b){return this.each(function(){var c=a(this),d=c.data("headroom"),e="object"==typeof b&&b;e=a.extend(!0,{},Headroom.options,e),d||(d=new Headroom(this,e),d.init(),c.data("headroom",d)),"string"==typeof b&&(d[b](),"destroy"===b&&c.removeData("headroom"))})},a("[data-headroom]").each(function(){var b=a(this);b.headroom(b.data())}))}(window.Zepto||window.jQuery); \ No newline at end of file diff --git a/deps/jquery-3.6.0/jquery-3.6.0.js b/deps/jquery-3.6.0/jquery-3.6.0.js new file mode 100644 index 0000000..fc6c299 --- /dev/null +++ b/deps/jquery-3.6.0/jquery-3.6.0.js @@ -0,0 +1,10881 @@ +/*! + * jQuery JavaScript Library v3.6.0 + * https://jquery.com/ + * + * Includes Sizzle.js + * https://sizzlejs.com/ + * + * Copyright OpenJS Foundation and other contributors + * Released under the MIT license + * https://jquery.org/license + * + * Date: 2021-03-02T17:08Z + */ +( function( global, factory ) { + + "use strict"; + + if ( typeof module === "object" && typeof module.exports === "object" ) { + + // For CommonJS and CommonJS-like environments where a proper `window` + // is present, execute the factory and get jQuery. + // For environments that do not have a `window` with a `document` + // (such as Node.js), expose a factory as module.exports. + // This accentuates the need for the creation of a real `window`. + // e.g. var jQuery = require("jquery")(window); + // See ticket #14549 for more info. + module.exports = global.document ? + factory( global, true ) : + function( w ) { + if ( !w.document ) { + throw new Error( "jQuery requires a window with a document" ); + } + return factory( w ); + }; + } else { + factory( global ); + } + +// Pass this if window is not defined yet +} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { + +// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 +// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode +// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common +// enough that all such attempts are guarded in a try block. +"use strict"; + +var arr = []; + +var getProto = Object.getPrototypeOf; + +var slice = arr.slice; + +var flat = arr.flat ? function( array ) { + return arr.flat.call( array ); +} : function( array ) { + return arr.concat.apply( [], array ); +}; + + +var push = arr.push; + +var indexOf = arr.indexOf; + +var class2type = {}; + +var toString = class2type.toString; + +var hasOwn = class2type.hasOwnProperty; + +var fnToString = hasOwn.toString; + +var ObjectFunctionString = fnToString.call( Object ); + +var support = {}; + +var isFunction = function isFunction( obj ) { + + // Support: Chrome <=57, Firefox <=52 + // In some browsers, typeof returns "function" for HTML elements + // (i.e., `typeof document.createElement( "object" ) === "function"`). + // We don't want to classify *any* DOM node as a function. + // Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5 + // Plus for old WebKit, typeof returns "function" for HTML collections + // (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756) + return typeof obj === "function" && typeof obj.nodeType !== "number" && + typeof obj.item !== "function"; + }; + + +var isWindow = function isWindow( obj ) { + return obj != null && obj === obj.window; + }; + + +var document = window.document; + + + + var preservedScriptAttributes = { + type: true, + src: true, + nonce: true, + noModule: true + }; + + function DOMEval( code, node, doc ) { + doc = doc || document; + + var i, val, + script = doc.createElement( "script" ); + + script.text = code; + if ( node ) { + for ( i in preservedScriptAttributes ) { + + // Support: Firefox 64+, Edge 18+ + // Some browsers don't support the "nonce" property on scripts. + // On the other hand, just using `getAttribute` is not enough as + // the `nonce` attribute is reset to an empty string whenever it + // becomes browsing-context connected. + // See https://github.com/whatwg/html/issues/2369 + // See https://html.spec.whatwg.org/#nonce-attributes + // The `node.getAttribute` check was added for the sake of + // `jQuery.globalEval` so that it can fake a nonce-containing node + // via an object. + val = node[ i ] || node.getAttribute && node.getAttribute( i ); + if ( val ) { + script.setAttribute( i, val ); + } + } + } + doc.head.appendChild( script ).parentNode.removeChild( script ); + } + + +function toType( obj ) { + if ( obj == null ) { + return obj + ""; + } + + // Support: Android <=2.3 only (functionish RegExp) + return typeof obj === "object" || typeof obj === "function" ? + class2type[ toString.call( obj ) ] || "object" : + typeof obj; +} +/* global Symbol */ +// Defining this global in .eslintrc.json would create a danger of using the global +// unguarded in another place, it seems safer to define global only for this module + + + +var + version = "3.6.0", + + // Define a local copy of jQuery + jQuery = function( selector, context ) { + + // The jQuery object is actually just the init constructor 'enhanced' + // Need init if jQuery is called (just allow error to be thrown if not included) + return new jQuery.fn.init( selector, context ); + }; + +jQuery.fn = jQuery.prototype = { + + // The current version of jQuery being used + jquery: version, + + constructor: jQuery, + + // The default length of a jQuery object is 0 + length: 0, + + toArray: function() { + return slice.call( this ); + }, + + // Get the Nth element in the matched element set OR + // Get the whole matched element set as a clean array + get: function( num ) { + + // Return all the elements in a clean array + if ( num == null ) { + return slice.call( this ); + } + + // Return just the one element from the set + return num < 0 ? this[ num + this.length ] : this[ num ]; + }, + + // Take an array of elements and push it onto the stack + // (returning the new matched element set) + pushStack: function( elems ) { + + // Build a new jQuery matched element set + var ret = jQuery.merge( this.constructor(), elems ); + + // Add the old object onto the stack (as a reference) + ret.prevObject = this; + + // Return the newly-formed element set + return ret; + }, + + // Execute a callback for every element in the matched set. + each: function( callback ) { + return jQuery.each( this, callback ); + }, + + map: function( callback ) { + return this.pushStack( jQuery.map( this, function( elem, i ) { + return callback.call( elem, i, elem ); + } ) ); + }, + + slice: function() { + return this.pushStack( slice.apply( this, arguments ) ); + }, + + first: function() { + return this.eq( 0 ); + }, + + last: function() { + return this.eq( -1 ); + }, + + even: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return ( i + 1 ) % 2; + } ) ); + }, + + odd: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return i % 2; + } ) ); + }, + + eq: function( i ) { + var len = this.length, + j = +i + ( i < 0 ? len : 0 ); + return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); + }, + + end: function() { + return this.prevObject || this.constructor(); + }, + + // For internal use only. + // Behaves like an Array's method, not like a jQuery method. + push: push, + sort: arr.sort, + splice: arr.splice +}; + +jQuery.extend = jQuery.fn.extend = function() { + var options, name, src, copy, copyIsArray, clone, + target = arguments[ 0 ] || {}, + i = 1, + length = arguments.length, + deep = false; + + // Handle a deep copy situation + if ( typeof target === "boolean" ) { + deep = target; + + // Skip the boolean and the target + target = arguments[ i ] || {}; + i++; + } + + // Handle case when target is a string or something (possible in deep copy) + if ( typeof target !== "object" && !isFunction( target ) ) { + target = {}; + } + + // Extend jQuery itself if only one argument is passed + if ( i === length ) { + target = this; + i--; + } + + for ( ; i < length; i++ ) { + + // Only deal with non-null/undefined values + if ( ( options = arguments[ i ] ) != null ) { + + // Extend the base object + for ( name in options ) { + copy = options[ name ]; + + // Prevent Object.prototype pollution + // Prevent never-ending loop + if ( name === "__proto__" || target === copy ) { + continue; + } + + // Recurse if we're merging plain objects or arrays + if ( deep && copy && ( jQuery.isPlainObject( copy ) || + ( copyIsArray = Array.isArray( copy ) ) ) ) { + src = target[ name ]; + + // Ensure proper type for the source value + if ( copyIsArray && !Array.isArray( src ) ) { + clone = []; + } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { + clone = {}; + } else { + clone = src; + } + copyIsArray = false; + + // Never move original objects, clone them + target[ name ] = jQuery.extend( deep, clone, copy ); + + // Don't bring in undefined values + } else if ( copy !== undefined ) { + target[ name ] = copy; + } + } + } + } + + // Return the modified object + return target; +}; + +jQuery.extend( { + + // Unique for each copy of jQuery on the page + expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), + + // Assume jQuery is ready without the ready module + isReady: true, + + error: function( msg ) { + throw new Error( msg ); + }, + + noop: function() {}, + + isPlainObject: function( obj ) { + var proto, Ctor; + + // Detect obvious negatives + // Use toString instead of jQuery.type to catch host objects + if ( !obj || toString.call( obj ) !== "[object Object]" ) { + return false; + } + + proto = getProto( obj ); + + // Objects with no prototype (e.g., `Object.create( null )`) are plain + if ( !proto ) { + return true; + } + + // Objects with prototype are plain iff they were constructed by a global Object function + Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; + return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; + }, + + isEmptyObject: function( obj ) { + var name; + + for ( name in obj ) { + return false; + } + return true; + }, + + // Evaluates a script in a provided context; falls back to the global one + // if not specified. + globalEval: function( code, options, doc ) { + DOMEval( code, { nonce: options && options.nonce }, doc ); + }, + + each: function( obj, callback ) { + var length, i = 0; + + if ( isArrayLike( obj ) ) { + length = obj.length; + for ( ; i < length; i++ ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } else { + for ( i in obj ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } + + return obj; + }, + + // results is for internal usage only + makeArray: function( arr, results ) { + var ret = results || []; + + if ( arr != null ) { + if ( isArrayLike( Object( arr ) ) ) { + jQuery.merge( ret, + typeof arr === "string" ? + [ arr ] : arr + ); + } else { + push.call( ret, arr ); + } + } + + return ret; + }, + + inArray: function( elem, arr, i ) { + return arr == null ? -1 : indexOf.call( arr, elem, i ); + }, + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + merge: function( first, second ) { + var len = +second.length, + j = 0, + i = first.length; + + for ( ; j < len; j++ ) { + first[ i++ ] = second[ j ]; + } + + first.length = i; + + return first; + }, + + grep: function( elems, callback, invert ) { + var callbackInverse, + matches = [], + i = 0, + length = elems.length, + callbackExpect = !invert; + + // Go through the array, only saving the items + // that pass the validator function + for ( ; i < length; i++ ) { + callbackInverse = !callback( elems[ i ], i ); + if ( callbackInverse !== callbackExpect ) { + matches.push( elems[ i ] ); + } + } + + return matches; + }, + + // arg is for internal usage only + map: function( elems, callback, arg ) { + var length, value, + i = 0, + ret = []; + + // Go through the array, translating each of the items to their new values + if ( isArrayLike( elems ) ) { + length = elems.length; + for ( ; i < length; i++ ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + + // Go through every key on the object, + } else { + for ( i in elems ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + } + + // Flatten any nested arrays + return flat( ret ); + }, + + // A global GUID counter for objects + guid: 1, + + // jQuery.support is not used in Core but other projects attach their + // properties to it so it needs to exist. + support: support +} ); + +if ( typeof Symbol === "function" ) { + jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; +} + +// Populate the class2type map +jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), + function( _i, name ) { + class2type[ "[object " + name + "]" ] = name.toLowerCase(); + } ); + +function isArrayLike( obj ) { + + // Support: real iOS 8.2 only (not reproducible in simulator) + // `in` check used to prevent JIT error (gh-2145) + // hasOwn isn't used here due to false negatives + // regarding Nodelist length in IE + var length = !!obj && "length" in obj && obj.length, + type = toType( obj ); + + if ( isFunction( obj ) || isWindow( obj ) ) { + return false; + } + + return type === "array" || length === 0 || + typeof length === "number" && length > 0 && ( length - 1 ) in obj; +} +var Sizzle = +/*! + * Sizzle CSS Selector Engine v2.3.6 + * https://sizzlejs.com/ + * + * Copyright JS Foundation and other contributors + * Released under the MIT license + * https://js.foundation/ + * + * Date: 2021-02-16 + */ +( function( window ) { +var i, + support, + Expr, + getText, + isXML, + tokenize, + compile, + select, + outermostContext, + sortInput, + hasDuplicate, + + // Local document vars + setDocument, + document, + docElem, + documentIsHTML, + rbuggyQSA, + rbuggyMatches, + matches, + contains, + + // Instance-specific data + expando = "sizzle" + 1 * new Date(), + preferredDoc = window.document, + dirruns = 0, + done = 0, + classCache = createCache(), + tokenCache = createCache(), + compilerCache = createCache(), + nonnativeSelectorCache = createCache(), + sortOrder = function( a, b ) { + if ( a === b ) { + hasDuplicate = true; + } + return 0; + }, + + // Instance methods + hasOwn = ( {} ).hasOwnProperty, + arr = [], + pop = arr.pop, + pushNative = arr.push, + push = arr.push, + slice = arr.slice, + + // Use a stripped-down indexOf as it's faster than native + // https://jsperf.com/thor-indexof-vs-for/5 + indexOf = function( list, elem ) { + var i = 0, + len = list.length; + for ( ; i < len; i++ ) { + if ( list[ i ] === elem ) { + return i; + } + } + return -1; + }, + + booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + + "ismap|loop|multiple|open|readonly|required|scoped", + + // Regular expressions + + // http://www.w3.org/TR/css3-selectors/#whitespace + whitespace = "[\\x20\\t\\r\\n\\f]", + + // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram + identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + + "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", + + // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors + attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + + + // Operator (capture 2) + "*([*^$|!~]?=)" + whitespace + + + // "Attribute values must be CSS identifiers [capture 5] + // or strings [capture 3 or capture 4]" + "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + + whitespace + "*\\]", + + pseudos = ":(" + identifier + ")(?:\\((" + + + // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: + // 1. quoted (capture 3; capture 4 or capture 5) + "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + + + // 2. simple (capture 6) + "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + + + // 3. anything else (capture 2) + ".*" + + ")\\)|)", + + // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter + rwhitespace = new RegExp( whitespace + "+", "g" ), + rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + + whitespace + "+$", "g" ), + + rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), + rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + + "*" ), + rdescend = new RegExp( whitespace + "|>" ), + + rpseudo = new RegExp( pseudos ), + ridentifier = new RegExp( "^" + identifier + "$" ), + + matchExpr = { + "ID": new RegExp( "^#(" + identifier + ")" ), + "CLASS": new RegExp( "^\\.(" + identifier + ")" ), + "TAG": new RegExp( "^(" + identifier + "|[*])" ), + "ATTR": new RegExp( "^" + attributes ), + "PSEUDO": new RegExp( "^" + pseudos ), + "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + + whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + + whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), + "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), + + // For use in libraries implementing .is() + // We use this for POS matching in `select` + "needsContext": new RegExp( "^" + whitespace + + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) + }, + + rhtml = /HTML$/i, + rinputs = /^(?:input|select|textarea|button)$/i, + rheader = /^h\d$/i, + + rnative = /^[^{]+\{\s*\[native \w/, + + // Easily-parseable/retrievable ID or TAG or CLASS selectors + rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, + + rsibling = /[+~]/, + + // CSS escapes + // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters + runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), + funescape = function( escape, nonHex ) { + var high = "0x" + escape.slice( 1 ) - 0x10000; + + return nonHex ? + + // Strip the backslash prefix from a non-hex escape sequence + nonHex : + + // Replace a hexadecimal escape sequence with the encoded Unicode code point + // Support: IE <=11+ + // For values outside the Basic Multilingual Plane (BMP), manually construct a + // surrogate pair + high < 0 ? + String.fromCharCode( high + 0x10000 ) : + String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); + }, + + // CSS string/identifier serialization + // https://drafts.csswg.org/cssom/#common-serializing-idioms + rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, + fcssescape = function( ch, asCodePoint ) { + if ( asCodePoint ) { + + // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER + if ( ch === "\0" ) { + return "\uFFFD"; + } + + // Control characters and (dependent upon position) numbers get escaped as code points + return ch.slice( 0, -1 ) + "\\" + + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; + } + + // Other potentially-special ASCII characters get backslash-escaped + return "\\" + ch; + }, + + // Used for iframes + // See setDocument() + // Removing the function wrapper causes a "Permission Denied" + // error in IE + unloadHandler = function() { + setDocument(); + }, + + inDisabledFieldset = addCombinator( + function( elem ) { + return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; + }, + { dir: "parentNode", next: "legend" } + ); + +// Optimize for push.apply( _, NodeList ) +try { + push.apply( + ( arr = slice.call( preferredDoc.childNodes ) ), + preferredDoc.childNodes + ); + + // Support: Android<4.0 + // Detect silently failing push.apply + // eslint-disable-next-line no-unused-expressions + arr[ preferredDoc.childNodes.length ].nodeType; +} catch ( e ) { + push = { apply: arr.length ? + + // Leverage slice if possible + function( target, els ) { + pushNative.apply( target, slice.call( els ) ); + } : + + // Support: IE<9 + // Otherwise append directly + function( target, els ) { + var j = target.length, + i = 0; + + // Can't trust NodeList.length + while ( ( target[ j++ ] = els[ i++ ] ) ) {} + target.length = j - 1; + } + }; +} + +function Sizzle( selector, context, results, seed ) { + var m, i, elem, nid, match, groups, newSelector, + newContext = context && context.ownerDocument, + + // nodeType defaults to 9, since context defaults to document + nodeType = context ? context.nodeType : 9; + + results = results || []; + + // Return early from calls with invalid selector or context + if ( typeof selector !== "string" || !selector || + nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { + + return results; + } + + // Try to shortcut find operations (as opposed to filters) in HTML documents + if ( !seed ) { + setDocument( context ); + context = context || document; + + if ( documentIsHTML ) { + + // If the selector is sufficiently simple, try using a "get*By*" DOM method + // (excepting DocumentFragment context, where the methods don't exist) + if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { + + // ID selector + if ( ( m = match[ 1 ] ) ) { + + // Document context + if ( nodeType === 9 ) { + if ( ( elem = context.getElementById( m ) ) ) { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( elem.id === m ) { + results.push( elem ); + return results; + } + } else { + return results; + } + + // Element context + } else { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( newContext && ( elem = newContext.getElementById( m ) ) && + contains( context, elem ) && + elem.id === m ) { + + results.push( elem ); + return results; + } + } + + // Type selector + } else if ( match[ 2 ] ) { + push.apply( results, context.getElementsByTagName( selector ) ); + return results; + + // Class selector + } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && + context.getElementsByClassName ) { + + push.apply( results, context.getElementsByClassName( m ) ); + return results; + } + } + + // Take advantage of querySelectorAll + if ( support.qsa && + !nonnativeSelectorCache[ selector + " " ] && + ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && + + // Support: IE 8 only + // Exclude object elements + ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { + + newSelector = selector; + newContext = context; + + // qSA considers elements outside a scoping root when evaluating child or + // descendant combinators, which is not what we want. + // In such cases, we work around the behavior by prefixing every selector in the + // list with an ID selector referencing the scope context. + // The technique has to be used as well when a leading combinator is used + // as such selectors are not recognized by querySelectorAll. + // Thanks to Andrew Dupont for this technique. + if ( nodeType === 1 && + ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { + + // Expand context for sibling selectors + newContext = rsibling.test( selector ) && testContext( context.parentNode ) || + context; + + // We can use :scope instead of the ID hack if the browser + // supports it & if we're not changing the context. + if ( newContext !== context || !support.scope ) { + + // Capture the context ID, setting it first if necessary + if ( ( nid = context.getAttribute( "id" ) ) ) { + nid = nid.replace( rcssescape, fcssescape ); + } else { + context.setAttribute( "id", ( nid = expando ) ); + } + } + + // Prefix every selector in the list + groups = tokenize( selector ); + i = groups.length; + while ( i-- ) { + groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + + toSelector( groups[ i ] ); + } + newSelector = groups.join( "," ); + } + + try { + push.apply( results, + newContext.querySelectorAll( newSelector ) + ); + return results; + } catch ( qsaError ) { + nonnativeSelectorCache( selector, true ); + } finally { + if ( nid === expando ) { + context.removeAttribute( "id" ); + } + } + } + } + } + + // All others + return select( selector.replace( rtrim, "$1" ), context, results, seed ); +} + +/** + * Create key-value caches of limited size + * @returns {function(string, object)} Returns the Object data after storing it on itself with + * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) + * deleting the oldest entry + */ +function createCache() { + var keys = []; + + function cache( key, value ) { + + // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) + if ( keys.push( key + " " ) > Expr.cacheLength ) { + + // Only keep the most recent entries + delete cache[ keys.shift() ]; + } + return ( cache[ key + " " ] = value ); + } + return cache; +} + +/** + * Mark a function for special use by Sizzle + * @param {Function} fn The function to mark + */ +function markFunction( fn ) { + fn[ expando ] = true; + return fn; +} + +/** + * Support testing using an element + * @param {Function} fn Passed the created element and returns a boolean result + */ +function assert( fn ) { + var el = document.createElement( "fieldset" ); + + try { + return !!fn( el ); + } catch ( e ) { + return false; + } finally { + + // Remove from its parent by default + if ( el.parentNode ) { + el.parentNode.removeChild( el ); + } + + // release memory in IE + el = null; + } +} + +/** + * Adds the same handler for all of the specified attrs + * @param {String} attrs Pipe-separated list of attributes + * @param {Function} handler The method that will be applied + */ +function addHandle( attrs, handler ) { + var arr = attrs.split( "|" ), + i = arr.length; + + while ( i-- ) { + Expr.attrHandle[ arr[ i ] ] = handler; + } +} + +/** + * Checks document order of two siblings + * @param {Element} a + * @param {Element} b + * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b + */ +function siblingCheck( a, b ) { + var cur = b && a, + diff = cur && a.nodeType === 1 && b.nodeType === 1 && + a.sourceIndex - b.sourceIndex; + + // Use IE sourceIndex if available on both nodes + if ( diff ) { + return diff; + } + + // Check if b follows a + if ( cur ) { + while ( ( cur = cur.nextSibling ) ) { + if ( cur === b ) { + return -1; + } + } + } + + return a ? 1 : -1; +} + +/** + * Returns a function to use in pseudos for input types + * @param {String} type + */ +function createInputPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for buttons + * @param {String} type + */ +function createButtonPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return ( name === "input" || name === "button" ) && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for :enabled/:disabled + * @param {Boolean} disabled true for :disabled; false for :enabled + */ +function createDisabledPseudo( disabled ) { + + // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable + return function( elem ) { + + // Only certain elements can match :enabled or :disabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled + if ( "form" in elem ) { + + // Check for inherited disabledness on relevant non-disabled elements: + // * listed form-associated elements in a disabled fieldset + // https://html.spec.whatwg.org/multipage/forms.html#category-listed + // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled + // * option elements in a disabled optgroup + // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled + // All such elements have a "form" property. + if ( elem.parentNode && elem.disabled === false ) { + + // Option elements defer to a parent optgroup if present + if ( "label" in elem ) { + if ( "label" in elem.parentNode ) { + return elem.parentNode.disabled === disabled; + } else { + return elem.disabled === disabled; + } + } + + // Support: IE 6 - 11 + // Use the isDisabled shortcut property to check for disabled fieldset ancestors + return elem.isDisabled === disabled || + + // Where there is no isDisabled, check manually + /* jshint -W018 */ + elem.isDisabled !== !disabled && + inDisabledFieldset( elem ) === disabled; + } + + return elem.disabled === disabled; + + // Try to winnow out elements that can't be disabled before trusting the disabled property. + // Some victims get caught in our net (label, legend, menu, track), but it shouldn't + // even exist on them, let alone have a boolean value. + } else if ( "label" in elem ) { + return elem.disabled === disabled; + } + + // Remaining elements are neither :enabled nor :disabled + return false; + }; +} + +/** + * Returns a function to use in pseudos for positionals + * @param {Function} fn + */ +function createPositionalPseudo( fn ) { + return markFunction( function( argument ) { + argument = +argument; + return markFunction( function( seed, matches ) { + var j, + matchIndexes = fn( [], seed.length, argument ), + i = matchIndexes.length; + + // Match elements found at the specified indexes + while ( i-- ) { + if ( seed[ ( j = matchIndexes[ i ] ) ] ) { + seed[ j ] = !( matches[ j ] = seed[ j ] ); + } + } + } ); + } ); +} + +/** + * Checks a node for validity as a Sizzle context + * @param {Element|Object=} context + * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value + */ +function testContext( context ) { + return context && typeof context.getElementsByTagName !== "undefined" && context; +} + +// Expose support vars for convenience +support = Sizzle.support = {}; + +/** + * Detects XML nodes + * @param {Element|Object} elem An element or a document + * @returns {Boolean} True iff elem is a non-HTML XML node + */ +isXML = Sizzle.isXML = function( elem ) { + var namespace = elem && elem.namespaceURI, + docElem = elem && ( elem.ownerDocument || elem ).documentElement; + + // Support: IE <=8 + // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes + // https://bugs.jquery.com/ticket/4833 + return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); +}; + +/** + * Sets document-related variables once based on the current document + * @param {Element|Object} [doc] An element or document object to use to set the document + * @returns {Object} Returns the current document + */ +setDocument = Sizzle.setDocument = function( node ) { + var hasCompare, subWindow, + doc = node ? node.ownerDocument || node : preferredDoc; + + // Return early if doc is invalid or already selected + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { + return document; + } + + // Update global variables + document = doc; + docElem = document.documentElement; + documentIsHTML = !isXML( document ); + + // Support: IE 9 - 11+, Edge 12 - 18+ + // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( preferredDoc != document && + ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { + + // Support: IE 11, Edge + if ( subWindow.addEventListener ) { + subWindow.addEventListener( "unload", unloadHandler, false ); + + // Support: IE 9 - 10 only + } else if ( subWindow.attachEvent ) { + subWindow.attachEvent( "onunload", unloadHandler ); + } + } + + // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, + // Safari 4 - 5 only, Opera <=11.6 - 12.x only + // IE/Edge & older browsers don't support the :scope pseudo-class. + // Support: Safari 6.0 only + // Safari 6.0 supports :scope but it's an alias of :root there. + support.scope = assert( function( el ) { + docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); + return typeof el.querySelectorAll !== "undefined" && + !el.querySelectorAll( ":scope fieldset div" ).length; + } ); + + /* Attributes + ---------------------------------------------------------------------- */ + + // Support: IE<8 + // Verify that getAttribute really returns attributes and not properties + // (excepting IE8 booleans) + support.attributes = assert( function( el ) { + el.className = "i"; + return !el.getAttribute( "className" ); + } ); + + /* getElement(s)By* + ---------------------------------------------------------------------- */ + + // Check if getElementsByTagName("*") returns only elements + support.getElementsByTagName = assert( function( el ) { + el.appendChild( document.createComment( "" ) ); + return !el.getElementsByTagName( "*" ).length; + } ); + + // Support: IE<9 + support.getElementsByClassName = rnative.test( document.getElementsByClassName ); + + // Support: IE<10 + // Check if getElementById returns elements by name + // The broken getElementById methods don't pick up programmatically-set names, + // so use a roundabout getElementsByName test + support.getById = assert( function( el ) { + docElem.appendChild( el ).id = expando; + return !document.getElementsByName || !document.getElementsByName( expando ).length; + } ); + + // ID filter and find + if ( support.getById ) { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + return elem.getAttribute( "id" ) === attrId; + }; + }; + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var elem = context.getElementById( id ); + return elem ? [ elem ] : []; + } + }; + } else { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + var node = typeof elem.getAttributeNode !== "undefined" && + elem.getAttributeNode( "id" ); + return node && node.value === attrId; + }; + }; + + // Support: IE 6 - 7 only + // getElementById is not reliable as a find shortcut + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var node, i, elems, + elem = context.getElementById( id ); + + if ( elem ) { + + // Verify the id attribute + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + + // Fall back on getElementsByName + elems = context.getElementsByName( id ); + i = 0; + while ( ( elem = elems[ i++ ] ) ) { + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + } + } + + return []; + } + }; + } + + // Tag + Expr.find[ "TAG" ] = support.getElementsByTagName ? + function( tag, context ) { + if ( typeof context.getElementsByTagName !== "undefined" ) { + return context.getElementsByTagName( tag ); + + // DocumentFragment nodes don't have gEBTN + } else if ( support.qsa ) { + return context.querySelectorAll( tag ); + } + } : + + function( tag, context ) { + var elem, + tmp = [], + i = 0, + + // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too + results = context.getElementsByTagName( tag ); + + // Filter out possible comments + if ( tag === "*" ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem.nodeType === 1 ) { + tmp.push( elem ); + } + } + + return tmp; + } + return results; + }; + + // Class + Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { + if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { + return context.getElementsByClassName( className ); + } + }; + + /* QSA/matchesSelector + ---------------------------------------------------------------------- */ + + // QSA and matchesSelector support + + // matchesSelector(:active) reports false when true (IE9/Opera 11.5) + rbuggyMatches = []; + + // qSa(:focus) reports false when true (Chrome 21) + // We allow this because of a bug in IE8/9 that throws an error + // whenever `document.activeElement` is accessed on an iframe + // So, we allow :focus to pass through QSA all the time to avoid the IE error + // See https://bugs.jquery.com/ticket/13378 + rbuggyQSA = []; + + if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { + + // Build QSA regex + // Regex strategy adopted from Diego Perini + assert( function( el ) { + + var input; + + // Select is set to empty string on purpose + // This is to test IE's treatment of not explicitly + // setting a boolean content attribute, + // since its presence should be enough + // https://bugs.jquery.com/ticket/12359 + docElem.appendChild( el ).innerHTML = "" + + ""; + + // Support: IE8, Opera 11-12.16 + // Nothing should be selected when empty strings follow ^= or $= or *= + // The test attribute must be unknown in Opera but "safe" for WinRT + // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section + if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { + rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); + } + + // Support: IE8 + // Boolean attributes and "value" are not treated correctly + if ( !el.querySelectorAll( "[selected]" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); + } + + // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ + if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { + rbuggyQSA.push( "~=" ); + } + + // Support: IE 11+, Edge 15 - 18+ + // IE 11/Edge don't find elements on a `[name='']` query in some cases. + // Adding a temporary attribute to the document before the selection works + // around the issue. + // Interestingly, IE 10 & older don't seem to have the issue. + input = document.createElement( "input" ); + input.setAttribute( "name", "" ); + el.appendChild( input ); + if ( !el.querySelectorAll( "[name='']" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + + whitespace + "*(?:''|\"\")" ); + } + + // Webkit/Opera - :checked should return selected option elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + // IE8 throws error here and will not see later tests + if ( !el.querySelectorAll( ":checked" ).length ) { + rbuggyQSA.push( ":checked" ); + } + + // Support: Safari 8+, iOS 8+ + // https://bugs.webkit.org/show_bug.cgi?id=136851 + // In-page `selector#id sibling-combinator selector` fails + if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { + rbuggyQSA.push( ".#.+[+~]" ); + } + + // Support: Firefox <=3.6 - 5 only + // Old Firefox doesn't throw on a badly-escaped identifier. + el.querySelectorAll( "\\\f" ); + rbuggyQSA.push( "[\\r\\n\\f]" ); + } ); + + assert( function( el ) { + el.innerHTML = "" + + ""; + + // Support: Windows 8 Native Apps + // The type and name attributes are restricted during .innerHTML assignment + var input = document.createElement( "input" ); + input.setAttribute( "type", "hidden" ); + el.appendChild( input ).setAttribute( "name", "D" ); + + // Support: IE8 + // Enforce case-sensitivity of name attribute + if ( el.querySelectorAll( "[name=d]" ).length ) { + rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); + } + + // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) + // IE8 throws error here and will not see later tests + if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: IE9-11+ + // IE's :disabled selector does not pick up the children of disabled fieldsets + docElem.appendChild( el ).disabled = true; + if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: Opera 10 - 11 only + // Opera 10-11 does not throw on post-comma invalid pseudos + el.querySelectorAll( "*,:x" ); + rbuggyQSA.push( ",.*:" ); + } ); + } + + if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || + docElem.webkitMatchesSelector || + docElem.mozMatchesSelector || + docElem.oMatchesSelector || + docElem.msMatchesSelector ) ) ) ) { + + assert( function( el ) { + + // Check to see if it's possible to do matchesSelector + // on a disconnected node (IE 9) + support.disconnectedMatch = matches.call( el, "*" ); + + // This should fail with an exception + // Gecko does not error, returns false instead + matches.call( el, "[s!='']:x" ); + rbuggyMatches.push( "!=", pseudos ); + } ); + } + + rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); + rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); + + /* Contains + ---------------------------------------------------------------------- */ + hasCompare = rnative.test( docElem.compareDocumentPosition ); + + // Element contains another + // Purposefully self-exclusive + // As in, an element does not contain itself + contains = hasCompare || rnative.test( docElem.contains ) ? + function( a, b ) { + var adown = a.nodeType === 9 ? a.documentElement : a, + bup = b && b.parentNode; + return a === bup || !!( bup && bup.nodeType === 1 && ( + adown.contains ? + adown.contains( bup ) : + a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 + ) ); + } : + function( a, b ) { + if ( b ) { + while ( ( b = b.parentNode ) ) { + if ( b === a ) { + return true; + } + } + } + return false; + }; + + /* Sorting + ---------------------------------------------------------------------- */ + + // Document order sorting + sortOrder = hasCompare ? + function( a, b ) { + + // Flag for duplicate removal + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + // Sort on method existence if only one input has compareDocumentPosition + var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; + if ( compare ) { + return compare; + } + + // Calculate position if both inputs belong to the same document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? + a.compareDocumentPosition( b ) : + + // Otherwise we know they are disconnected + 1; + + // Disconnected nodes + if ( compare & 1 || + ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { + + // Choose the first element that is related to our preferred document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( a == document || a.ownerDocument == preferredDoc && + contains( preferredDoc, a ) ) { + return -1; + } + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( b == document || b.ownerDocument == preferredDoc && + contains( preferredDoc, b ) ) { + return 1; + } + + // Maintain original order + return sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + } + + return compare & 4 ? -1 : 1; + } : + function( a, b ) { + + // Exit early if the nodes are identical + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + var cur, + i = 0, + aup = a.parentNode, + bup = b.parentNode, + ap = [ a ], + bp = [ b ]; + + // Parentless nodes are either documents or disconnected + if ( !aup || !bup ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + return a == document ? -1 : + b == document ? 1 : + /* eslint-enable eqeqeq */ + aup ? -1 : + bup ? 1 : + sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + + // If the nodes are siblings, we can do a quick check + } else if ( aup === bup ) { + return siblingCheck( a, b ); + } + + // Otherwise we need full lists of their ancestors for comparison + cur = a; + while ( ( cur = cur.parentNode ) ) { + ap.unshift( cur ); + } + cur = b; + while ( ( cur = cur.parentNode ) ) { + bp.unshift( cur ); + } + + // Walk down the tree looking for a discrepancy + while ( ap[ i ] === bp[ i ] ) { + i++; + } + + return i ? + + // Do a sibling check if the nodes have a common ancestor + siblingCheck( ap[ i ], bp[ i ] ) : + + // Otherwise nodes in our document sort first + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + ap[ i ] == preferredDoc ? -1 : + bp[ i ] == preferredDoc ? 1 : + /* eslint-enable eqeqeq */ + 0; + }; + + return document; +}; + +Sizzle.matches = function( expr, elements ) { + return Sizzle( expr, null, null, elements ); +}; + +Sizzle.matchesSelector = function( elem, expr ) { + setDocument( elem ); + + if ( support.matchesSelector && documentIsHTML && + !nonnativeSelectorCache[ expr + " " ] && + ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && + ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { + + try { + var ret = matches.call( elem, expr ); + + // IE 9's matchesSelector returns false on disconnected nodes + if ( ret || support.disconnectedMatch || + + // As well, disconnected nodes are said to be in a document + // fragment in IE 9 + elem.document && elem.document.nodeType !== 11 ) { + return ret; + } + } catch ( e ) { + nonnativeSelectorCache( expr, true ); + } + } + + return Sizzle( expr, document, null, [ elem ] ).length > 0; +}; + +Sizzle.contains = function( context, elem ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( context.ownerDocument || context ) != document ) { + setDocument( context ); + } + return contains( context, elem ); +}; + +Sizzle.attr = function( elem, name ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( elem.ownerDocument || elem ) != document ) { + setDocument( elem ); + } + + var fn = Expr.attrHandle[ name.toLowerCase() ], + + // Don't get fooled by Object.prototype properties (jQuery #13807) + val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? + fn( elem, name, !documentIsHTML ) : + undefined; + + return val !== undefined ? + val : + support.attributes || !documentIsHTML ? + elem.getAttribute( name ) : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; +}; + +Sizzle.escape = function( sel ) { + return ( sel + "" ).replace( rcssescape, fcssescape ); +}; + +Sizzle.error = function( msg ) { + throw new Error( "Syntax error, unrecognized expression: " + msg ); +}; + +/** + * Document sorting and removing duplicates + * @param {ArrayLike} results + */ +Sizzle.uniqueSort = function( results ) { + var elem, + duplicates = [], + j = 0, + i = 0; + + // Unless we *know* we can detect duplicates, assume their presence + hasDuplicate = !support.detectDuplicates; + sortInput = !support.sortStable && results.slice( 0 ); + results.sort( sortOrder ); + + if ( hasDuplicate ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem === results[ i ] ) { + j = duplicates.push( i ); + } + } + while ( j-- ) { + results.splice( duplicates[ j ], 1 ); + } + } + + // Clear input after sorting to release objects + // See https://github.com/jquery/sizzle/pull/225 + sortInput = null; + + return results; +}; + +/** + * Utility function for retrieving the text value of an array of DOM nodes + * @param {Array|Element} elem + */ +getText = Sizzle.getText = function( elem ) { + var node, + ret = "", + i = 0, + nodeType = elem.nodeType; + + if ( !nodeType ) { + + // If no nodeType, this is expected to be an array + while ( ( node = elem[ i++ ] ) ) { + + // Do not traverse comment nodes + ret += getText( node ); + } + } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { + + // Use textContent for elements + // innerText usage removed for consistency of new lines (jQuery #11153) + if ( typeof elem.textContent === "string" ) { + return elem.textContent; + } else { + + // Traverse its children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + ret += getText( elem ); + } + } + } else if ( nodeType === 3 || nodeType === 4 ) { + return elem.nodeValue; + } + + // Do not include comment or processing instruction nodes + + return ret; +}; + +Expr = Sizzle.selectors = { + + // Can be adjusted by the user + cacheLength: 50, + + createPseudo: markFunction, + + match: matchExpr, + + attrHandle: {}, + + find: {}, + + relative: { + ">": { dir: "parentNode", first: true }, + " ": { dir: "parentNode" }, + "+": { dir: "previousSibling", first: true }, + "~": { dir: "previousSibling" } + }, + + preFilter: { + "ATTR": function( match ) { + match[ 1 ] = match[ 1 ].replace( runescape, funescape ); + + // Move the given value to match[3] whether quoted or unquoted + match[ 3 ] = ( match[ 3 ] || match[ 4 ] || + match[ 5 ] || "" ).replace( runescape, funescape ); + + if ( match[ 2 ] === "~=" ) { + match[ 3 ] = " " + match[ 3 ] + " "; + } + + return match.slice( 0, 4 ); + }, + + "CHILD": function( match ) { + + /* matches from matchExpr["CHILD"] + 1 type (only|nth|...) + 2 what (child|of-type) + 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) + 4 xn-component of xn+y argument ([+-]?\d*n|) + 5 sign of xn-component + 6 x of xn-component + 7 sign of y-component + 8 y of y-component + */ + match[ 1 ] = match[ 1 ].toLowerCase(); + + if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { + + // nth-* requires argument + if ( !match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + // numeric x and y parameters for Expr.filter.CHILD + // remember that false/true cast respectively to 0/1 + match[ 4 ] = +( match[ 4 ] ? + match[ 5 ] + ( match[ 6 ] || 1 ) : + 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); + match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); + + // other types prohibit arguments + } else if ( match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + return match; + }, + + "PSEUDO": function( match ) { + var excess, + unquoted = !match[ 6 ] && match[ 2 ]; + + if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { + return null; + } + + // Accept quoted arguments as-is + if ( match[ 3 ] ) { + match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; + + // Strip excess characters from unquoted arguments + } else if ( unquoted && rpseudo.test( unquoted ) && + + // Get excess from tokenize (recursively) + ( excess = tokenize( unquoted, true ) ) && + + // advance to the next closing parenthesis + ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { + + // excess is a negative index + match[ 0 ] = match[ 0 ].slice( 0, excess ); + match[ 2 ] = unquoted.slice( 0, excess ); + } + + // Return only captures needed by the pseudo filter method (type and argument) + return match.slice( 0, 3 ); + } + }, + + filter: { + + "TAG": function( nodeNameSelector ) { + var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); + return nodeNameSelector === "*" ? + function() { + return true; + } : + function( elem ) { + return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; + }; + }, + + "CLASS": function( className ) { + var pattern = classCache[ className + " " ]; + + return pattern || + ( pattern = new RegExp( "(^|" + whitespace + + ")" + className + "(" + whitespace + "|$)" ) ) && classCache( + className, function( elem ) { + return pattern.test( + typeof elem.className === "string" && elem.className || + typeof elem.getAttribute !== "undefined" && + elem.getAttribute( "class" ) || + "" + ); + } ); + }, + + "ATTR": function( name, operator, check ) { + return function( elem ) { + var result = Sizzle.attr( elem, name ); + + if ( result == null ) { + return operator === "!="; + } + if ( !operator ) { + return true; + } + + result += ""; + + /* eslint-disable max-len */ + + return operator === "=" ? result === check : + operator === "!=" ? result !== check : + operator === "^=" ? check && result.indexOf( check ) === 0 : + operator === "*=" ? check && result.indexOf( check ) > -1 : + operator === "$=" ? check && result.slice( -check.length ) === check : + operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : + operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : + false; + /* eslint-enable max-len */ + + }; + }, + + "CHILD": function( type, what, _argument, first, last ) { + var simple = type.slice( 0, 3 ) !== "nth", + forward = type.slice( -4 ) !== "last", + ofType = what === "of-type"; + + return first === 1 && last === 0 ? + + // Shortcut for :nth-*(n) + function( elem ) { + return !!elem.parentNode; + } : + + function( elem, _context, xml ) { + var cache, uniqueCache, outerCache, node, nodeIndex, start, + dir = simple !== forward ? "nextSibling" : "previousSibling", + parent = elem.parentNode, + name = ofType && elem.nodeName.toLowerCase(), + useCache = !xml && !ofType, + diff = false; + + if ( parent ) { + + // :(first|last|only)-(child|of-type) + if ( simple ) { + while ( dir ) { + node = elem; + while ( ( node = node[ dir ] ) ) { + if ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) { + + return false; + } + } + + // Reverse direction for :only-* (if we haven't yet done so) + start = dir = type === "only" && !start && "nextSibling"; + } + return true; + } + + start = [ forward ? parent.firstChild : parent.lastChild ]; + + // non-xml :nth-child(...) stores cache data on `parent` + if ( forward && useCache ) { + + // Seek `elem` from a previously-cached index + + // ...in a gzip-friendly way + node = parent; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex && cache[ 2 ]; + node = nodeIndex && parent.childNodes[ nodeIndex ]; + + while ( ( node = ++nodeIndex && node && node[ dir ] || + + // Fallback to seeking `elem` from the start + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + // When found, cache indexes on `parent` and break + if ( node.nodeType === 1 && ++diff && node === elem ) { + uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; + break; + } + } + + } else { + + // Use previously-cached element index if available + if ( useCache ) { + + // ...in a gzip-friendly way + node = elem; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex; + } + + // xml :nth-child(...) + // or :nth-last-child(...) or :nth(-last)?-of-type(...) + if ( diff === false ) { + + // Use the same loop as above to seek `elem` from the start + while ( ( node = ++nodeIndex && node && node[ dir ] || + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + if ( ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) && + ++diff ) { + + // Cache the index of each encountered element + if ( useCache ) { + outerCache = node[ expando ] || + ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + uniqueCache[ type ] = [ dirruns, diff ]; + } + + if ( node === elem ) { + break; + } + } + } + } + } + + // Incorporate the offset, then check against cycle size + diff -= last; + return diff === first || ( diff % first === 0 && diff / first >= 0 ); + } + }; + }, + + "PSEUDO": function( pseudo, argument ) { + + // pseudo-class names are case-insensitive + // http://www.w3.org/TR/selectors/#pseudo-classes + // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters + // Remember that setFilters inherits from pseudos + var args, + fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || + Sizzle.error( "unsupported pseudo: " + pseudo ); + + // The user may use createPseudo to indicate that + // arguments are needed to create the filter function + // just as Sizzle does + if ( fn[ expando ] ) { + return fn( argument ); + } + + // But maintain support for old signatures + if ( fn.length > 1 ) { + args = [ pseudo, pseudo, "", argument ]; + return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? + markFunction( function( seed, matches ) { + var idx, + matched = fn( seed, argument ), + i = matched.length; + while ( i-- ) { + idx = indexOf( seed, matched[ i ] ); + seed[ idx ] = !( matches[ idx ] = matched[ i ] ); + } + } ) : + function( elem ) { + return fn( elem, 0, args ); + }; + } + + return fn; + } + }, + + pseudos: { + + // Potentially complex pseudos + "not": markFunction( function( selector ) { + + // Trim the selector passed to compile + // to avoid treating leading and trailing + // spaces as combinators + var input = [], + results = [], + matcher = compile( selector.replace( rtrim, "$1" ) ); + + return matcher[ expando ] ? + markFunction( function( seed, matches, _context, xml ) { + var elem, + unmatched = matcher( seed, null, xml, [] ), + i = seed.length; + + // Match elements unmatched by `matcher` + while ( i-- ) { + if ( ( elem = unmatched[ i ] ) ) { + seed[ i ] = !( matches[ i ] = elem ); + } + } + } ) : + function( elem, _context, xml ) { + input[ 0 ] = elem; + matcher( input, null, xml, results ); + + // Don't keep the element (issue #299) + input[ 0 ] = null; + return !results.pop(); + }; + } ), + + "has": markFunction( function( selector ) { + return function( elem ) { + return Sizzle( selector, elem ).length > 0; + }; + } ), + + "contains": markFunction( function( text ) { + text = text.replace( runescape, funescape ); + return function( elem ) { + return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; + }; + } ), + + // "Whether an element is represented by a :lang() selector + // is based solely on the element's language value + // being equal to the identifier C, + // or beginning with the identifier C immediately followed by "-". + // The matching of C against the element's language value is performed case-insensitively. + // The identifier C does not have to be a valid language name." + // http://www.w3.org/TR/selectors/#lang-pseudo + "lang": markFunction( function( lang ) { + + // lang value must be a valid identifier + if ( !ridentifier.test( lang || "" ) ) { + Sizzle.error( "unsupported lang: " + lang ); + } + lang = lang.replace( runescape, funescape ).toLowerCase(); + return function( elem ) { + var elemLang; + do { + if ( ( elemLang = documentIsHTML ? + elem.lang : + elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { + + elemLang = elemLang.toLowerCase(); + return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; + } + } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); + return false; + }; + } ), + + // Miscellaneous + "target": function( elem ) { + var hash = window.location && window.location.hash; + return hash && hash.slice( 1 ) === elem.id; + }, + + "root": function( elem ) { + return elem === docElem; + }, + + "focus": function( elem ) { + return elem === document.activeElement && + ( !document.hasFocus || document.hasFocus() ) && + !!( elem.type || elem.href || ~elem.tabIndex ); + }, + + // Boolean properties + "enabled": createDisabledPseudo( false ), + "disabled": createDisabledPseudo( true ), + + "checked": function( elem ) { + + // In CSS3, :checked should return both checked and selected elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + var nodeName = elem.nodeName.toLowerCase(); + return ( nodeName === "input" && !!elem.checked ) || + ( nodeName === "option" && !!elem.selected ); + }, + + "selected": function( elem ) { + + // Accessing this property makes selected-by-default + // options in Safari work properly + if ( elem.parentNode ) { + // eslint-disable-next-line no-unused-expressions + elem.parentNode.selectedIndex; + } + + return elem.selected === true; + }, + + // Contents + "empty": function( elem ) { + + // http://www.w3.org/TR/selectors/#empty-pseudo + // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), + // but not by others (comment: 8; processing instruction: 7; etc.) + // nodeType < 6 works because attributes (2) do not appear as children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + if ( elem.nodeType < 6 ) { + return false; + } + } + return true; + }, + + "parent": function( elem ) { + return !Expr.pseudos[ "empty" ]( elem ); + }, + + // Element/input types + "header": function( elem ) { + return rheader.test( elem.nodeName ); + }, + + "input": function( elem ) { + return rinputs.test( elem.nodeName ); + }, + + "button": function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === "button" || name === "button"; + }, + + "text": function( elem ) { + var attr; + return elem.nodeName.toLowerCase() === "input" && + elem.type === "text" && + + // Support: IE<8 + // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" + ( ( attr = elem.getAttribute( "type" ) ) == null || + attr.toLowerCase() === "text" ); + }, + + // Position-in-collection + "first": createPositionalPseudo( function() { + return [ 0 ]; + } ), + + "last": createPositionalPseudo( function( _matchIndexes, length ) { + return [ length - 1 ]; + } ), + + "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { + return [ argument < 0 ? argument + length : argument ]; + } ), + + "even": createPositionalPseudo( function( matchIndexes, length ) { + var i = 0; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "odd": createPositionalPseudo( function( matchIndexes, length ) { + var i = 1; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? + argument + length : + argument > length ? + length : + argument; + for ( ; --i >= 0; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? argument + length : argument; + for ( ; ++i < length; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ) + } +}; + +Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; + +// Add button/input type pseudos +for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { + Expr.pseudos[ i ] = createInputPseudo( i ); +} +for ( i in { submit: true, reset: true } ) { + Expr.pseudos[ i ] = createButtonPseudo( i ); +} + +// Easy API for creating new setFilters +function setFilters() {} +setFilters.prototype = Expr.filters = Expr.pseudos; +Expr.setFilters = new setFilters(); + +tokenize = Sizzle.tokenize = function( selector, parseOnly ) { + var matched, match, tokens, type, + soFar, groups, preFilters, + cached = tokenCache[ selector + " " ]; + + if ( cached ) { + return parseOnly ? 0 : cached.slice( 0 ); + } + + soFar = selector; + groups = []; + preFilters = Expr.preFilter; + + while ( soFar ) { + + // Comma and first run + if ( !matched || ( match = rcomma.exec( soFar ) ) ) { + if ( match ) { + + // Don't consume trailing commas as valid + soFar = soFar.slice( match[ 0 ].length ) || soFar; + } + groups.push( ( tokens = [] ) ); + } + + matched = false; + + // Combinators + if ( ( match = rcombinators.exec( soFar ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + + // Cast descendant combinators to space + type: match[ 0 ].replace( rtrim, " " ) + } ); + soFar = soFar.slice( matched.length ); + } + + // Filters + for ( type in Expr.filter ) { + if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || + ( match = preFilters[ type ]( match ) ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + type: type, + matches: match + } ); + soFar = soFar.slice( matched.length ); + } + } + + if ( !matched ) { + break; + } + } + + // Return the length of the invalid excess + // if we're just parsing + // Otherwise, throw an error or return tokens + return parseOnly ? + soFar.length : + soFar ? + Sizzle.error( selector ) : + + // Cache the tokens + tokenCache( selector, groups ).slice( 0 ); +}; + +function toSelector( tokens ) { + var i = 0, + len = tokens.length, + selector = ""; + for ( ; i < len; i++ ) { + selector += tokens[ i ].value; + } + return selector; +} + +function addCombinator( matcher, combinator, base ) { + var dir = combinator.dir, + skip = combinator.next, + key = skip || dir, + checkNonElements = base && key === "parentNode", + doneName = done++; + + return combinator.first ? + + // Check against closest ancestor/preceding element + function( elem, context, xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + return matcher( elem, context, xml ); + } + } + return false; + } : + + // Check against all ancestor/preceding elements + function( elem, context, xml ) { + var oldCache, uniqueCache, outerCache, + newCache = [ dirruns, doneName ]; + + // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching + if ( xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + if ( matcher( elem, context, xml ) ) { + return true; + } + } + } + } else { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + outerCache = elem[ expando ] || ( elem[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ elem.uniqueID ] || + ( outerCache[ elem.uniqueID ] = {} ); + + if ( skip && skip === elem.nodeName.toLowerCase() ) { + elem = elem[ dir ] || elem; + } else if ( ( oldCache = uniqueCache[ key ] ) && + oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { + + // Assign to newCache so results back-propagate to previous elements + return ( newCache[ 2 ] = oldCache[ 2 ] ); + } else { + + // Reuse newcache so results back-propagate to previous elements + uniqueCache[ key ] = newCache; + + // A match means we're done; a fail means we have to keep checking + if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { + return true; + } + } + } + } + } + return false; + }; +} + +function elementMatcher( matchers ) { + return matchers.length > 1 ? + function( elem, context, xml ) { + var i = matchers.length; + while ( i-- ) { + if ( !matchers[ i ]( elem, context, xml ) ) { + return false; + } + } + return true; + } : + matchers[ 0 ]; +} + +function multipleContexts( selector, contexts, results ) { + var i = 0, + len = contexts.length; + for ( ; i < len; i++ ) { + Sizzle( selector, contexts[ i ], results ); + } + return results; +} + +function condense( unmatched, map, filter, context, xml ) { + var elem, + newUnmatched = [], + i = 0, + len = unmatched.length, + mapped = map != null; + + for ( ; i < len; i++ ) { + if ( ( elem = unmatched[ i ] ) ) { + if ( !filter || filter( elem, context, xml ) ) { + newUnmatched.push( elem ); + if ( mapped ) { + map.push( i ); + } + } + } + } + + return newUnmatched; +} + +function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { + if ( postFilter && !postFilter[ expando ] ) { + postFilter = setMatcher( postFilter ); + } + if ( postFinder && !postFinder[ expando ] ) { + postFinder = setMatcher( postFinder, postSelector ); + } + return markFunction( function( seed, results, context, xml ) { + var temp, i, elem, + preMap = [], + postMap = [], + preexisting = results.length, + + // Get initial elements from seed or context + elems = seed || multipleContexts( + selector || "*", + context.nodeType ? [ context ] : context, + [] + ), + + // Prefilter to get matcher input, preserving a map for seed-results synchronization + matcherIn = preFilter && ( seed || !selector ) ? + condense( elems, preMap, preFilter, context, xml ) : + elems, + + matcherOut = matcher ? + + // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, + postFinder || ( seed ? preFilter : preexisting || postFilter ) ? + + // ...intermediate processing is necessary + [] : + + // ...otherwise use results directly + results : + matcherIn; + + // Find primary matches + if ( matcher ) { + matcher( matcherIn, matcherOut, context, xml ); + } + + // Apply postFilter + if ( postFilter ) { + temp = condense( matcherOut, postMap ); + postFilter( temp, [], context, xml ); + + // Un-match failing elements by moving them back to matcherIn + i = temp.length; + while ( i-- ) { + if ( ( elem = temp[ i ] ) ) { + matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); + } + } + } + + if ( seed ) { + if ( postFinder || preFilter ) { + if ( postFinder ) { + + // Get the final matcherOut by condensing this intermediate into postFinder contexts + temp = []; + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) ) { + + // Restore matcherIn since elem is not yet a final match + temp.push( ( matcherIn[ i ] = elem ) ); + } + } + postFinder( null, ( matcherOut = [] ), temp, xml ); + } + + // Move matched elements from seed to results to keep them synchronized + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) && + ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { + + seed[ temp ] = !( results[ temp ] = elem ); + } + } + } + + // Add elements to results, through postFinder if defined + } else { + matcherOut = condense( + matcherOut === results ? + matcherOut.splice( preexisting, matcherOut.length ) : + matcherOut + ); + if ( postFinder ) { + postFinder( null, results, matcherOut, xml ); + } else { + push.apply( results, matcherOut ); + } + } + } ); +} + +function matcherFromTokens( tokens ) { + var checkContext, matcher, j, + len = tokens.length, + leadingRelative = Expr.relative[ tokens[ 0 ].type ], + implicitRelative = leadingRelative || Expr.relative[ " " ], + i = leadingRelative ? 1 : 0, + + // The foundational matcher ensures that elements are reachable from top-level context(s) + matchContext = addCombinator( function( elem ) { + return elem === checkContext; + }, implicitRelative, true ), + matchAnyContext = addCombinator( function( elem ) { + return indexOf( checkContext, elem ) > -1; + }, implicitRelative, true ), + matchers = [ function( elem, context, xml ) { + var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( + ( checkContext = context ).nodeType ? + matchContext( elem, context, xml ) : + matchAnyContext( elem, context, xml ) ); + + // Avoid hanging onto element (issue #299) + checkContext = null; + return ret; + } ]; + + for ( ; i < len; i++ ) { + if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { + matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; + } else { + matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); + + // Return special upon seeing a positional matcher + if ( matcher[ expando ] ) { + + // Find the next relative operator (if any) for proper handling + j = ++i; + for ( ; j < len; j++ ) { + if ( Expr.relative[ tokens[ j ].type ] ) { + break; + } + } + return setMatcher( + i > 1 && elementMatcher( matchers ), + i > 1 && toSelector( + + // If the preceding token was a descendant combinator, insert an implicit any-element `*` + tokens + .slice( 0, i - 1 ) + .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) + ).replace( rtrim, "$1" ), + matcher, + i < j && matcherFromTokens( tokens.slice( i, j ) ), + j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), + j < len && toSelector( tokens ) + ); + } + matchers.push( matcher ); + } + } + + return elementMatcher( matchers ); +} + +function matcherFromGroupMatchers( elementMatchers, setMatchers ) { + var bySet = setMatchers.length > 0, + byElement = elementMatchers.length > 0, + superMatcher = function( seed, context, xml, results, outermost ) { + var elem, j, matcher, + matchedCount = 0, + i = "0", + unmatched = seed && [], + setMatched = [], + contextBackup = outermostContext, + + // We must always have either seed elements or outermost context + elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), + + // Use integer dirruns iff this is the outermost matcher + dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), + len = elems.length; + + if ( outermost ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + outermostContext = context == document || context || outermost; + } + + // Add elements passing elementMatchers directly to results + // Support: IE<9, Safari + // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id + for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { + if ( byElement && elem ) { + j = 0; + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( !context && elem.ownerDocument != document ) { + setDocument( elem ); + xml = !documentIsHTML; + } + while ( ( matcher = elementMatchers[ j++ ] ) ) { + if ( matcher( elem, context || document, xml ) ) { + results.push( elem ); + break; + } + } + if ( outermost ) { + dirruns = dirrunsUnique; + } + } + + // Track unmatched elements for set filters + if ( bySet ) { + + // They will have gone through all possible matchers + if ( ( elem = !matcher && elem ) ) { + matchedCount--; + } + + // Lengthen the array for every element, matched or not + if ( seed ) { + unmatched.push( elem ); + } + } + } + + // `i` is now the count of elements visited above, and adding it to `matchedCount` + // makes the latter nonnegative. + matchedCount += i; + + // Apply set filters to unmatched elements + // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` + // equals `i`), unless we didn't visit _any_ elements in the above loop because we have + // no element matchers and no seed. + // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that + // case, which will result in a "00" `matchedCount` that differs from `i` but is also + // numerically zero. + if ( bySet && i !== matchedCount ) { + j = 0; + while ( ( matcher = setMatchers[ j++ ] ) ) { + matcher( unmatched, setMatched, context, xml ); + } + + if ( seed ) { + + // Reintegrate element matches to eliminate the need for sorting + if ( matchedCount > 0 ) { + while ( i-- ) { + if ( !( unmatched[ i ] || setMatched[ i ] ) ) { + setMatched[ i ] = pop.call( results ); + } + } + } + + // Discard index placeholder values to get only actual matches + setMatched = condense( setMatched ); + } + + // Add matches to results + push.apply( results, setMatched ); + + // Seedless set matches succeeding multiple successful matchers stipulate sorting + if ( outermost && !seed && setMatched.length > 0 && + ( matchedCount + setMatchers.length ) > 1 ) { + + Sizzle.uniqueSort( results ); + } + } + + // Override manipulation of globals by nested matchers + if ( outermost ) { + dirruns = dirrunsUnique; + outermostContext = contextBackup; + } + + return unmatched; + }; + + return bySet ? + markFunction( superMatcher ) : + superMatcher; +} + +compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { + var i, + setMatchers = [], + elementMatchers = [], + cached = compilerCache[ selector + " " ]; + + if ( !cached ) { + + // Generate a function of recursive functions that can be used to check each element + if ( !match ) { + match = tokenize( selector ); + } + i = match.length; + while ( i-- ) { + cached = matcherFromTokens( match[ i ] ); + if ( cached[ expando ] ) { + setMatchers.push( cached ); + } else { + elementMatchers.push( cached ); + } + } + + // Cache the compiled function + cached = compilerCache( + selector, + matcherFromGroupMatchers( elementMatchers, setMatchers ) + ); + + // Save selector and tokenization + cached.selector = selector; + } + return cached; +}; + +/** + * A low-level selection function that works with Sizzle's compiled + * selector functions + * @param {String|Function} selector A selector or a pre-compiled + * selector function built with Sizzle.compile + * @param {Element} context + * @param {Array} [results] + * @param {Array} [seed] A set of elements to match against + */ +select = Sizzle.select = function( selector, context, results, seed ) { + var i, tokens, token, type, find, + compiled = typeof selector === "function" && selector, + match = !seed && tokenize( ( selector = compiled.selector || selector ) ); + + results = results || []; + + // Try to minimize operations if there is only one selector in the list and no seed + // (the latter of which guarantees us context) + if ( match.length === 1 ) { + + // Reduce context if the leading compound selector is an ID + tokens = match[ 0 ] = match[ 0 ].slice( 0 ); + if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && + context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { + + context = ( Expr.find[ "ID" ]( token.matches[ 0 ] + .replace( runescape, funescape ), context ) || [] )[ 0 ]; + if ( !context ) { + return results; + + // Precompiled matchers will still verify ancestry, so step up a level + } else if ( compiled ) { + context = context.parentNode; + } + + selector = selector.slice( tokens.shift().value.length ); + } + + // Fetch a seed set for right-to-left matching + i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; + while ( i-- ) { + token = tokens[ i ]; + + // Abort if we hit a combinator + if ( Expr.relative[ ( type = token.type ) ] ) { + break; + } + if ( ( find = Expr.find[ type ] ) ) { + + // Search, expanding context for leading sibling combinators + if ( ( seed = find( + token.matches[ 0 ].replace( runescape, funescape ), + rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || + context + ) ) ) { + + // If seed is empty or no tokens remain, we can return early + tokens.splice( i, 1 ); + selector = seed.length && toSelector( tokens ); + if ( !selector ) { + push.apply( results, seed ); + return results; + } + + break; + } + } + } + } + + // Compile and execute a filtering function if one is not provided + // Provide `match` to avoid retokenization if we modified the selector above + ( compiled || compile( selector, match ) )( + seed, + context, + !documentIsHTML, + results, + !context || rsibling.test( selector ) && testContext( context.parentNode ) || context + ); + return results; +}; + +// One-time assignments + +// Sort stability +support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; + +// Support: Chrome 14-35+ +// Always assume duplicates if they aren't passed to the comparison function +support.detectDuplicates = !!hasDuplicate; + +// Initialize against the default document +setDocument(); + +// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) +// Detached nodes confoundingly follow *each other* +support.sortDetached = assert( function( el ) { + + // Should return 1, but returns 4 (following) + return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; +} ); + +// Support: IE<8 +// Prevent attribute/property "interpolation" +// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx +if ( !assert( function( el ) { + el.innerHTML = ""; + return el.firstChild.getAttribute( "href" ) === "#"; +} ) ) { + addHandle( "type|href|height|width", function( elem, name, isXML ) { + if ( !isXML ) { + return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); + } + } ); +} + +// Support: IE<9 +// Use defaultValue in place of getAttribute("value") +if ( !support.attributes || !assert( function( el ) { + el.innerHTML = ""; + el.firstChild.setAttribute( "value", "" ); + return el.firstChild.getAttribute( "value" ) === ""; +} ) ) { + addHandle( "value", function( elem, _name, isXML ) { + if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { + return elem.defaultValue; + } + } ); +} + +// Support: IE<9 +// Use getAttributeNode to fetch booleans when getAttribute lies +if ( !assert( function( el ) { + return el.getAttribute( "disabled" ) == null; +} ) ) { + addHandle( booleans, function( elem, name, isXML ) { + var val; + if ( !isXML ) { + return elem[ name ] === true ? name.toLowerCase() : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; + } + } ); +} + +return Sizzle; + +} )( window ); + + + +jQuery.find = Sizzle; +jQuery.expr = Sizzle.selectors; + +// Deprecated +jQuery.expr[ ":" ] = jQuery.expr.pseudos; +jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; +jQuery.text = Sizzle.getText; +jQuery.isXMLDoc = Sizzle.isXML; +jQuery.contains = Sizzle.contains; +jQuery.escapeSelector = Sizzle.escape; + + + + +var dir = function( elem, dir, until ) { + var matched = [], + truncate = until !== undefined; + + while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { + if ( elem.nodeType === 1 ) { + if ( truncate && jQuery( elem ).is( until ) ) { + break; + } + matched.push( elem ); + } + } + return matched; +}; + + +var siblings = function( n, elem ) { + var matched = []; + + for ( ; n; n = n.nextSibling ) { + if ( n.nodeType === 1 && n !== elem ) { + matched.push( n ); + } + } + + return matched; +}; + + +var rneedsContext = jQuery.expr.match.needsContext; + + + +function nodeName( elem, name ) { + + return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); + +} +var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); + + + +// Implement the identical functionality for filter and not +function winnow( elements, qualifier, not ) { + if ( isFunction( qualifier ) ) { + return jQuery.grep( elements, function( elem, i ) { + return !!qualifier.call( elem, i, elem ) !== not; + } ); + } + + // Single element + if ( qualifier.nodeType ) { + return jQuery.grep( elements, function( elem ) { + return ( elem === qualifier ) !== not; + } ); + } + + // Arraylike of elements (jQuery, arguments, Array) + if ( typeof qualifier !== "string" ) { + return jQuery.grep( elements, function( elem ) { + return ( indexOf.call( qualifier, elem ) > -1 ) !== not; + } ); + } + + // Filtered directly for both simple and complex selectors + return jQuery.filter( qualifier, elements, not ); +} + +jQuery.filter = function( expr, elems, not ) { + var elem = elems[ 0 ]; + + if ( not ) { + expr = ":not(" + expr + ")"; + } + + if ( elems.length === 1 && elem.nodeType === 1 ) { + return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; + } + + return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { + return elem.nodeType === 1; + } ) ); +}; + +jQuery.fn.extend( { + find: function( selector ) { + var i, ret, + len = this.length, + self = this; + + if ( typeof selector !== "string" ) { + return this.pushStack( jQuery( selector ).filter( function() { + for ( i = 0; i < len; i++ ) { + if ( jQuery.contains( self[ i ], this ) ) { + return true; + } + } + } ) ); + } + + ret = this.pushStack( [] ); + + for ( i = 0; i < len; i++ ) { + jQuery.find( selector, self[ i ], ret ); + } + + return len > 1 ? jQuery.uniqueSort( ret ) : ret; + }, + filter: function( selector ) { + return this.pushStack( winnow( this, selector || [], false ) ); + }, + not: function( selector ) { + return this.pushStack( winnow( this, selector || [], true ) ); + }, + is: function( selector ) { + return !!winnow( + this, + + // If this is a positional/relative selector, check membership in the returned set + // so $("p:first").is("p:last") won't return true for a doc with two "p". + typeof selector === "string" && rneedsContext.test( selector ) ? + jQuery( selector ) : + selector || [], + false + ).length; + } +} ); + + +// Initialize a jQuery object + + +// A central reference to the root jQuery(document) +var rootjQuery, + + // A simple way to check for HTML strings + // Prioritize #id over to avoid XSS via location.hash (#9521) + // Strict HTML recognition (#11290: must start with <) + // Shortcut simple #id case for speed + rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, + + init = jQuery.fn.init = function( selector, context, root ) { + var match, elem; + + // HANDLE: $(""), $(null), $(undefined), $(false) + if ( !selector ) { + return this; + } + + // Method init() accepts an alternate rootjQuery + // so migrate can support jQuery.sub (gh-2101) + root = root || rootjQuery; + + // Handle HTML strings + if ( typeof selector === "string" ) { + if ( selector[ 0 ] === "<" && + selector[ selector.length - 1 ] === ">" && + selector.length >= 3 ) { + + // Assume that strings that start and end with <> are HTML and skip the regex check + match = [ null, selector, null ]; + + } else { + match = rquickExpr.exec( selector ); + } + + // Match html or make sure no context is specified for #id + if ( match && ( match[ 1 ] || !context ) ) { + + // HANDLE: $(html) -> $(array) + if ( match[ 1 ] ) { + context = context instanceof jQuery ? context[ 0 ] : context; + + // Option to run scripts is true for back-compat + // Intentionally let the error be thrown if parseHTML is not present + jQuery.merge( this, jQuery.parseHTML( + match[ 1 ], + context && context.nodeType ? context.ownerDocument || context : document, + true + ) ); + + // HANDLE: $(html, props) + if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { + for ( match in context ) { + + // Properties of context are called as methods if possible + if ( isFunction( this[ match ] ) ) { + this[ match ]( context[ match ] ); + + // ...and otherwise set as attributes + } else { + this.attr( match, context[ match ] ); + } + } + } + + return this; + + // HANDLE: $(#id) + } else { + elem = document.getElementById( match[ 2 ] ); + + if ( elem ) { + + // Inject the element directly into the jQuery object + this[ 0 ] = elem; + this.length = 1; + } + return this; + } + + // HANDLE: $(expr, $(...)) + } else if ( !context || context.jquery ) { + return ( context || root ).find( selector ); + + // HANDLE: $(expr, context) + // (which is just equivalent to: $(context).find(expr) + } else { + return this.constructor( context ).find( selector ); + } + + // HANDLE: $(DOMElement) + } else if ( selector.nodeType ) { + this[ 0 ] = selector; + this.length = 1; + return this; + + // HANDLE: $(function) + // Shortcut for document ready + } else if ( isFunction( selector ) ) { + return root.ready !== undefined ? + root.ready( selector ) : + + // Execute immediately if ready is not present + selector( jQuery ); + } + + return jQuery.makeArray( selector, this ); + }; + +// Give the init function the jQuery prototype for later instantiation +init.prototype = jQuery.fn; + +// Initialize central reference +rootjQuery = jQuery( document ); + + +var rparentsprev = /^(?:parents|prev(?:Until|All))/, + + // Methods guaranteed to produce a unique set when starting from a unique set + guaranteedUnique = { + children: true, + contents: true, + next: true, + prev: true + }; + +jQuery.fn.extend( { + has: function( target ) { + var targets = jQuery( target, this ), + l = targets.length; + + return this.filter( function() { + var i = 0; + for ( ; i < l; i++ ) { + if ( jQuery.contains( this, targets[ i ] ) ) { + return true; + } + } + } ); + }, + + closest: function( selectors, context ) { + var cur, + i = 0, + l = this.length, + matched = [], + targets = typeof selectors !== "string" && jQuery( selectors ); + + // Positional selectors never match, since there's no _selection_ context + if ( !rneedsContext.test( selectors ) ) { + for ( ; i < l; i++ ) { + for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { + + // Always skip document fragments + if ( cur.nodeType < 11 && ( targets ? + targets.index( cur ) > -1 : + + // Don't pass non-elements to Sizzle + cur.nodeType === 1 && + jQuery.find.matchesSelector( cur, selectors ) ) ) { + + matched.push( cur ); + break; + } + } + } + } + + return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); + }, + + // Determine the position of an element within the set + index: function( elem ) { + + // No argument, return index in parent + if ( !elem ) { + return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; + } + + // Index in selector + if ( typeof elem === "string" ) { + return indexOf.call( jQuery( elem ), this[ 0 ] ); + } + + // Locate the position of the desired element + return indexOf.call( this, + + // If it receives a jQuery object, the first element is used + elem.jquery ? elem[ 0 ] : elem + ); + }, + + add: function( selector, context ) { + return this.pushStack( + jQuery.uniqueSort( + jQuery.merge( this.get(), jQuery( selector, context ) ) + ) + ); + }, + + addBack: function( selector ) { + return this.add( selector == null ? + this.prevObject : this.prevObject.filter( selector ) + ); + } +} ); + +function sibling( cur, dir ) { + while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} + return cur; +} + +jQuery.each( { + parent: function( elem ) { + var parent = elem.parentNode; + return parent && parent.nodeType !== 11 ? parent : null; + }, + parents: function( elem ) { + return dir( elem, "parentNode" ); + }, + parentsUntil: function( elem, _i, until ) { + return dir( elem, "parentNode", until ); + }, + next: function( elem ) { + return sibling( elem, "nextSibling" ); + }, + prev: function( elem ) { + return sibling( elem, "previousSibling" ); + }, + nextAll: function( elem ) { + return dir( elem, "nextSibling" ); + }, + prevAll: function( elem ) { + return dir( elem, "previousSibling" ); + }, + nextUntil: function( elem, _i, until ) { + return dir( elem, "nextSibling", until ); + }, + prevUntil: function( elem, _i, until ) { + return dir( elem, "previousSibling", until ); + }, + siblings: function( elem ) { + return siblings( ( elem.parentNode || {} ).firstChild, elem ); + }, + children: function( elem ) { + return siblings( elem.firstChild ); + }, + contents: function( elem ) { + if ( elem.contentDocument != null && + + // Support: IE 11+ + // elements with no `data` attribute has an object + // `contentDocument` with a `null` prototype. + getProto( elem.contentDocument ) ) { + + return elem.contentDocument; + } + + // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only + // Treat the template element as a regular one in browsers that + // don't support it. + if ( nodeName( elem, "template" ) ) { + elem = elem.content || elem; + } + + return jQuery.merge( [], elem.childNodes ); + } +}, function( name, fn ) { + jQuery.fn[ name ] = function( until, selector ) { + var matched = jQuery.map( this, fn, until ); + + if ( name.slice( -5 ) !== "Until" ) { + selector = until; + } + + if ( selector && typeof selector === "string" ) { + matched = jQuery.filter( selector, matched ); + } + + if ( this.length > 1 ) { + + // Remove duplicates + if ( !guaranteedUnique[ name ] ) { + jQuery.uniqueSort( matched ); + } + + // Reverse order for parents* and prev-derivatives + if ( rparentsprev.test( name ) ) { + matched.reverse(); + } + } + + return this.pushStack( matched ); + }; +} ); +var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); + + + +// Convert String-formatted options into Object-formatted ones +function createOptions( options ) { + var object = {}; + jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { + object[ flag ] = true; + } ); + return object; +} + +/* + * Create a callback list using the following parameters: + * + * options: an optional list of space-separated options that will change how + * the callback list behaves or a more traditional option object + * + * By default a callback list will act like an event callback list and can be + * "fired" multiple times. + * + * Possible options: + * + * once: will ensure the callback list can only be fired once (like a Deferred) + * + * memory: will keep track of previous values and will call any callback added + * after the list has been fired right away with the latest "memorized" + * values (like a Deferred) + * + * unique: will ensure a callback can only be added once (no duplicate in the list) + * + * stopOnFalse: interrupt callings when a callback returns false + * + */ +jQuery.Callbacks = function( options ) { + + // Convert options from String-formatted to Object-formatted if needed + // (we check in cache first) + options = typeof options === "string" ? + createOptions( options ) : + jQuery.extend( {}, options ); + + var // Flag to know if list is currently firing + firing, + + // Last fire value for non-forgettable lists + memory, + + // Flag to know if list was already fired + fired, + + // Flag to prevent firing + locked, + + // Actual callback list + list = [], + + // Queue of execution data for repeatable lists + queue = [], + + // Index of currently firing callback (modified by add/remove as needed) + firingIndex = -1, + + // Fire callbacks + fire = function() { + + // Enforce single-firing + locked = locked || options.once; + + // Execute callbacks for all pending executions, + // respecting firingIndex overrides and runtime changes + fired = firing = true; + for ( ; queue.length; firingIndex = -1 ) { + memory = queue.shift(); + while ( ++firingIndex < list.length ) { + + // Run callback and check for early termination + if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && + options.stopOnFalse ) { + + // Jump to end and forget the data so .add doesn't re-fire + firingIndex = list.length; + memory = false; + } + } + } + + // Forget the data if we're done with it + if ( !options.memory ) { + memory = false; + } + + firing = false; + + // Clean up if we're done firing for good + if ( locked ) { + + // Keep an empty list if we have data for future add calls + if ( memory ) { + list = []; + + // Otherwise, this object is spent + } else { + list = ""; + } + } + }, + + // Actual Callbacks object + self = { + + // Add a callback or a collection of callbacks to the list + add: function() { + if ( list ) { + + // If we have memory from a past run, we should fire after adding + if ( memory && !firing ) { + firingIndex = list.length - 1; + queue.push( memory ); + } + + ( function add( args ) { + jQuery.each( args, function( _, arg ) { + if ( isFunction( arg ) ) { + if ( !options.unique || !self.has( arg ) ) { + list.push( arg ); + } + } else if ( arg && arg.length && toType( arg ) !== "string" ) { + + // Inspect recursively + add( arg ); + } + } ); + } )( arguments ); + + if ( memory && !firing ) { + fire(); + } + } + return this; + }, + + // Remove a callback from the list + remove: function() { + jQuery.each( arguments, function( _, arg ) { + var index; + while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { + list.splice( index, 1 ); + + // Handle firing indexes + if ( index <= firingIndex ) { + firingIndex--; + } + } + } ); + return this; + }, + + // Check if a given callback is in the list. + // If no argument is given, return whether or not list has callbacks attached. + has: function( fn ) { + return fn ? + jQuery.inArray( fn, list ) > -1 : + list.length > 0; + }, + + // Remove all callbacks from the list + empty: function() { + if ( list ) { + list = []; + } + return this; + }, + + // Disable .fire and .add + // Abort any current/pending executions + // Clear all callbacks and values + disable: function() { + locked = queue = []; + list = memory = ""; + return this; + }, + disabled: function() { + return !list; + }, + + // Disable .fire + // Also disable .add unless we have memory (since it would have no effect) + // Abort any pending executions + lock: function() { + locked = queue = []; + if ( !memory && !firing ) { + list = memory = ""; + } + return this; + }, + locked: function() { + return !!locked; + }, + + // Call all callbacks with the given context and arguments + fireWith: function( context, args ) { + if ( !locked ) { + args = args || []; + args = [ context, args.slice ? args.slice() : args ]; + queue.push( args ); + if ( !firing ) { + fire(); + } + } + return this; + }, + + // Call all the callbacks with the given arguments + fire: function() { + self.fireWith( this, arguments ); + return this; + }, + + // To know if the callbacks have already been called at least once + fired: function() { + return !!fired; + } + }; + + return self; +}; + + +function Identity( v ) { + return v; +} +function Thrower( ex ) { + throw ex; +} + +function adoptValue( value, resolve, reject, noValue ) { + var method; + + try { + + // Check for promise aspect first to privilege synchronous behavior + if ( value && isFunction( ( method = value.promise ) ) ) { + method.call( value ).done( resolve ).fail( reject ); + + // Other thenables + } else if ( value && isFunction( ( method = value.then ) ) ) { + method.call( value, resolve, reject ); + + // Other non-thenables + } else { + + // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: + // * false: [ value ].slice( 0 ) => resolve( value ) + // * true: [ value ].slice( 1 ) => resolve() + resolve.apply( undefined, [ value ].slice( noValue ) ); + } + + // For Promises/A+, convert exceptions into rejections + // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in + // Deferred#then to conditionally suppress rejection. + } catch ( value ) { + + // Support: Android 4.0 only + // Strict mode functions invoked without .call/.apply get global-object context + reject.apply( undefined, [ value ] ); + } +} + +jQuery.extend( { + + Deferred: function( func ) { + var tuples = [ + + // action, add listener, callbacks, + // ... .then handlers, argument index, [final state] + [ "notify", "progress", jQuery.Callbacks( "memory" ), + jQuery.Callbacks( "memory" ), 2 ], + [ "resolve", "done", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 0, "resolved" ], + [ "reject", "fail", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 1, "rejected" ] + ], + state = "pending", + promise = { + state: function() { + return state; + }, + always: function() { + deferred.done( arguments ).fail( arguments ); + return this; + }, + "catch": function( fn ) { + return promise.then( null, fn ); + }, + + // Keep pipe for back-compat + pipe: function( /* fnDone, fnFail, fnProgress */ ) { + var fns = arguments; + + return jQuery.Deferred( function( newDefer ) { + jQuery.each( tuples, function( _i, tuple ) { + + // Map tuples (progress, done, fail) to arguments (done, fail, progress) + var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; + + // deferred.progress(function() { bind to newDefer or newDefer.notify }) + // deferred.done(function() { bind to newDefer or newDefer.resolve }) + // deferred.fail(function() { bind to newDefer or newDefer.reject }) + deferred[ tuple[ 1 ] ]( function() { + var returned = fn && fn.apply( this, arguments ); + if ( returned && isFunction( returned.promise ) ) { + returned.promise() + .progress( newDefer.notify ) + .done( newDefer.resolve ) + .fail( newDefer.reject ); + } else { + newDefer[ tuple[ 0 ] + "With" ]( + this, + fn ? [ returned ] : arguments + ); + } + } ); + } ); + fns = null; + } ).promise(); + }, + then: function( onFulfilled, onRejected, onProgress ) { + var maxDepth = 0; + function resolve( depth, deferred, handler, special ) { + return function() { + var that = this, + args = arguments, + mightThrow = function() { + var returned, then; + + // Support: Promises/A+ section 2.3.3.3.3 + // https://promisesaplus.com/#point-59 + // Ignore double-resolution attempts + if ( depth < maxDepth ) { + return; + } + + returned = handler.apply( that, args ); + + // Support: Promises/A+ section 2.3.1 + // https://promisesaplus.com/#point-48 + if ( returned === deferred.promise() ) { + throw new TypeError( "Thenable self-resolution" ); + } + + // Support: Promises/A+ sections 2.3.3.1, 3.5 + // https://promisesaplus.com/#point-54 + // https://promisesaplus.com/#point-75 + // Retrieve `then` only once + then = returned && + + // Support: Promises/A+ section 2.3.4 + // https://promisesaplus.com/#point-64 + // Only check objects and functions for thenability + ( typeof returned === "object" || + typeof returned === "function" ) && + returned.then; + + // Handle a returned thenable + if ( isFunction( then ) ) { + + // Special processors (notify) just wait for resolution + if ( special ) { + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ) + ); + + // Normal processors (resolve) also hook into progress + } else { + + // ...and disregard older resolution values + maxDepth++; + + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ), + resolve( maxDepth, deferred, Identity, + deferred.notifyWith ) + ); + } + + // Handle all other returned values + } else { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Identity ) { + that = undefined; + args = [ returned ]; + } + + // Process the value(s) + // Default process is resolve + ( special || deferred.resolveWith )( that, args ); + } + }, + + // Only normal processors (resolve) catch and reject exceptions + process = special ? + mightThrow : + function() { + try { + mightThrow(); + } catch ( e ) { + + if ( jQuery.Deferred.exceptionHook ) { + jQuery.Deferred.exceptionHook( e, + process.stackTrace ); + } + + // Support: Promises/A+ section 2.3.3.3.4.1 + // https://promisesaplus.com/#point-61 + // Ignore post-resolution exceptions + if ( depth + 1 >= maxDepth ) { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Thrower ) { + that = undefined; + args = [ e ]; + } + + deferred.rejectWith( that, args ); + } + } + }; + + // Support: Promises/A+ section 2.3.3.3.1 + // https://promisesaplus.com/#point-57 + // Re-resolve promises immediately to dodge false rejection from + // subsequent errors + if ( depth ) { + process(); + } else { + + // Call an optional hook to record the stack, in case of exception + // since it's otherwise lost when execution goes async + if ( jQuery.Deferred.getStackHook ) { + process.stackTrace = jQuery.Deferred.getStackHook(); + } + window.setTimeout( process ); + } + }; + } + + return jQuery.Deferred( function( newDefer ) { + + // progress_handlers.add( ... ) + tuples[ 0 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onProgress ) ? + onProgress : + Identity, + newDefer.notifyWith + ) + ); + + // fulfilled_handlers.add( ... ) + tuples[ 1 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onFulfilled ) ? + onFulfilled : + Identity + ) + ); + + // rejected_handlers.add( ... ) + tuples[ 2 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onRejected ) ? + onRejected : + Thrower + ) + ); + } ).promise(); + }, + + // Get a promise for this deferred + // If obj is provided, the promise aspect is added to the object + promise: function( obj ) { + return obj != null ? jQuery.extend( obj, promise ) : promise; + } + }, + deferred = {}; + + // Add list-specific methods + jQuery.each( tuples, function( i, tuple ) { + var list = tuple[ 2 ], + stateString = tuple[ 5 ]; + + // promise.progress = list.add + // promise.done = list.add + // promise.fail = list.add + promise[ tuple[ 1 ] ] = list.add; + + // Handle state + if ( stateString ) { + list.add( + function() { + + // state = "resolved" (i.e., fulfilled) + // state = "rejected" + state = stateString; + }, + + // rejected_callbacks.disable + // fulfilled_callbacks.disable + tuples[ 3 - i ][ 2 ].disable, + + // rejected_handlers.disable + // fulfilled_handlers.disable + tuples[ 3 - i ][ 3 ].disable, + + // progress_callbacks.lock + tuples[ 0 ][ 2 ].lock, + + // progress_handlers.lock + tuples[ 0 ][ 3 ].lock + ); + } + + // progress_handlers.fire + // fulfilled_handlers.fire + // rejected_handlers.fire + list.add( tuple[ 3 ].fire ); + + // deferred.notify = function() { deferred.notifyWith(...) } + // deferred.resolve = function() { deferred.resolveWith(...) } + // deferred.reject = function() { deferred.rejectWith(...) } + deferred[ tuple[ 0 ] ] = function() { + deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); + return this; + }; + + // deferred.notifyWith = list.fireWith + // deferred.resolveWith = list.fireWith + // deferred.rejectWith = list.fireWith + deferred[ tuple[ 0 ] + "With" ] = list.fireWith; + } ); + + // Make the deferred a promise + promise.promise( deferred ); + + // Call given func if any + if ( func ) { + func.call( deferred, deferred ); + } + + // All done! + return deferred; + }, + + // Deferred helper + when: function( singleValue ) { + var + + // count of uncompleted subordinates + remaining = arguments.length, + + // count of unprocessed arguments + i = remaining, + + // subordinate fulfillment data + resolveContexts = Array( i ), + resolveValues = slice.call( arguments ), + + // the primary Deferred + primary = jQuery.Deferred(), + + // subordinate callback factory + updateFunc = function( i ) { + return function( value ) { + resolveContexts[ i ] = this; + resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; + if ( !( --remaining ) ) { + primary.resolveWith( resolveContexts, resolveValues ); + } + }; + }; + + // Single- and empty arguments are adopted like Promise.resolve + if ( remaining <= 1 ) { + adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject, + !remaining ); + + // Use .then() to unwrap secondary thenables (cf. gh-3000) + if ( primary.state() === "pending" || + isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { + + return primary.then(); + } + } + + // Multiple arguments are aggregated like Promise.all array elements + while ( i-- ) { + adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject ); + } + + return primary.promise(); + } +} ); + + +// These usually indicate a programmer mistake during development, +// warn about them ASAP rather than swallowing them by default. +var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; + +jQuery.Deferred.exceptionHook = function( error, stack ) { + + // Support: IE 8 - 9 only + // Console exists when dev tools are open, which can happen at any time + if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { + window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); + } +}; + + + + +jQuery.readyException = function( error ) { + window.setTimeout( function() { + throw error; + } ); +}; + + + + +// The deferred used on DOM ready +var readyList = jQuery.Deferred(); + +jQuery.fn.ready = function( fn ) { + + readyList + .then( fn ) + + // Wrap jQuery.readyException in a function so that the lookup + // happens at the time of error handling instead of callback + // registration. + .catch( function( error ) { + jQuery.readyException( error ); + } ); + + return this; +}; + +jQuery.extend( { + + // Is the DOM ready to be used? Set to true once it occurs. + isReady: false, + + // A counter to track how many items to wait for before + // the ready event fires. See #6781 + readyWait: 1, + + // Handle when the DOM is ready + ready: function( wait ) { + + // Abort if there are pending holds or we're already ready + if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { + return; + } + + // Remember that the DOM is ready + jQuery.isReady = true; + + // If a normal DOM Ready event fired, decrement, and wait if need be + if ( wait !== true && --jQuery.readyWait > 0 ) { + return; + } + + // If there are functions bound, to execute + readyList.resolveWith( document, [ jQuery ] ); + } +} ); + +jQuery.ready.then = readyList.then; + +// The ready event handler and self cleanup method +function completed() { + document.removeEventListener( "DOMContentLoaded", completed ); + window.removeEventListener( "load", completed ); + jQuery.ready(); +} + +// Catch cases where $(document).ready() is called +// after the browser event has already occurred. +// Support: IE <=9 - 10 only +// Older IE sometimes signals "interactive" too soon +if ( document.readyState === "complete" || + ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { + + // Handle it asynchronously to allow scripts the opportunity to delay ready + window.setTimeout( jQuery.ready ); + +} else { + + // Use the handy event callback + document.addEventListener( "DOMContentLoaded", completed ); + + // A fallback to window.onload, that will always work + window.addEventListener( "load", completed ); +} + + + + +// Multifunctional method to get and set values of a collection +// The value/s can optionally be executed if it's a function +var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { + var i = 0, + len = elems.length, + bulk = key == null; + + // Sets many values + if ( toType( key ) === "object" ) { + chainable = true; + for ( i in key ) { + access( elems, fn, i, key[ i ], true, emptyGet, raw ); + } + + // Sets one value + } else if ( value !== undefined ) { + chainable = true; + + if ( !isFunction( value ) ) { + raw = true; + } + + if ( bulk ) { + + // Bulk operations run against the entire set + if ( raw ) { + fn.call( elems, value ); + fn = null; + + // ...except when executing function values + } else { + bulk = fn; + fn = function( elem, _key, value ) { + return bulk.call( jQuery( elem ), value ); + }; + } + } + + if ( fn ) { + for ( ; i < len; i++ ) { + fn( + elems[ i ], key, raw ? + value : + value.call( elems[ i ], i, fn( elems[ i ], key ) ) + ); + } + } + } + + if ( chainable ) { + return elems; + } + + // Gets + if ( bulk ) { + return fn.call( elems ); + } + + return len ? fn( elems[ 0 ], key ) : emptyGet; +}; + + +// Matches dashed string for camelizing +var rmsPrefix = /^-ms-/, + rdashAlpha = /-([a-z])/g; + +// Used by camelCase as callback to replace() +function fcamelCase( _all, letter ) { + return letter.toUpperCase(); +} + +// Convert dashed to camelCase; used by the css and data modules +// Support: IE <=9 - 11, Edge 12 - 15 +// Microsoft forgot to hump their vendor prefix (#9572) +function camelCase( string ) { + return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); +} +var acceptData = function( owner ) { + + // Accepts only: + // - Node + // - Node.ELEMENT_NODE + // - Node.DOCUMENT_NODE + // - Object + // - Any + return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); +}; + + + + +function Data() { + this.expando = jQuery.expando + Data.uid++; +} + +Data.uid = 1; + +Data.prototype = { + + cache: function( owner ) { + + // Check if the owner object already has a cache + var value = owner[ this.expando ]; + + // If not, create one + if ( !value ) { + value = {}; + + // We can accept data for non-element nodes in modern browsers, + // but we should not, see #8335. + // Always return an empty object. + if ( acceptData( owner ) ) { + + // If it is a node unlikely to be stringify-ed or looped over + // use plain assignment + if ( owner.nodeType ) { + owner[ this.expando ] = value; + + // Otherwise secure it in a non-enumerable property + // configurable must be true to allow the property to be + // deleted when data is removed + } else { + Object.defineProperty( owner, this.expando, { + value: value, + configurable: true + } ); + } + } + } + + return value; + }, + set: function( owner, data, value ) { + var prop, + cache = this.cache( owner ); + + // Handle: [ owner, key, value ] args + // Always use camelCase key (gh-2257) + if ( typeof data === "string" ) { + cache[ camelCase( data ) ] = value; + + // Handle: [ owner, { properties } ] args + } else { + + // Copy the properties one-by-one to the cache object + for ( prop in data ) { + cache[ camelCase( prop ) ] = data[ prop ]; + } + } + return cache; + }, + get: function( owner, key ) { + return key === undefined ? + this.cache( owner ) : + + // Always use camelCase key (gh-2257) + owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; + }, + access: function( owner, key, value ) { + + // In cases where either: + // + // 1. No key was specified + // 2. A string key was specified, but no value provided + // + // Take the "read" path and allow the get method to determine + // which value to return, respectively either: + // + // 1. The entire cache object + // 2. The data stored at the key + // + if ( key === undefined || + ( ( key && typeof key === "string" ) && value === undefined ) ) { + + return this.get( owner, key ); + } + + // When the key is not a string, or both a key and value + // are specified, set or extend (existing objects) with either: + // + // 1. An object of properties + // 2. A key and value + // + this.set( owner, key, value ); + + // Since the "set" path can have two possible entry points + // return the expected data based on which path was taken[*] + return value !== undefined ? value : key; + }, + remove: function( owner, key ) { + var i, + cache = owner[ this.expando ]; + + if ( cache === undefined ) { + return; + } + + if ( key !== undefined ) { + + // Support array or space separated string of keys + if ( Array.isArray( key ) ) { + + // If key is an array of keys... + // We always set camelCase keys, so remove that. + key = key.map( camelCase ); + } else { + key = camelCase( key ); + + // If a key with the spaces exists, use it. + // Otherwise, create an array by matching non-whitespace + key = key in cache ? + [ key ] : + ( key.match( rnothtmlwhite ) || [] ); + } + + i = key.length; + + while ( i-- ) { + delete cache[ key[ i ] ]; + } + } + + // Remove the expando if there's no more data + if ( key === undefined || jQuery.isEmptyObject( cache ) ) { + + // Support: Chrome <=35 - 45 + // Webkit & Blink performance suffers when deleting properties + // from DOM nodes, so set to undefined instead + // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) + if ( owner.nodeType ) { + owner[ this.expando ] = undefined; + } else { + delete owner[ this.expando ]; + } + } + }, + hasData: function( owner ) { + var cache = owner[ this.expando ]; + return cache !== undefined && !jQuery.isEmptyObject( cache ); + } +}; +var dataPriv = new Data(); + +var dataUser = new Data(); + + + +// Implementation Summary +// +// 1. Enforce API surface and semantic compatibility with 1.9.x branch +// 2. Improve the module's maintainability by reducing the storage +// paths to a single mechanism. +// 3. Use the same single mechanism to support "private" and "user" data. +// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) +// 5. Avoid exposing implementation details on user objects (eg. expando properties) +// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 + +var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, + rmultiDash = /[A-Z]/g; + +function getData( data ) { + if ( data === "true" ) { + return true; + } + + if ( data === "false" ) { + return false; + } + + if ( data === "null" ) { + return null; + } + + // Only convert to a number if it doesn't change the string + if ( data === +data + "" ) { + return +data; + } + + if ( rbrace.test( data ) ) { + return JSON.parse( data ); + } + + return data; +} + +function dataAttr( elem, key, data ) { + var name; + + // If nothing was found internally, try to fetch any + // data from the HTML5 data-* attribute + if ( data === undefined && elem.nodeType === 1 ) { + name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); + data = elem.getAttribute( name ); + + if ( typeof data === "string" ) { + try { + data = getData( data ); + } catch ( e ) {} + + // Make sure we set the data so it isn't changed later + dataUser.set( elem, key, data ); + } else { + data = undefined; + } + } + return data; +} + +jQuery.extend( { + hasData: function( elem ) { + return dataUser.hasData( elem ) || dataPriv.hasData( elem ); + }, + + data: function( elem, name, data ) { + return dataUser.access( elem, name, data ); + }, + + removeData: function( elem, name ) { + dataUser.remove( elem, name ); + }, + + // TODO: Now that all calls to _data and _removeData have been replaced + // with direct calls to dataPriv methods, these can be deprecated. + _data: function( elem, name, data ) { + return dataPriv.access( elem, name, data ); + }, + + _removeData: function( elem, name ) { + dataPriv.remove( elem, name ); + } +} ); + +jQuery.fn.extend( { + data: function( key, value ) { + var i, name, data, + elem = this[ 0 ], + attrs = elem && elem.attributes; + + // Gets all values + if ( key === undefined ) { + if ( this.length ) { + data = dataUser.get( elem ); + + if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { + i = attrs.length; + while ( i-- ) { + + // Support: IE 11 only + // The attrs elements can be null (#14894) + if ( attrs[ i ] ) { + name = attrs[ i ].name; + if ( name.indexOf( "data-" ) === 0 ) { + name = camelCase( name.slice( 5 ) ); + dataAttr( elem, name, data[ name ] ); + } + } + } + dataPriv.set( elem, "hasDataAttrs", true ); + } + } + + return data; + } + + // Sets multiple values + if ( typeof key === "object" ) { + return this.each( function() { + dataUser.set( this, key ); + } ); + } + + return access( this, function( value ) { + var data; + + // The calling jQuery object (element matches) is not empty + // (and therefore has an element appears at this[ 0 ]) and the + // `value` parameter was not undefined. An empty jQuery object + // will result in `undefined` for elem = this[ 0 ] which will + // throw an exception if an attempt to read a data cache is made. + if ( elem && value === undefined ) { + + // Attempt to get data from the cache + // The key will always be camelCased in Data + data = dataUser.get( elem, key ); + if ( data !== undefined ) { + return data; + } + + // Attempt to "discover" the data in + // HTML5 custom data-* attrs + data = dataAttr( elem, key ); + if ( data !== undefined ) { + return data; + } + + // We tried really hard, but the data doesn't exist. + return; + } + + // Set the data... + this.each( function() { + + // We always store the camelCased key + dataUser.set( this, key, value ); + } ); + }, null, value, arguments.length > 1, null, true ); + }, + + removeData: function( key ) { + return this.each( function() { + dataUser.remove( this, key ); + } ); + } +} ); + + +jQuery.extend( { + queue: function( elem, type, data ) { + var queue; + + if ( elem ) { + type = ( type || "fx" ) + "queue"; + queue = dataPriv.get( elem, type ); + + // Speed up dequeue by getting out quickly if this is just a lookup + if ( data ) { + if ( !queue || Array.isArray( data ) ) { + queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); + } else { + queue.push( data ); + } + } + return queue || []; + } + }, + + dequeue: function( elem, type ) { + type = type || "fx"; + + var queue = jQuery.queue( elem, type ), + startLength = queue.length, + fn = queue.shift(), + hooks = jQuery._queueHooks( elem, type ), + next = function() { + jQuery.dequeue( elem, type ); + }; + + // If the fx queue is dequeued, always remove the progress sentinel + if ( fn === "inprogress" ) { + fn = queue.shift(); + startLength--; + } + + if ( fn ) { + + // Add a progress sentinel to prevent the fx queue from being + // automatically dequeued + if ( type === "fx" ) { + queue.unshift( "inprogress" ); + } + + // Clear up the last queue stop function + delete hooks.stop; + fn.call( elem, next, hooks ); + } + + if ( !startLength && hooks ) { + hooks.empty.fire(); + } + }, + + // Not public - generate a queueHooks object, or return the current one + _queueHooks: function( elem, type ) { + var key = type + "queueHooks"; + return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { + empty: jQuery.Callbacks( "once memory" ).add( function() { + dataPriv.remove( elem, [ type + "queue", key ] ); + } ) + } ); + } +} ); + +jQuery.fn.extend( { + queue: function( type, data ) { + var setter = 2; + + if ( typeof type !== "string" ) { + data = type; + type = "fx"; + setter--; + } + + if ( arguments.length < setter ) { + return jQuery.queue( this[ 0 ], type ); + } + + return data === undefined ? + this : + this.each( function() { + var queue = jQuery.queue( this, type, data ); + + // Ensure a hooks for this queue + jQuery._queueHooks( this, type ); + + if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { + jQuery.dequeue( this, type ); + } + } ); + }, + dequeue: function( type ) { + return this.each( function() { + jQuery.dequeue( this, type ); + } ); + }, + clearQueue: function( type ) { + return this.queue( type || "fx", [] ); + }, + + // Get a promise resolved when queues of a certain type + // are emptied (fx is the type by default) + promise: function( type, obj ) { + var tmp, + count = 1, + defer = jQuery.Deferred(), + elements = this, + i = this.length, + resolve = function() { + if ( !( --count ) ) { + defer.resolveWith( elements, [ elements ] ); + } + }; + + if ( typeof type !== "string" ) { + obj = type; + type = undefined; + } + type = type || "fx"; + + while ( i-- ) { + tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); + if ( tmp && tmp.empty ) { + count++; + tmp.empty.add( resolve ); + } + } + resolve(); + return defer.promise( obj ); + } +} ); +var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; + +var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); + + +var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; + +var documentElement = document.documentElement; + + + + var isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ); + }, + composed = { composed: true }; + + // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only + // Check attachment across shadow DOM boundaries when possible (gh-3504) + // Support: iOS 10.0-10.2 only + // Early iOS 10 versions support `attachShadow` but not `getRootNode`, + // leading to errors. We need to check for `getRootNode`. + if ( documentElement.getRootNode ) { + isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ) || + elem.getRootNode( composed ) === elem.ownerDocument; + }; + } +var isHiddenWithinTree = function( elem, el ) { + + // isHiddenWithinTree might be called from jQuery#filter function; + // in that case, element will be second argument + elem = el || elem; + + // Inline style trumps all + return elem.style.display === "none" || + elem.style.display === "" && + + // Otherwise, check computed style + // Support: Firefox <=43 - 45 + // Disconnected elements can have computed display: none, so first confirm that elem is + // in the document. + isAttached( elem ) && + + jQuery.css( elem, "display" ) === "none"; + }; + + + +function adjustCSS( elem, prop, valueParts, tween ) { + var adjusted, scale, + maxIterations = 20, + currentValue = tween ? + function() { + return tween.cur(); + } : + function() { + return jQuery.css( elem, prop, "" ); + }, + initial = currentValue(), + unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), + + // Starting value computation is required for potential unit mismatches + initialInUnit = elem.nodeType && + ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && + rcssNum.exec( jQuery.css( elem, prop ) ); + + if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { + + // Support: Firefox <=54 + // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) + initial = initial / 2; + + // Trust units reported by jQuery.css + unit = unit || initialInUnit[ 3 ]; + + // Iteratively approximate from a nonzero starting point + initialInUnit = +initial || 1; + + while ( maxIterations-- ) { + + // Evaluate and update our best guess (doubling guesses that zero out). + // Finish if the scale equals or crosses 1 (making the old*new product non-positive). + jQuery.style( elem, prop, initialInUnit + unit ); + if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { + maxIterations = 0; + } + initialInUnit = initialInUnit / scale; + + } + + initialInUnit = initialInUnit * 2; + jQuery.style( elem, prop, initialInUnit + unit ); + + // Make sure we update the tween properties later on + valueParts = valueParts || []; + } + + if ( valueParts ) { + initialInUnit = +initialInUnit || +initial || 0; + + // Apply relative offset (+=/-=) if specified + adjusted = valueParts[ 1 ] ? + initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : + +valueParts[ 2 ]; + if ( tween ) { + tween.unit = unit; + tween.start = initialInUnit; + tween.end = adjusted; + } + } + return adjusted; +} + + +var defaultDisplayMap = {}; + +function getDefaultDisplay( elem ) { + var temp, + doc = elem.ownerDocument, + nodeName = elem.nodeName, + display = defaultDisplayMap[ nodeName ]; + + if ( display ) { + return display; + } + + temp = doc.body.appendChild( doc.createElement( nodeName ) ); + display = jQuery.css( temp, "display" ); + + temp.parentNode.removeChild( temp ); + + if ( display === "none" ) { + display = "block"; + } + defaultDisplayMap[ nodeName ] = display; + + return display; +} + +function showHide( elements, show ) { + var display, elem, + values = [], + index = 0, + length = elements.length; + + // Determine new display value for elements that need to change + for ( ; index < length; index++ ) { + elem = elements[ index ]; + if ( !elem.style ) { + continue; + } + + display = elem.style.display; + if ( show ) { + + // Since we force visibility upon cascade-hidden elements, an immediate (and slow) + // check is required in this first loop unless we have a nonempty display value (either + // inline or about-to-be-restored) + if ( display === "none" ) { + values[ index ] = dataPriv.get( elem, "display" ) || null; + if ( !values[ index ] ) { + elem.style.display = ""; + } + } + if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { + values[ index ] = getDefaultDisplay( elem ); + } + } else { + if ( display !== "none" ) { + values[ index ] = "none"; + + // Remember what we're overwriting + dataPriv.set( elem, "display", display ); + } + } + } + + // Set the display of the elements in a second loop to avoid constant reflow + for ( index = 0; index < length; index++ ) { + if ( values[ index ] != null ) { + elements[ index ].style.display = values[ index ]; + } + } + + return elements; +} + +jQuery.fn.extend( { + show: function() { + return showHide( this, true ); + }, + hide: function() { + return showHide( this ); + }, + toggle: function( state ) { + if ( typeof state === "boolean" ) { + return state ? this.show() : this.hide(); + } + + return this.each( function() { + if ( isHiddenWithinTree( this ) ) { + jQuery( this ).show(); + } else { + jQuery( this ).hide(); + } + } ); + } +} ); +var rcheckableType = ( /^(?:checkbox|radio)$/i ); + +var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); + +var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); + + + +( function() { + var fragment = document.createDocumentFragment(), + div = fragment.appendChild( document.createElement( "div" ) ), + input = document.createElement( "input" ); + + // Support: Android 4.0 - 4.3 only + // Check state lost if the name is set (#11217) + // Support: Windows Web Apps (WWA) + // `name` and `type` must use .setAttribute for WWA (#14901) + input.setAttribute( "type", "radio" ); + input.setAttribute( "checked", "checked" ); + input.setAttribute( "name", "t" ); + + div.appendChild( input ); + + // Support: Android <=4.1 only + // Older WebKit doesn't clone checked state correctly in fragments + support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; + + // Support: IE <=11 only + // Make sure textarea (and checkbox) defaultValue is properly cloned + div.innerHTML = ""; + support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; + + // Support: IE <=9 only + // IE <=9 replaces "; + support.option = !!div.lastChild; +} )(); + + +// We have to close these tags to support XHTML (#13200) +var wrapMap = { + + // XHTML parsers do not magically insert elements in the + // same way that tag soup parsers do. So we cannot shorten + // this by omitting or other required elements. + thead: [ 1, "", "
    " ], + col: [ 2, "", "
    " ], + tr: [ 2, "", "
    " ], + td: [ 3, "", "
    " ], + + _default: [ 0, "", "" ] +}; + +wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; +wrapMap.th = wrapMap.td; + +// Support: IE <=9 only +if ( !support.option ) { + wrapMap.optgroup = wrapMap.option = [ 1, "" ]; +} + + +function getAll( context, tag ) { + + // Support: IE <=9 - 11 only + // Use typeof to avoid zero-argument method invocation on host objects (#15151) + var ret; + + if ( typeof context.getElementsByTagName !== "undefined" ) { + ret = context.getElementsByTagName( tag || "*" ); + + } else if ( typeof context.querySelectorAll !== "undefined" ) { + ret = context.querySelectorAll( tag || "*" ); + + } else { + ret = []; + } + + if ( tag === undefined || tag && nodeName( context, tag ) ) { + return jQuery.merge( [ context ], ret ); + } + + return ret; +} + + +// Mark scripts as having already been evaluated +function setGlobalEval( elems, refElements ) { + var i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + dataPriv.set( + elems[ i ], + "globalEval", + !refElements || dataPriv.get( refElements[ i ], "globalEval" ) + ); + } +} + + +var rhtml = /<|&#?\w+;/; + +function buildFragment( elems, context, scripts, selection, ignored ) { + var elem, tmp, tag, wrap, attached, j, + fragment = context.createDocumentFragment(), + nodes = [], + i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + elem = elems[ i ]; + + if ( elem || elem === 0 ) { + + // Add nodes directly + if ( toType( elem ) === "object" ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); + + // Convert non-html into a text node + } else if ( !rhtml.test( elem ) ) { + nodes.push( context.createTextNode( elem ) ); + + // Convert html into DOM nodes + } else { + tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); + + // Deserialize a standard representation + tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); + wrap = wrapMap[ tag ] || wrapMap._default; + tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; + + // Descend through wrappers to the right content + j = wrap[ 0 ]; + while ( j-- ) { + tmp = tmp.lastChild; + } + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, tmp.childNodes ); + + // Remember the top-level container + tmp = fragment.firstChild; + + // Ensure the created nodes are orphaned (#12392) + tmp.textContent = ""; + } + } + } + + // Remove wrapper from fragment + fragment.textContent = ""; + + i = 0; + while ( ( elem = nodes[ i++ ] ) ) { + + // Skip elements already in the context collection (trac-4087) + if ( selection && jQuery.inArray( elem, selection ) > -1 ) { + if ( ignored ) { + ignored.push( elem ); + } + continue; + } + + attached = isAttached( elem ); + + // Append to fragment + tmp = getAll( fragment.appendChild( elem ), "script" ); + + // Preserve script evaluation history + if ( attached ) { + setGlobalEval( tmp ); + } + + // Capture executables + if ( scripts ) { + j = 0; + while ( ( elem = tmp[ j++ ] ) ) { + if ( rscriptType.test( elem.type || "" ) ) { + scripts.push( elem ); + } + } + } + } + + return fragment; +} + + +var rtypenamespace = /^([^.]*)(?:\.(.+)|)/; + +function returnTrue() { + return true; +} + +function returnFalse() { + return false; +} + +// Support: IE <=9 - 11+ +// focus() and blur() are asynchronous, except when they are no-op. +// So expect focus to be synchronous when the element is already active, +// and blur to be synchronous when the element is not already active. +// (focus and blur are always synchronous in other supported browsers, +// this just defines when we can count on it). +function expectSync( elem, type ) { + return ( elem === safeActiveElement() ) === ( type === "focus" ); +} + +// Support: IE <=9 only +// Accessing document.activeElement can throw unexpectedly +// https://bugs.jquery.com/ticket/13393 +function safeActiveElement() { + try { + return document.activeElement; + } catch ( err ) { } +} + +function on( elem, types, selector, data, fn, one ) { + var origFn, type; + + // Types can be a map of types/handlers + if ( typeof types === "object" ) { + + // ( types-Object, selector, data ) + if ( typeof selector !== "string" ) { + + // ( types-Object, data ) + data = data || selector; + selector = undefined; + } + for ( type in types ) { + on( elem, type, selector, data, types[ type ], one ); + } + return elem; + } + + if ( data == null && fn == null ) { + + // ( types, fn ) + fn = selector; + data = selector = undefined; + } else if ( fn == null ) { + if ( typeof selector === "string" ) { + + // ( types, selector, fn ) + fn = data; + data = undefined; + } else { + + // ( types, data, fn ) + fn = data; + data = selector; + selector = undefined; + } + } + if ( fn === false ) { + fn = returnFalse; + } else if ( !fn ) { + return elem; + } + + if ( one === 1 ) { + origFn = fn; + fn = function( event ) { + + // Can use an empty set, since event contains the info + jQuery().off( event ); + return origFn.apply( this, arguments ); + }; + + // Use same guid so caller can remove using origFn + fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); + } + return elem.each( function() { + jQuery.event.add( this, types, fn, data, selector ); + } ); +} + +/* + * Helper functions for managing events -- not part of the public interface. + * Props to Dean Edwards' addEvent library for many of the ideas. + */ +jQuery.event = { + + global: {}, + + add: function( elem, types, handler, data, selector ) { + + var handleObjIn, eventHandle, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.get( elem ); + + // Only attach events to objects that accept data + if ( !acceptData( elem ) ) { + return; + } + + // Caller can pass in an object of custom data in lieu of the handler + if ( handler.handler ) { + handleObjIn = handler; + handler = handleObjIn.handler; + selector = handleObjIn.selector; + } + + // Ensure that invalid selectors throw exceptions at attach time + // Evaluate against documentElement in case elem is a non-element node (e.g., document) + if ( selector ) { + jQuery.find.matchesSelector( documentElement, selector ); + } + + // Make sure that the handler has a unique ID, used to find/remove it later + if ( !handler.guid ) { + handler.guid = jQuery.guid++; + } + + // Init the element's event structure and main handler, if this is the first + if ( !( events = elemData.events ) ) { + events = elemData.events = Object.create( null ); + } + if ( !( eventHandle = elemData.handle ) ) { + eventHandle = elemData.handle = function( e ) { + + // Discard the second event of a jQuery.event.trigger() and + // when an event is called after a page has unloaded + return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? + jQuery.event.dispatch.apply( elem, arguments ) : undefined; + }; + } + + // Handle multiple events separated by a space + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // There *must* be a type, no attaching namespace-only handlers + if ( !type ) { + continue; + } + + // If event changes its type, use the special event handlers for the changed type + special = jQuery.event.special[ type ] || {}; + + // If selector defined, determine special event api type, otherwise given type + type = ( selector ? special.delegateType : special.bindType ) || type; + + // Update special based on newly reset type + special = jQuery.event.special[ type ] || {}; + + // handleObj is passed to all event handlers + handleObj = jQuery.extend( { + type: type, + origType: origType, + data: data, + handler: handler, + guid: handler.guid, + selector: selector, + needsContext: selector && jQuery.expr.match.needsContext.test( selector ), + namespace: namespaces.join( "." ) + }, handleObjIn ); + + // Init the event handler queue if we're the first + if ( !( handlers = events[ type ] ) ) { + handlers = events[ type ] = []; + handlers.delegateCount = 0; + + // Only use addEventListener if the special events handler returns false + if ( !special.setup || + special.setup.call( elem, data, namespaces, eventHandle ) === false ) { + + if ( elem.addEventListener ) { + elem.addEventListener( type, eventHandle ); + } + } + } + + if ( special.add ) { + special.add.call( elem, handleObj ); + + if ( !handleObj.handler.guid ) { + handleObj.handler.guid = handler.guid; + } + } + + // Add to the element's handler list, delegates in front + if ( selector ) { + handlers.splice( handlers.delegateCount++, 0, handleObj ); + } else { + handlers.push( handleObj ); + } + + // Keep track of which events have ever been used, for event optimization + jQuery.event.global[ type ] = true; + } + + }, + + // Detach an event or set of events from an element + remove: function( elem, types, handler, selector, mappedTypes ) { + + var j, origCount, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); + + if ( !elemData || !( events = elemData.events ) ) { + return; + } + + // Once for each type.namespace in types; type may be omitted + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // Unbind all events (on this namespace, if provided) for the element + if ( !type ) { + for ( type in events ) { + jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); + } + continue; + } + + special = jQuery.event.special[ type ] || {}; + type = ( selector ? special.delegateType : special.bindType ) || type; + handlers = events[ type ] || []; + tmp = tmp[ 2 ] && + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); + + // Remove matching events + origCount = j = handlers.length; + while ( j-- ) { + handleObj = handlers[ j ]; + + if ( ( mappedTypes || origType === handleObj.origType ) && + ( !handler || handler.guid === handleObj.guid ) && + ( !tmp || tmp.test( handleObj.namespace ) ) && + ( !selector || selector === handleObj.selector || + selector === "**" && handleObj.selector ) ) { + handlers.splice( j, 1 ); + + if ( handleObj.selector ) { + handlers.delegateCount--; + } + if ( special.remove ) { + special.remove.call( elem, handleObj ); + } + } + } + + // Remove generic event handler if we removed something and no more handlers exist + // (avoids potential for endless recursion during removal of special event handlers) + if ( origCount && !handlers.length ) { + if ( !special.teardown || + special.teardown.call( elem, namespaces, elemData.handle ) === false ) { + + jQuery.removeEvent( elem, type, elemData.handle ); + } + + delete events[ type ]; + } + } + + // Remove data and the expando if it's no longer used + if ( jQuery.isEmptyObject( events ) ) { + dataPriv.remove( elem, "handle events" ); + } + }, + + dispatch: function( nativeEvent ) { + + var i, j, ret, matched, handleObj, handlerQueue, + args = new Array( arguments.length ), + + // Make a writable jQuery.Event from the native event object + event = jQuery.event.fix( nativeEvent ), + + handlers = ( + dataPriv.get( this, "events" ) || Object.create( null ) + )[ event.type ] || [], + special = jQuery.event.special[ event.type ] || {}; + + // Use the fix-ed jQuery.Event rather than the (read-only) native event + args[ 0 ] = event; + + for ( i = 1; i < arguments.length; i++ ) { + args[ i ] = arguments[ i ]; + } + + event.delegateTarget = this; + + // Call the preDispatch hook for the mapped type, and let it bail if desired + if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { + return; + } + + // Determine handlers + handlerQueue = jQuery.event.handlers.call( this, event, handlers ); + + // Run delegates first; they may want to stop propagation beneath us + i = 0; + while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { + event.currentTarget = matched.elem; + + j = 0; + while ( ( handleObj = matched.handlers[ j++ ] ) && + !event.isImmediatePropagationStopped() ) { + + // If the event is namespaced, then each handler is only invoked if it is + // specially universal or its namespaces are a superset of the event's. + if ( !event.rnamespace || handleObj.namespace === false || + event.rnamespace.test( handleObj.namespace ) ) { + + event.handleObj = handleObj; + event.data = handleObj.data; + + ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || + handleObj.handler ).apply( matched.elem, args ); + + if ( ret !== undefined ) { + if ( ( event.result = ret ) === false ) { + event.preventDefault(); + event.stopPropagation(); + } + } + } + } + } + + // Call the postDispatch hook for the mapped type + if ( special.postDispatch ) { + special.postDispatch.call( this, event ); + } + + return event.result; + }, + + handlers: function( event, handlers ) { + var i, handleObj, sel, matchedHandlers, matchedSelectors, + handlerQueue = [], + delegateCount = handlers.delegateCount, + cur = event.target; + + // Find delegate handlers + if ( delegateCount && + + // Support: IE <=9 + // Black-hole SVG instance trees (trac-13180) + cur.nodeType && + + // Support: Firefox <=42 + // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) + // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click + // Support: IE 11 only + // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) + !( event.type === "click" && event.button >= 1 ) ) { + + for ( ; cur !== this; cur = cur.parentNode || this ) { + + // Don't check non-elements (#13208) + // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) + if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { + matchedHandlers = []; + matchedSelectors = {}; + for ( i = 0; i < delegateCount; i++ ) { + handleObj = handlers[ i ]; + + // Don't conflict with Object.prototype properties (#13203) + sel = handleObj.selector + " "; + + if ( matchedSelectors[ sel ] === undefined ) { + matchedSelectors[ sel ] = handleObj.needsContext ? + jQuery( sel, this ).index( cur ) > -1 : + jQuery.find( sel, this, null, [ cur ] ).length; + } + if ( matchedSelectors[ sel ] ) { + matchedHandlers.push( handleObj ); + } + } + if ( matchedHandlers.length ) { + handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); + } + } + } + } + + // Add the remaining (directly-bound) handlers + cur = this; + if ( delegateCount < handlers.length ) { + handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); + } + + return handlerQueue; + }, + + addProp: function( name, hook ) { + Object.defineProperty( jQuery.Event.prototype, name, { + enumerable: true, + configurable: true, + + get: isFunction( hook ) ? + function() { + if ( this.originalEvent ) { + return hook( this.originalEvent ); + } + } : + function() { + if ( this.originalEvent ) { + return this.originalEvent[ name ]; + } + }, + + set: function( value ) { + Object.defineProperty( this, name, { + enumerable: true, + configurable: true, + writable: true, + value: value + } ); + } + } ); + }, + + fix: function( originalEvent ) { + return originalEvent[ jQuery.expando ] ? + originalEvent : + new jQuery.Event( originalEvent ); + }, + + special: { + load: { + + // Prevent triggered image.load events from bubbling to window.load + noBubble: true + }, + click: { + + // Utilize native event to ensure correct state for checkable inputs + setup: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Claim the first handler + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + // dataPriv.set( el, "click", ... ) + leverageNative( el, "click", returnTrue ); + } + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Force setup before triggering a click + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + leverageNative( el, "click" ); + } + + // Return non-false to allow normal event-path propagation + return true; + }, + + // For cross-browser consistency, suppress native .click() on links + // Also prevent it if we're currently inside a leveraged native-event stack + _default: function( event ) { + var target = event.target; + return rcheckableType.test( target.type ) && + target.click && nodeName( target, "input" ) && + dataPriv.get( target, "click" ) || + nodeName( target, "a" ); + } + }, + + beforeunload: { + postDispatch: function( event ) { + + // Support: Firefox 20+ + // Firefox doesn't alert if the returnValue field is not set. + if ( event.result !== undefined && event.originalEvent ) { + event.originalEvent.returnValue = event.result; + } + } + } + } +}; + +// Ensure the presence of an event listener that handles manually-triggered +// synthetic events by interrupting progress until reinvoked in response to +// *native* events that it fires directly, ensuring that state changes have +// already occurred before other listeners are invoked. +function leverageNative( el, type, expectSync ) { + + // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add + if ( !expectSync ) { + if ( dataPriv.get( el, type ) === undefined ) { + jQuery.event.add( el, type, returnTrue ); + } + return; + } + + // Register the controller as a special universal handler for all event namespaces + dataPriv.set( el, type, false ); + jQuery.event.add( el, type, { + namespace: false, + handler: function( event ) { + var notAsync, result, + saved = dataPriv.get( this, type ); + + if ( ( event.isTrigger & 1 ) && this[ type ] ) { + + // Interrupt processing of the outer synthetic .trigger()ed event + // Saved data should be false in such cases, but might be a leftover capture object + // from an async native handler (gh-4350) + if ( !saved.length ) { + + // Store arguments for use when handling the inner native event + // There will always be at least one argument (an event object), so this array + // will not be confused with a leftover capture object. + saved = slice.call( arguments ); + dataPriv.set( this, type, saved ); + + // Trigger the native event and capture its result + // Support: IE <=9 - 11+ + // focus() and blur() are asynchronous + notAsync = expectSync( this, type ); + this[ type ](); + result = dataPriv.get( this, type ); + if ( saved !== result || notAsync ) { + dataPriv.set( this, type, false ); + } else { + result = {}; + } + if ( saved !== result ) { + + // Cancel the outer synthetic event + event.stopImmediatePropagation(); + event.preventDefault(); + + // Support: Chrome 86+ + // In Chrome, if an element having a focusout handler is blurred by + // clicking outside of it, it invokes the handler synchronously. If + // that handler calls `.remove()` on the element, the data is cleared, + // leaving `result` undefined. We need to guard against this. + return result && result.value; + } + + // If this is an inner synthetic event for an event with a bubbling surrogate + // (focus or blur), assume that the surrogate already propagated from triggering the + // native event and prevent that from happening again here. + // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the + // bubbling surrogate propagates *after* the non-bubbling base), but that seems + // less bad than duplication. + } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { + event.stopPropagation(); + } + + // If this is a native event triggered above, everything is now in order + // Fire an inner synthetic event with the original arguments + } else if ( saved.length ) { + + // ...and capture the result + dataPriv.set( this, type, { + value: jQuery.event.trigger( + + // Support: IE <=9 - 11+ + // Extend with the prototype to reset the above stopImmediatePropagation() + jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), + saved.slice( 1 ), + this + ) + } ); + + // Abort handling of the native event + event.stopImmediatePropagation(); + } + } + } ); +} + +jQuery.removeEvent = function( elem, type, handle ) { + + // This "if" is needed for plain objects + if ( elem.removeEventListener ) { + elem.removeEventListener( type, handle ); + } +}; + +jQuery.Event = function( src, props ) { + + // Allow instantiation without the 'new' keyword + if ( !( this instanceof jQuery.Event ) ) { + return new jQuery.Event( src, props ); + } + + // Event object + if ( src && src.type ) { + this.originalEvent = src; + this.type = src.type; + + // Events bubbling up the document may have been marked as prevented + // by a handler lower down the tree; reflect the correct value. + this.isDefaultPrevented = src.defaultPrevented || + src.defaultPrevented === undefined && + + // Support: Android <=2.3 only + src.returnValue === false ? + returnTrue : + returnFalse; + + // Create target properties + // Support: Safari <=6 - 7 only + // Target should not be a text node (#504, #13143) + this.target = ( src.target && src.target.nodeType === 3 ) ? + src.target.parentNode : + src.target; + + this.currentTarget = src.currentTarget; + this.relatedTarget = src.relatedTarget; + + // Event type + } else { + this.type = src; + } + + // Put explicitly provided properties onto the event object + if ( props ) { + jQuery.extend( this, props ); + } + + // Create a timestamp if incoming event doesn't have one + this.timeStamp = src && src.timeStamp || Date.now(); + + // Mark it as fixed + this[ jQuery.expando ] = true; +}; + +// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding +// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html +jQuery.Event.prototype = { + constructor: jQuery.Event, + isDefaultPrevented: returnFalse, + isPropagationStopped: returnFalse, + isImmediatePropagationStopped: returnFalse, + isSimulated: false, + + preventDefault: function() { + var e = this.originalEvent; + + this.isDefaultPrevented = returnTrue; + + if ( e && !this.isSimulated ) { + e.preventDefault(); + } + }, + stopPropagation: function() { + var e = this.originalEvent; + + this.isPropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopPropagation(); + } + }, + stopImmediatePropagation: function() { + var e = this.originalEvent; + + this.isImmediatePropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopImmediatePropagation(); + } + + this.stopPropagation(); + } +}; + +// Includes all common event props including KeyEvent and MouseEvent specific props +jQuery.each( { + altKey: true, + bubbles: true, + cancelable: true, + changedTouches: true, + ctrlKey: true, + detail: true, + eventPhase: true, + metaKey: true, + pageX: true, + pageY: true, + shiftKey: true, + view: true, + "char": true, + code: true, + charCode: true, + key: true, + keyCode: true, + button: true, + buttons: true, + clientX: true, + clientY: true, + offsetX: true, + offsetY: true, + pointerId: true, + pointerType: true, + screenX: true, + screenY: true, + targetTouches: true, + toElement: true, + touches: true, + which: true +}, jQuery.event.addProp ); + +jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { + jQuery.event.special[ type ] = { + + // Utilize native event if possible so blur/focus sequence is correct + setup: function() { + + // Claim the first handler + // dataPriv.set( this, "focus", ... ) + // dataPriv.set( this, "blur", ... ) + leverageNative( this, type, expectSync ); + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function() { + + // Force setup before trigger + leverageNative( this, type ); + + // Return non-false to allow normal event-path propagation + return true; + }, + + // Suppress native focus or blur as it's already being fired + // in leverageNative. + _default: function() { + return true; + }, + + delegateType: delegateType + }; +} ); + +// Create mouseenter/leave events using mouseover/out and event-time checks +// so that event delegation works in jQuery. +// Do the same for pointerenter/pointerleave and pointerover/pointerout +// +// Support: Safari 7 only +// Safari sends mouseenter too often; see: +// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 +// for the description of the bug (it existed in older Chrome versions as well). +jQuery.each( { + mouseenter: "mouseover", + mouseleave: "mouseout", + pointerenter: "pointerover", + pointerleave: "pointerout" +}, function( orig, fix ) { + jQuery.event.special[ orig ] = { + delegateType: fix, + bindType: fix, + + handle: function( event ) { + var ret, + target = this, + related = event.relatedTarget, + handleObj = event.handleObj; + + // For mouseenter/leave call the handler if related is outside the target. + // NB: No relatedTarget if the mouse left/entered the browser window + if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { + event.type = handleObj.origType; + ret = handleObj.handler.apply( this, arguments ); + event.type = fix; + } + return ret; + } + }; +} ); + +jQuery.fn.extend( { + + on: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn ); + }, + one: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn, 1 ); + }, + off: function( types, selector, fn ) { + var handleObj, type; + if ( types && types.preventDefault && types.handleObj ) { + + // ( event ) dispatched jQuery.Event + handleObj = types.handleObj; + jQuery( types.delegateTarget ).off( + handleObj.namespace ? + handleObj.origType + "." + handleObj.namespace : + handleObj.origType, + handleObj.selector, + handleObj.handler + ); + return this; + } + if ( typeof types === "object" ) { + + // ( types-object [, selector] ) + for ( type in types ) { + this.off( type, selector, types[ type ] ); + } + return this; + } + if ( selector === false || typeof selector === "function" ) { + + // ( types [, fn] ) + fn = selector; + selector = undefined; + } + if ( fn === false ) { + fn = returnFalse; + } + return this.each( function() { + jQuery.event.remove( this, types, fn, selector ); + } ); + } +} ); + + +var + + // Support: IE <=10 - 11, Edge 12 - 13 only + // In IE/Edge using regex groups here causes severe slowdowns. + // See https://connect.microsoft.com/IE/feedback/details/1736512/ + rnoInnerhtml = /\s*$/g; + +// Prefer a tbody over its parent table for containing new rows +function manipulationTarget( elem, content ) { + if ( nodeName( elem, "table" ) && + nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { + + return jQuery( elem ).children( "tbody" )[ 0 ] || elem; + } + + return elem; +} + +// Replace/restore the type attribute of script elements for safe DOM manipulation +function disableScript( elem ) { + elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; + return elem; +} +function restoreScript( elem ) { + if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { + elem.type = elem.type.slice( 5 ); + } else { + elem.removeAttribute( "type" ); + } + + return elem; +} + +function cloneCopyEvent( src, dest ) { + var i, l, type, pdataOld, udataOld, udataCur, events; + + if ( dest.nodeType !== 1 ) { + return; + } + + // 1. Copy private data: events, handlers, etc. + if ( dataPriv.hasData( src ) ) { + pdataOld = dataPriv.get( src ); + events = pdataOld.events; + + if ( events ) { + dataPriv.remove( dest, "handle events" ); + + for ( type in events ) { + for ( i = 0, l = events[ type ].length; i < l; i++ ) { + jQuery.event.add( dest, type, events[ type ][ i ] ); + } + } + } + } + + // 2. Copy user data + if ( dataUser.hasData( src ) ) { + udataOld = dataUser.access( src ); + udataCur = jQuery.extend( {}, udataOld ); + + dataUser.set( dest, udataCur ); + } +} + +// Fix IE bugs, see support tests +function fixInput( src, dest ) { + var nodeName = dest.nodeName.toLowerCase(); + + // Fails to persist the checked state of a cloned checkbox or radio button. + if ( nodeName === "input" && rcheckableType.test( src.type ) ) { + dest.checked = src.checked; + + // Fails to return the selected option to the default selected state when cloning options + } else if ( nodeName === "input" || nodeName === "textarea" ) { + dest.defaultValue = src.defaultValue; + } +} + +function domManip( collection, args, callback, ignored ) { + + // Flatten any nested arrays + args = flat( args ); + + var fragment, first, scripts, hasScripts, node, doc, + i = 0, + l = collection.length, + iNoClone = l - 1, + value = args[ 0 ], + valueIsFunction = isFunction( value ); + + // We can't cloneNode fragments that contain checked, in WebKit + if ( valueIsFunction || + ( l > 1 && typeof value === "string" && + !support.checkClone && rchecked.test( value ) ) ) { + return collection.each( function( index ) { + var self = collection.eq( index ); + if ( valueIsFunction ) { + args[ 0 ] = value.call( this, index, self.html() ); + } + domManip( self, args, callback, ignored ); + } ); + } + + if ( l ) { + fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); + first = fragment.firstChild; + + if ( fragment.childNodes.length === 1 ) { + fragment = first; + } + + // Require either new content or an interest in ignored elements to invoke the callback + if ( first || ignored ) { + scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); + hasScripts = scripts.length; + + // Use the original fragment for the last item + // instead of the first because it can end up + // being emptied incorrectly in certain situations (#8070). + for ( ; i < l; i++ ) { + node = fragment; + + if ( i !== iNoClone ) { + node = jQuery.clone( node, true, true ); + + // Keep references to cloned scripts for later restoration + if ( hasScripts ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( scripts, getAll( node, "script" ) ); + } + } + + callback.call( collection[ i ], node, i ); + } + + if ( hasScripts ) { + doc = scripts[ scripts.length - 1 ].ownerDocument; + + // Reenable scripts + jQuery.map( scripts, restoreScript ); + + // Evaluate executable scripts on first document insertion + for ( i = 0; i < hasScripts; i++ ) { + node = scripts[ i ]; + if ( rscriptType.test( node.type || "" ) && + !dataPriv.access( node, "globalEval" ) && + jQuery.contains( doc, node ) ) { + + if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { + + // Optional AJAX dependency, but won't run scripts if not present + if ( jQuery._evalUrl && !node.noModule ) { + jQuery._evalUrl( node.src, { + nonce: node.nonce || node.getAttribute( "nonce" ) + }, doc ); + } + } else { + DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); + } + } + } + } + } + } + + return collection; +} + +function remove( elem, selector, keepData ) { + var node, + nodes = selector ? jQuery.filter( selector, elem ) : elem, + i = 0; + + for ( ; ( node = nodes[ i ] ) != null; i++ ) { + if ( !keepData && node.nodeType === 1 ) { + jQuery.cleanData( getAll( node ) ); + } + + if ( node.parentNode ) { + if ( keepData && isAttached( node ) ) { + setGlobalEval( getAll( node, "script" ) ); + } + node.parentNode.removeChild( node ); + } + } + + return elem; +} + +jQuery.extend( { + htmlPrefilter: function( html ) { + return html; + }, + + clone: function( elem, dataAndEvents, deepDataAndEvents ) { + var i, l, srcElements, destElements, + clone = elem.cloneNode( true ), + inPage = isAttached( elem ); + + // Fix IE cloning issues + if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && + !jQuery.isXMLDoc( elem ) ) { + + // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 + destElements = getAll( clone ); + srcElements = getAll( elem ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + fixInput( srcElements[ i ], destElements[ i ] ); + } + } + + // Copy the events from the original to the clone + if ( dataAndEvents ) { + if ( deepDataAndEvents ) { + srcElements = srcElements || getAll( elem ); + destElements = destElements || getAll( clone ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + cloneCopyEvent( srcElements[ i ], destElements[ i ] ); + } + } else { + cloneCopyEvent( elem, clone ); + } + } + + // Preserve script evaluation history + destElements = getAll( clone, "script" ); + if ( destElements.length > 0 ) { + setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); + } + + // Return the cloned set + return clone; + }, + + cleanData: function( elems ) { + var data, elem, type, + special = jQuery.event.special, + i = 0; + + for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { + if ( acceptData( elem ) ) { + if ( ( data = elem[ dataPriv.expando ] ) ) { + if ( data.events ) { + for ( type in data.events ) { + if ( special[ type ] ) { + jQuery.event.remove( elem, type ); + + // This is a shortcut to avoid jQuery.event.remove's overhead + } else { + jQuery.removeEvent( elem, type, data.handle ); + } + } + } + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataPriv.expando ] = undefined; + } + if ( elem[ dataUser.expando ] ) { + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataUser.expando ] = undefined; + } + } + } + } +} ); + +jQuery.fn.extend( { + detach: function( selector ) { + return remove( this, selector, true ); + }, + + remove: function( selector ) { + return remove( this, selector ); + }, + + text: function( value ) { + return access( this, function( value ) { + return value === undefined ? + jQuery.text( this ) : + this.empty().each( function() { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + this.textContent = value; + } + } ); + }, null, value, arguments.length ); + }, + + append: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.appendChild( elem ); + } + } ); + }, + + prepend: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.insertBefore( elem, target.firstChild ); + } + } ); + }, + + before: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this ); + } + } ); + }, + + after: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this.nextSibling ); + } + } ); + }, + + empty: function() { + var elem, + i = 0; + + for ( ; ( elem = this[ i ] ) != null; i++ ) { + if ( elem.nodeType === 1 ) { + + // Prevent memory leaks + jQuery.cleanData( getAll( elem, false ) ); + + // Remove any remaining nodes + elem.textContent = ""; + } + } + + return this; + }, + + clone: function( dataAndEvents, deepDataAndEvents ) { + dataAndEvents = dataAndEvents == null ? false : dataAndEvents; + deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; + + return this.map( function() { + return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); + } ); + }, + + html: function( value ) { + return access( this, function( value ) { + var elem = this[ 0 ] || {}, + i = 0, + l = this.length; + + if ( value === undefined && elem.nodeType === 1 ) { + return elem.innerHTML; + } + + // See if we can take a shortcut and just use innerHTML + if ( typeof value === "string" && !rnoInnerhtml.test( value ) && + !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { + + value = jQuery.htmlPrefilter( value ); + + try { + for ( ; i < l; i++ ) { + elem = this[ i ] || {}; + + // Remove element nodes and prevent memory leaks + if ( elem.nodeType === 1 ) { + jQuery.cleanData( getAll( elem, false ) ); + elem.innerHTML = value; + } + } + + elem = 0; + + // If using innerHTML throws an exception, use the fallback method + } catch ( e ) {} + } + + if ( elem ) { + this.empty().append( value ); + } + }, null, value, arguments.length ); + }, + + replaceWith: function() { + var ignored = []; + + // Make the changes, replacing each non-ignored context element with the new content + return domManip( this, arguments, function( elem ) { + var parent = this.parentNode; + + if ( jQuery.inArray( this, ignored ) < 0 ) { + jQuery.cleanData( getAll( this ) ); + if ( parent ) { + parent.replaceChild( elem, this ); + } + } + + // Force callback invocation + }, ignored ); + } +} ); + +jQuery.each( { + appendTo: "append", + prependTo: "prepend", + insertBefore: "before", + insertAfter: "after", + replaceAll: "replaceWith" +}, function( name, original ) { + jQuery.fn[ name ] = function( selector ) { + var elems, + ret = [], + insert = jQuery( selector ), + last = insert.length - 1, + i = 0; + + for ( ; i <= last; i++ ) { + elems = i === last ? this : this.clone( true ); + jQuery( insert[ i ] )[ original ]( elems ); + + // Support: Android <=4.0 only, PhantomJS 1 only + // .get() because push.apply(_, arraylike) throws on ancient WebKit + push.apply( ret, elems.get() ); + } + + return this.pushStack( ret ); + }; +} ); +var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); + +var getStyles = function( elem ) { + + // Support: IE <=11 only, Firefox <=30 (#15098, #14150) + // IE throws on elements created in popups + // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" + var view = elem.ownerDocument.defaultView; + + if ( !view || !view.opener ) { + view = window; + } + + return view.getComputedStyle( elem ); + }; + +var swap = function( elem, options, callback ) { + var ret, name, + old = {}; + + // Remember the old values, and insert the new ones + for ( name in options ) { + old[ name ] = elem.style[ name ]; + elem.style[ name ] = options[ name ]; + } + + ret = callback.call( elem ); + + // Revert the old values + for ( name in options ) { + elem.style[ name ] = old[ name ]; + } + + return ret; +}; + + +var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); + + + +( function() { + + // Executing both pixelPosition & boxSizingReliable tests require only one layout + // so they're executed at the same time to save the second computation. + function computeStyleTests() { + + // This is a singleton, we need to execute it only once + if ( !div ) { + return; + } + + container.style.cssText = "position:absolute;left:-11111px;width:60px;" + + "margin-top:1px;padding:0;border:0"; + div.style.cssText = + "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + + "margin:auto;border:1px;padding:1px;" + + "width:60%;top:1%"; + documentElement.appendChild( container ).appendChild( div ); + + var divStyle = window.getComputedStyle( div ); + pixelPositionVal = divStyle.top !== "1%"; + + // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 + reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; + + // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 + // Some styles come back with percentage values, even though they shouldn't + div.style.right = "60%"; + pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; + + // Support: IE 9 - 11 only + // Detect misreporting of content dimensions for box-sizing:border-box elements + boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; + + // Support: IE 9 only + // Detect overflow:scroll screwiness (gh-3699) + // Support: Chrome <=64 + // Don't get tricked when zoom affects offsetWidth (gh-4029) + div.style.position = "absolute"; + scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; + + documentElement.removeChild( container ); + + // Nullify the div so it wouldn't be stored in the memory and + // it will also be a sign that checks already performed + div = null; + } + + function roundPixelMeasures( measure ) { + return Math.round( parseFloat( measure ) ); + } + + var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, + reliableTrDimensionsVal, reliableMarginLeftVal, + container = document.createElement( "div" ), + div = document.createElement( "div" ); + + // Finish early in limited (non-browser) environments + if ( !div.style ) { + return; + } + + // Support: IE <=9 - 11 only + // Style of cloned element affects source element cloned (#8908) + div.style.backgroundClip = "content-box"; + div.cloneNode( true ).style.backgroundClip = ""; + support.clearCloneStyle = div.style.backgroundClip === "content-box"; + + jQuery.extend( support, { + boxSizingReliable: function() { + computeStyleTests(); + return boxSizingReliableVal; + }, + pixelBoxStyles: function() { + computeStyleTests(); + return pixelBoxStylesVal; + }, + pixelPosition: function() { + computeStyleTests(); + return pixelPositionVal; + }, + reliableMarginLeft: function() { + computeStyleTests(); + return reliableMarginLeftVal; + }, + scrollboxSize: function() { + computeStyleTests(); + return scrollboxSizeVal; + }, + + // Support: IE 9 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Behavior in IE 9 is more subtle than in newer versions & it passes + // some versions of this test; make sure not to make it pass there! + // + // Support: Firefox 70+ + // Only Firefox includes border widths + // in computed dimensions. (gh-4529) + reliableTrDimensions: function() { + var table, tr, trChild, trStyle; + if ( reliableTrDimensionsVal == null ) { + table = document.createElement( "table" ); + tr = document.createElement( "tr" ); + trChild = document.createElement( "div" ); + + table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate"; + tr.style.cssText = "border:1px solid"; + + // Support: Chrome 86+ + // Height set through cssText does not get applied. + // Computed height then comes back as 0. + tr.style.height = "1px"; + trChild.style.height = "9px"; + + // Support: Android 8 Chrome 86+ + // In our bodyBackground.html iframe, + // display for all div elements is set to "inline", + // which causes a problem only in Android 8 Chrome 86. + // Ensuring the div is display: block + // gets around this issue. + trChild.style.display = "block"; + + documentElement + .appendChild( table ) + .appendChild( tr ) + .appendChild( trChild ); + + trStyle = window.getComputedStyle( tr ); + reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) + + parseInt( trStyle.borderTopWidth, 10 ) + + parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight; + + documentElement.removeChild( table ); + } + return reliableTrDimensionsVal; + } + } ); +} )(); + + +function curCSS( elem, name, computed ) { + var width, minWidth, maxWidth, ret, + + // Support: Firefox 51+ + // Retrieving style before computed somehow + // fixes an issue with getting wrong values + // on detached elements + style = elem.style; + + computed = computed || getStyles( elem ); + + // getPropertyValue is needed for: + // .css('filter') (IE 9 only, #12537) + // .css('--customProperty) (#3144) + if ( computed ) { + ret = computed.getPropertyValue( name ) || computed[ name ]; + + if ( ret === "" && !isAttached( elem ) ) { + ret = jQuery.style( elem, name ); + } + + // A tribute to the "awesome hack by Dean Edwards" + // Android Browser returns percentage for some values, + // but width seems to be reliably pixels. + // This is against the CSSOM draft spec: + // https://drafts.csswg.org/cssom/#resolved-values + if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { + + // Remember the original values + width = style.width; + minWidth = style.minWidth; + maxWidth = style.maxWidth; + + // Put in the new values to get a computed value out + style.minWidth = style.maxWidth = style.width = ret; + ret = computed.width; + + // Revert the changed values + style.width = width; + style.minWidth = minWidth; + style.maxWidth = maxWidth; + } + } + + return ret !== undefined ? + + // Support: IE <=9 - 11 only + // IE returns zIndex value as an integer. + ret + "" : + ret; +} + + +function addGetHookIf( conditionFn, hookFn ) { + + // Define the hook, we'll check on the first run if it's really needed. + return { + get: function() { + if ( conditionFn() ) { + + // Hook not needed (or it's not possible to use it due + // to missing dependency), remove it. + delete this.get; + return; + } + + // Hook needed; redefine it so that the support test is not executed again. + return ( this.get = hookFn ).apply( this, arguments ); + } + }; +} + + +var cssPrefixes = [ "Webkit", "Moz", "ms" ], + emptyStyle = document.createElement( "div" ).style, + vendorProps = {}; + +// Return a vendor-prefixed property or undefined +function vendorPropName( name ) { + + // Check for vendor prefixed names + var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), + i = cssPrefixes.length; + + while ( i-- ) { + name = cssPrefixes[ i ] + capName; + if ( name in emptyStyle ) { + return name; + } + } +} + +// Return a potentially-mapped jQuery.cssProps or vendor prefixed property +function finalPropName( name ) { + var final = jQuery.cssProps[ name ] || vendorProps[ name ]; + + if ( final ) { + return final; + } + if ( name in emptyStyle ) { + return name; + } + return vendorProps[ name ] = vendorPropName( name ) || name; +} + + +var + + // Swappable if display is none or starts with table + // except "table", "table-cell", or "table-caption" + // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display + rdisplayswap = /^(none|table(?!-c[ea]).+)/, + rcustomProp = /^--/, + cssShow = { position: "absolute", visibility: "hidden", display: "block" }, + cssNormalTransform = { + letterSpacing: "0", + fontWeight: "400" + }; + +function setPositiveNumber( _elem, value, subtract ) { + + // Any relative (+/-) values have already been + // normalized at this point + var matches = rcssNum.exec( value ); + return matches ? + + // Guard against undefined "subtract", e.g., when used as in cssHooks + Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : + value; +} + +function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { + var i = dimension === "width" ? 1 : 0, + extra = 0, + delta = 0; + + // Adjustment may not be necessary + if ( box === ( isBorderBox ? "border" : "content" ) ) { + return 0; + } + + for ( ; i < 4; i += 2 ) { + + // Both box models exclude margin + if ( box === "margin" ) { + delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); + } + + // If we get here with a content-box, we're seeking "padding" or "border" or "margin" + if ( !isBorderBox ) { + + // Add padding + delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + + // For "border" or "margin", add border + if ( box !== "padding" ) { + delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + + // But still keep track of it otherwise + } else { + extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + + // If we get here with a border-box (content + padding + border), we're seeking "content" or + // "padding" or "margin" + } else { + + // For "content", subtract padding + if ( box === "content" ) { + delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + } + + // For "content" or "padding", subtract border + if ( box !== "margin" ) { + delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + } + } + + // Account for positive content-box scroll gutter when requested by providing computedVal + if ( !isBorderBox && computedVal >= 0 ) { + + // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border + // Assuming integer scroll gutter, subtract the rest and round down + delta += Math.max( 0, Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + computedVal - + delta - + extra - + 0.5 + + // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter + // Use an explicit zero to avoid NaN (gh-3964) + ) ) || 0; + } + + return delta; +} + +function getWidthOrHeight( elem, dimension, extra ) { + + // Start with computed style + var styles = getStyles( elem ), + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). + // Fake content-box until we know it's needed to know the true value. + boxSizingNeeded = !support.boxSizingReliable() || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + valueIsBorderBox = isBorderBox, + + val = curCSS( elem, dimension, styles ), + offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); + + // Support: Firefox <=54 + // Return a confounding non-pixel value or feign ignorance, as appropriate. + if ( rnumnonpx.test( val ) ) { + if ( !extra ) { + return val; + } + val = "auto"; + } + + + // Support: IE 9 - 11 only + // Use offsetWidth/offsetHeight for when box sizing is unreliable. + // In those cases, the computed value can be trusted to be border-box. + if ( ( !support.boxSizingReliable() && isBorderBox || + + // Support: IE 10 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Interestingly, in some cases IE 9 doesn't suffer from this issue. + !support.reliableTrDimensions() && nodeName( elem, "tr" ) || + + // Fall back to offsetWidth/offsetHeight when value is "auto" + // This happens for inline elements with no explicit setting (gh-3571) + val === "auto" || + + // Support: Android <=4.1 - 4.3 only + // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) + !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && + + // Make sure the element is visible & connected + elem.getClientRects().length ) { + + isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; + + // Where available, offsetWidth/offsetHeight approximate border box dimensions. + // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the + // retrieved value as a content box dimension. + valueIsBorderBox = offsetProp in elem; + if ( valueIsBorderBox ) { + val = elem[ offsetProp ]; + } + } + + // Normalize "" and auto + val = parseFloat( val ) || 0; + + // Adjust for the element's box model + return ( val + + boxModelAdjustment( + elem, + dimension, + extra || ( isBorderBox ? "border" : "content" ), + valueIsBorderBox, + styles, + + // Provide the current computed size to request scroll gutter calculation (gh-3589) + val + ) + ) + "px"; +} + +jQuery.extend( { + + // Add in style property hooks for overriding the default + // behavior of getting and setting a style property + cssHooks: { + opacity: { + get: function( elem, computed ) { + if ( computed ) { + + // We should always get a number back from opacity + var ret = curCSS( elem, "opacity" ); + return ret === "" ? "1" : ret; + } + } + } + }, + + // Don't automatically add "px" to these possibly-unitless properties + cssNumber: { + "animationIterationCount": true, + "columnCount": true, + "fillOpacity": true, + "flexGrow": true, + "flexShrink": true, + "fontWeight": true, + "gridArea": true, + "gridColumn": true, + "gridColumnEnd": true, + "gridColumnStart": true, + "gridRow": true, + "gridRowEnd": true, + "gridRowStart": true, + "lineHeight": true, + "opacity": true, + "order": true, + "orphans": true, + "widows": true, + "zIndex": true, + "zoom": true + }, + + // Add in properties whose names you wish to fix before + // setting or getting the value + cssProps: {}, + + // Get and set the style property on a DOM Node + style: function( elem, name, value, extra ) { + + // Don't set styles on text and comment nodes + if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { + return; + } + + // Make sure that we're working with the right name + var ret, type, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ), + style = elem.style; + + // Make sure that we're working with the right name. We don't + // want to query the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Gets hook for the prefixed version, then unprefixed version + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // Check if we're setting a value + if ( value !== undefined ) { + type = typeof value; + + // Convert "+=" or "-=" to relative numbers (#7345) + if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { + value = adjustCSS( elem, name, ret ); + + // Fixes bug #9237 + type = "number"; + } + + // Make sure that null and NaN values aren't set (#7116) + if ( value == null || value !== value ) { + return; + } + + // If a number was passed in, add the unit (except for certain CSS properties) + // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append + // "px" to a few hardcoded values. + if ( type === "number" && !isCustomProp ) { + value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); + } + + // background-* props affect original clone's values + if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { + style[ name ] = "inherit"; + } + + // If a hook was provided, use that value, otherwise just set the specified value + if ( !hooks || !( "set" in hooks ) || + ( value = hooks.set( elem, value, extra ) ) !== undefined ) { + + if ( isCustomProp ) { + style.setProperty( name, value ); + } else { + style[ name ] = value; + } + } + + } else { + + // If a hook was provided get the non-computed value from there + if ( hooks && "get" in hooks && + ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { + + return ret; + } + + // Otherwise just get the value from the style object + return style[ name ]; + } + }, + + css: function( elem, name, extra, styles ) { + var val, num, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ); + + // Make sure that we're working with the right name. We don't + // want to modify the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Try prefixed name followed by the unprefixed name + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // If a hook was provided get the computed value from there + if ( hooks && "get" in hooks ) { + val = hooks.get( elem, true, extra ); + } + + // Otherwise, if a way to get the computed value exists, use that + if ( val === undefined ) { + val = curCSS( elem, name, styles ); + } + + // Convert "normal" to computed value + if ( val === "normal" && name in cssNormalTransform ) { + val = cssNormalTransform[ name ]; + } + + // Make numeric if forced or a qualifier was provided and val looks numeric + if ( extra === "" || extra ) { + num = parseFloat( val ); + return extra === true || isFinite( num ) ? num || 0 : val; + } + + return val; + } +} ); + +jQuery.each( [ "height", "width" ], function( _i, dimension ) { + jQuery.cssHooks[ dimension ] = { + get: function( elem, computed, extra ) { + if ( computed ) { + + // Certain elements can have dimension info if we invisibly show them + // but it must have a current display style that would benefit + return rdisplayswap.test( jQuery.css( elem, "display" ) ) && + + // Support: Safari 8+ + // Table columns in Safari have non-zero offsetWidth & zero + // getBoundingClientRect().width unless display is changed. + // Support: IE <=11 only + // Running getBoundingClientRect on a disconnected node + // in IE throws an error. + ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? + swap( elem, cssShow, function() { + return getWidthOrHeight( elem, dimension, extra ); + } ) : + getWidthOrHeight( elem, dimension, extra ); + } + }, + + set: function( elem, value, extra ) { + var matches, + styles = getStyles( elem ), + + // Only read styles.position if the test has a chance to fail + // to avoid forcing a reflow. + scrollboxSizeBuggy = !support.scrollboxSize() && + styles.position === "absolute", + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) + boxSizingNeeded = scrollboxSizeBuggy || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + subtract = extra ? + boxModelAdjustment( + elem, + dimension, + extra, + isBorderBox, + styles + ) : + 0; + + // Account for unreliable border-box dimensions by comparing offset* to computed and + // faking a content-box to get border and padding (gh-3699) + if ( isBorderBox && scrollboxSizeBuggy ) { + subtract -= Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + parseFloat( styles[ dimension ] ) - + boxModelAdjustment( elem, dimension, "border", false, styles ) - + 0.5 + ); + } + + // Convert to pixels if value adjustment is needed + if ( subtract && ( matches = rcssNum.exec( value ) ) && + ( matches[ 3 ] || "px" ) !== "px" ) { + + elem.style[ dimension ] = value; + value = jQuery.css( elem, dimension ); + } + + return setPositiveNumber( elem, value, subtract ); + } + }; +} ); + +jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, + function( elem, computed ) { + if ( computed ) { + return ( parseFloat( curCSS( elem, "marginLeft" ) ) || + elem.getBoundingClientRect().left - + swap( elem, { marginLeft: 0 }, function() { + return elem.getBoundingClientRect().left; + } ) + ) + "px"; + } + } +); + +// These hooks are used by animate to expand properties +jQuery.each( { + margin: "", + padding: "", + border: "Width" +}, function( prefix, suffix ) { + jQuery.cssHooks[ prefix + suffix ] = { + expand: function( value ) { + var i = 0, + expanded = {}, + + // Assumes a single number if not a string + parts = typeof value === "string" ? value.split( " " ) : [ value ]; + + for ( ; i < 4; i++ ) { + expanded[ prefix + cssExpand[ i ] + suffix ] = + parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; + } + + return expanded; + } + }; + + if ( prefix !== "margin" ) { + jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; + } +} ); + +jQuery.fn.extend( { + css: function( name, value ) { + return access( this, function( elem, name, value ) { + var styles, len, + map = {}, + i = 0; + + if ( Array.isArray( name ) ) { + styles = getStyles( elem ); + len = name.length; + + for ( ; i < len; i++ ) { + map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); + } + + return map; + } + + return value !== undefined ? + jQuery.style( elem, name, value ) : + jQuery.css( elem, name ); + }, name, value, arguments.length > 1 ); + } +} ); + + +function Tween( elem, options, prop, end, easing ) { + return new Tween.prototype.init( elem, options, prop, end, easing ); +} +jQuery.Tween = Tween; + +Tween.prototype = { + constructor: Tween, + init: function( elem, options, prop, end, easing, unit ) { + this.elem = elem; + this.prop = prop; + this.easing = easing || jQuery.easing._default; + this.options = options; + this.start = this.now = this.cur(); + this.end = end; + this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); + }, + cur: function() { + var hooks = Tween.propHooks[ this.prop ]; + + return hooks && hooks.get ? + hooks.get( this ) : + Tween.propHooks._default.get( this ); + }, + run: function( percent ) { + var eased, + hooks = Tween.propHooks[ this.prop ]; + + if ( this.options.duration ) { + this.pos = eased = jQuery.easing[ this.easing ]( + percent, this.options.duration * percent, 0, 1, this.options.duration + ); + } else { + this.pos = eased = percent; + } + this.now = ( this.end - this.start ) * eased + this.start; + + if ( this.options.step ) { + this.options.step.call( this.elem, this.now, this ); + } + + if ( hooks && hooks.set ) { + hooks.set( this ); + } else { + Tween.propHooks._default.set( this ); + } + return this; + } +}; + +Tween.prototype.init.prototype = Tween.prototype; + +Tween.propHooks = { + _default: { + get: function( tween ) { + var result; + + // Use a property on the element directly when it is not a DOM element, + // or when there is no matching style property that exists. + if ( tween.elem.nodeType !== 1 || + tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { + return tween.elem[ tween.prop ]; + } + + // Passing an empty string as a 3rd parameter to .css will automatically + // attempt a parseFloat and fallback to a string if the parse fails. + // Simple values such as "10px" are parsed to Float; + // complex values such as "rotate(1rad)" are returned as-is. + result = jQuery.css( tween.elem, tween.prop, "" ); + + // Empty strings, null, undefined and "auto" are converted to 0. + return !result || result === "auto" ? 0 : result; + }, + set: function( tween ) { + + // Use step hook for back compat. + // Use cssHook if its there. + // Use .style if available and use plain properties where available. + if ( jQuery.fx.step[ tween.prop ] ) { + jQuery.fx.step[ tween.prop ]( tween ); + } else if ( tween.elem.nodeType === 1 && ( + jQuery.cssHooks[ tween.prop ] || + tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { + jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); + } else { + tween.elem[ tween.prop ] = tween.now; + } + } + } +}; + +// Support: IE <=9 only +// Panic based approach to setting things on disconnected nodes +Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { + set: function( tween ) { + if ( tween.elem.nodeType && tween.elem.parentNode ) { + tween.elem[ tween.prop ] = tween.now; + } + } +}; + +jQuery.easing = { + linear: function( p ) { + return p; + }, + swing: function( p ) { + return 0.5 - Math.cos( p * Math.PI ) / 2; + }, + _default: "swing" +}; + +jQuery.fx = Tween.prototype.init; + +// Back compat <1.8 extension point +jQuery.fx.step = {}; + + + + +var + fxNow, inProgress, + rfxtypes = /^(?:toggle|show|hide)$/, + rrun = /queueHooks$/; + +function schedule() { + if ( inProgress ) { + if ( document.hidden === false && window.requestAnimationFrame ) { + window.requestAnimationFrame( schedule ); + } else { + window.setTimeout( schedule, jQuery.fx.interval ); + } + + jQuery.fx.tick(); + } +} + +// Animations created synchronously will run synchronously +function createFxNow() { + window.setTimeout( function() { + fxNow = undefined; + } ); + return ( fxNow = Date.now() ); +} + +// Generate parameters to create a standard animation +function genFx( type, includeWidth ) { + var which, + i = 0, + attrs = { height: type }; + + // If we include width, step value is 1 to do all cssExpand values, + // otherwise step value is 2 to skip over Left and Right + includeWidth = includeWidth ? 1 : 0; + for ( ; i < 4; i += 2 - includeWidth ) { + which = cssExpand[ i ]; + attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; + } + + if ( includeWidth ) { + attrs.opacity = attrs.width = type; + } + + return attrs; +} + +function createTween( value, prop, animation ) { + var tween, + collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), + index = 0, + length = collection.length; + for ( ; index < length; index++ ) { + if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { + + // We're done with this property + return tween; + } + } +} + +function defaultPrefilter( elem, props, opts ) { + var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, + isBox = "width" in props || "height" in props, + anim = this, + orig = {}, + style = elem.style, + hidden = elem.nodeType && isHiddenWithinTree( elem ), + dataShow = dataPriv.get( elem, "fxshow" ); + + // Queue-skipping animations hijack the fx hooks + if ( !opts.queue ) { + hooks = jQuery._queueHooks( elem, "fx" ); + if ( hooks.unqueued == null ) { + hooks.unqueued = 0; + oldfire = hooks.empty.fire; + hooks.empty.fire = function() { + if ( !hooks.unqueued ) { + oldfire(); + } + }; + } + hooks.unqueued++; + + anim.always( function() { + + // Ensure the complete handler is called before this completes + anim.always( function() { + hooks.unqueued--; + if ( !jQuery.queue( elem, "fx" ).length ) { + hooks.empty.fire(); + } + } ); + } ); + } + + // Detect show/hide animations + for ( prop in props ) { + value = props[ prop ]; + if ( rfxtypes.test( value ) ) { + delete props[ prop ]; + toggle = toggle || value === "toggle"; + if ( value === ( hidden ? "hide" : "show" ) ) { + + // Pretend to be hidden if this is a "show" and + // there is still data from a stopped show/hide + if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { + hidden = true; + + // Ignore all other no-op show/hide data + } else { + continue; + } + } + orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); + } + } + + // Bail out if this is a no-op like .hide().hide() + propTween = !jQuery.isEmptyObject( props ); + if ( !propTween && jQuery.isEmptyObject( orig ) ) { + return; + } + + // Restrict "overflow" and "display" styles during box animations + if ( isBox && elem.nodeType === 1 ) { + + // Support: IE <=9 - 11, Edge 12 - 15 + // Record all 3 overflow attributes because IE does not infer the shorthand + // from identically-valued overflowX and overflowY and Edge just mirrors + // the overflowX value there. + opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; + + // Identify a display type, preferring old show/hide data over the CSS cascade + restoreDisplay = dataShow && dataShow.display; + if ( restoreDisplay == null ) { + restoreDisplay = dataPriv.get( elem, "display" ); + } + display = jQuery.css( elem, "display" ); + if ( display === "none" ) { + if ( restoreDisplay ) { + display = restoreDisplay; + } else { + + // Get nonempty value(s) by temporarily forcing visibility + showHide( [ elem ], true ); + restoreDisplay = elem.style.display || restoreDisplay; + display = jQuery.css( elem, "display" ); + showHide( [ elem ] ); + } + } + + // Animate inline elements as inline-block + if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { + if ( jQuery.css( elem, "float" ) === "none" ) { + + // Restore the original display value at the end of pure show/hide animations + if ( !propTween ) { + anim.done( function() { + style.display = restoreDisplay; + } ); + if ( restoreDisplay == null ) { + display = style.display; + restoreDisplay = display === "none" ? "" : display; + } + } + style.display = "inline-block"; + } + } + } + + if ( opts.overflow ) { + style.overflow = "hidden"; + anim.always( function() { + style.overflow = opts.overflow[ 0 ]; + style.overflowX = opts.overflow[ 1 ]; + style.overflowY = opts.overflow[ 2 ]; + } ); + } + + // Implement show/hide animations + propTween = false; + for ( prop in orig ) { + + // General show/hide setup for this element animation + if ( !propTween ) { + if ( dataShow ) { + if ( "hidden" in dataShow ) { + hidden = dataShow.hidden; + } + } else { + dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); + } + + // Store hidden/visible for toggle so `.stop().toggle()` "reverses" + if ( toggle ) { + dataShow.hidden = !hidden; + } + + // Show elements before animating them + if ( hidden ) { + showHide( [ elem ], true ); + } + + /* eslint-disable no-loop-func */ + + anim.done( function() { + + /* eslint-enable no-loop-func */ + + // The final step of a "hide" animation is actually hiding the element + if ( !hidden ) { + showHide( [ elem ] ); + } + dataPriv.remove( elem, "fxshow" ); + for ( prop in orig ) { + jQuery.style( elem, prop, orig[ prop ] ); + } + } ); + } + + // Per-property setup + propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); + if ( !( prop in dataShow ) ) { + dataShow[ prop ] = propTween.start; + if ( hidden ) { + propTween.end = propTween.start; + propTween.start = 0; + } + } + } +} + +function propFilter( props, specialEasing ) { + var index, name, easing, value, hooks; + + // camelCase, specialEasing and expand cssHook pass + for ( index in props ) { + name = camelCase( index ); + easing = specialEasing[ name ]; + value = props[ index ]; + if ( Array.isArray( value ) ) { + easing = value[ 1 ]; + value = props[ index ] = value[ 0 ]; + } + + if ( index !== name ) { + props[ name ] = value; + delete props[ index ]; + } + + hooks = jQuery.cssHooks[ name ]; + if ( hooks && "expand" in hooks ) { + value = hooks.expand( value ); + delete props[ name ]; + + // Not quite $.extend, this won't overwrite existing keys. + // Reusing 'index' because we have the correct "name" + for ( index in value ) { + if ( !( index in props ) ) { + props[ index ] = value[ index ]; + specialEasing[ index ] = easing; + } + } + } else { + specialEasing[ name ] = easing; + } + } +} + +function Animation( elem, properties, options ) { + var result, + stopped, + index = 0, + length = Animation.prefilters.length, + deferred = jQuery.Deferred().always( function() { + + // Don't match elem in the :animated selector + delete tick.elem; + } ), + tick = function() { + if ( stopped ) { + return false; + } + var currentTime = fxNow || createFxNow(), + remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), + + // Support: Android 2.3 only + // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) + temp = remaining / animation.duration || 0, + percent = 1 - temp, + index = 0, + length = animation.tweens.length; + + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( percent ); + } + + deferred.notifyWith( elem, [ animation, percent, remaining ] ); + + // If there's more to do, yield + if ( percent < 1 && length ) { + return remaining; + } + + // If this was an empty animation, synthesize a final progress notification + if ( !length ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + } + + // Resolve the animation and report its conclusion + deferred.resolveWith( elem, [ animation ] ); + return false; + }, + animation = deferred.promise( { + elem: elem, + props: jQuery.extend( {}, properties ), + opts: jQuery.extend( true, { + specialEasing: {}, + easing: jQuery.easing._default + }, options ), + originalProperties: properties, + originalOptions: options, + startTime: fxNow || createFxNow(), + duration: options.duration, + tweens: [], + createTween: function( prop, end ) { + var tween = jQuery.Tween( elem, animation.opts, prop, end, + animation.opts.specialEasing[ prop ] || animation.opts.easing ); + animation.tweens.push( tween ); + return tween; + }, + stop: function( gotoEnd ) { + var index = 0, + + // If we are going to the end, we want to run all the tweens + // otherwise we skip this part + length = gotoEnd ? animation.tweens.length : 0; + if ( stopped ) { + return this; + } + stopped = true; + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( 1 ); + } + + // Resolve when we played the last frame; otherwise, reject + if ( gotoEnd ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + deferred.resolveWith( elem, [ animation, gotoEnd ] ); + } else { + deferred.rejectWith( elem, [ animation, gotoEnd ] ); + } + return this; + } + } ), + props = animation.props; + + propFilter( props, animation.opts.specialEasing ); + + for ( ; index < length; index++ ) { + result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); + if ( result ) { + if ( isFunction( result.stop ) ) { + jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = + result.stop.bind( result ); + } + return result; + } + } + + jQuery.map( props, createTween, animation ); + + if ( isFunction( animation.opts.start ) ) { + animation.opts.start.call( elem, animation ); + } + + // Attach callbacks from options + animation + .progress( animation.opts.progress ) + .done( animation.opts.done, animation.opts.complete ) + .fail( animation.opts.fail ) + .always( animation.opts.always ); + + jQuery.fx.timer( + jQuery.extend( tick, { + elem: elem, + anim: animation, + queue: animation.opts.queue + } ) + ); + + return animation; +} + +jQuery.Animation = jQuery.extend( Animation, { + + tweeners: { + "*": [ function( prop, value ) { + var tween = this.createTween( prop, value ); + adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); + return tween; + } ] + }, + + tweener: function( props, callback ) { + if ( isFunction( props ) ) { + callback = props; + props = [ "*" ]; + } else { + props = props.match( rnothtmlwhite ); + } + + var prop, + index = 0, + length = props.length; + + for ( ; index < length; index++ ) { + prop = props[ index ]; + Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; + Animation.tweeners[ prop ].unshift( callback ); + } + }, + + prefilters: [ defaultPrefilter ], + + prefilter: function( callback, prepend ) { + if ( prepend ) { + Animation.prefilters.unshift( callback ); + } else { + Animation.prefilters.push( callback ); + } + } +} ); + +jQuery.speed = function( speed, easing, fn ) { + var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { + complete: fn || !fn && easing || + isFunction( speed ) && speed, + duration: speed, + easing: fn && easing || easing && !isFunction( easing ) && easing + }; + + // Go to the end state if fx are off + if ( jQuery.fx.off ) { + opt.duration = 0; + + } else { + if ( typeof opt.duration !== "number" ) { + if ( opt.duration in jQuery.fx.speeds ) { + opt.duration = jQuery.fx.speeds[ opt.duration ]; + + } else { + opt.duration = jQuery.fx.speeds._default; + } + } + } + + // Normalize opt.queue - true/undefined/null -> "fx" + if ( opt.queue == null || opt.queue === true ) { + opt.queue = "fx"; + } + + // Queueing + opt.old = opt.complete; + + opt.complete = function() { + if ( isFunction( opt.old ) ) { + opt.old.call( this ); + } + + if ( opt.queue ) { + jQuery.dequeue( this, opt.queue ); + } + }; + + return opt; +}; + +jQuery.fn.extend( { + fadeTo: function( speed, to, easing, callback ) { + + // Show any hidden elements after setting opacity to 0 + return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() + + // Animate to the value specified + .end().animate( { opacity: to }, speed, easing, callback ); + }, + animate: function( prop, speed, easing, callback ) { + var empty = jQuery.isEmptyObject( prop ), + optall = jQuery.speed( speed, easing, callback ), + doAnimation = function() { + + // Operate on a copy of prop so per-property easing won't be lost + var anim = Animation( this, jQuery.extend( {}, prop ), optall ); + + // Empty animations, or finishing resolves immediately + if ( empty || dataPriv.get( this, "finish" ) ) { + anim.stop( true ); + } + }; + + doAnimation.finish = doAnimation; + + return empty || optall.queue === false ? + this.each( doAnimation ) : + this.queue( optall.queue, doAnimation ); + }, + stop: function( type, clearQueue, gotoEnd ) { + var stopQueue = function( hooks ) { + var stop = hooks.stop; + delete hooks.stop; + stop( gotoEnd ); + }; + + if ( typeof type !== "string" ) { + gotoEnd = clearQueue; + clearQueue = type; + type = undefined; + } + if ( clearQueue ) { + this.queue( type || "fx", [] ); + } + + return this.each( function() { + var dequeue = true, + index = type != null && type + "queueHooks", + timers = jQuery.timers, + data = dataPriv.get( this ); + + if ( index ) { + if ( data[ index ] && data[ index ].stop ) { + stopQueue( data[ index ] ); + } + } else { + for ( index in data ) { + if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { + stopQueue( data[ index ] ); + } + } + } + + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && + ( type == null || timers[ index ].queue === type ) ) { + + timers[ index ].anim.stop( gotoEnd ); + dequeue = false; + timers.splice( index, 1 ); + } + } + + // Start the next in the queue if the last step wasn't forced. + // Timers currently will call their complete callbacks, which + // will dequeue but only if they were gotoEnd. + if ( dequeue || !gotoEnd ) { + jQuery.dequeue( this, type ); + } + } ); + }, + finish: function( type ) { + if ( type !== false ) { + type = type || "fx"; + } + return this.each( function() { + var index, + data = dataPriv.get( this ), + queue = data[ type + "queue" ], + hooks = data[ type + "queueHooks" ], + timers = jQuery.timers, + length = queue ? queue.length : 0; + + // Enable finishing flag on private data + data.finish = true; + + // Empty the queue first + jQuery.queue( this, type, [] ); + + if ( hooks && hooks.stop ) { + hooks.stop.call( this, true ); + } + + // Look for any active animations, and finish them + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && timers[ index ].queue === type ) { + timers[ index ].anim.stop( true ); + timers.splice( index, 1 ); + } + } + + // Look for any animations in the old queue and finish them + for ( index = 0; index < length; index++ ) { + if ( queue[ index ] && queue[ index ].finish ) { + queue[ index ].finish.call( this ); + } + } + + // Turn off finishing flag + delete data.finish; + } ); + } +} ); + +jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { + var cssFn = jQuery.fn[ name ]; + jQuery.fn[ name ] = function( speed, easing, callback ) { + return speed == null || typeof speed === "boolean" ? + cssFn.apply( this, arguments ) : + this.animate( genFx( name, true ), speed, easing, callback ); + }; +} ); + +// Generate shortcuts for custom animations +jQuery.each( { + slideDown: genFx( "show" ), + slideUp: genFx( "hide" ), + slideToggle: genFx( "toggle" ), + fadeIn: { opacity: "show" }, + fadeOut: { opacity: "hide" }, + fadeToggle: { opacity: "toggle" } +}, function( name, props ) { + jQuery.fn[ name ] = function( speed, easing, callback ) { + return this.animate( props, speed, easing, callback ); + }; +} ); + +jQuery.timers = []; +jQuery.fx.tick = function() { + var timer, + i = 0, + timers = jQuery.timers; + + fxNow = Date.now(); + + for ( ; i < timers.length; i++ ) { + timer = timers[ i ]; + + // Run the timer and safely remove it when done (allowing for external removal) + if ( !timer() && timers[ i ] === timer ) { + timers.splice( i--, 1 ); + } + } + + if ( !timers.length ) { + jQuery.fx.stop(); + } + fxNow = undefined; +}; + +jQuery.fx.timer = function( timer ) { + jQuery.timers.push( timer ); + jQuery.fx.start(); +}; + +jQuery.fx.interval = 13; +jQuery.fx.start = function() { + if ( inProgress ) { + return; + } + + inProgress = true; + schedule(); +}; + +jQuery.fx.stop = function() { + inProgress = null; +}; + +jQuery.fx.speeds = { + slow: 600, + fast: 200, + + // Default speed + _default: 400 +}; + + +// Based off of the plugin by Clint Helfers, with permission. +// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ +jQuery.fn.delay = function( time, type ) { + time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; + type = type || "fx"; + + return this.queue( type, function( next, hooks ) { + var timeout = window.setTimeout( next, time ); + hooks.stop = function() { + window.clearTimeout( timeout ); + }; + } ); +}; + + +( function() { + var input = document.createElement( "input" ), + select = document.createElement( "select" ), + opt = select.appendChild( document.createElement( "option" ) ); + + input.type = "checkbox"; + + // Support: Android <=4.3 only + // Default value for a checkbox should be "on" + support.checkOn = input.value !== ""; + + // Support: IE <=11 only + // Must access selectedIndex to make default options select + support.optSelected = opt.selected; + + // Support: IE <=11 only + // An input loses its value after becoming a radio + input = document.createElement( "input" ); + input.value = "t"; + input.type = "radio"; + support.radioValue = input.value === "t"; +} )(); + + +var boolHook, + attrHandle = jQuery.expr.attrHandle; + +jQuery.fn.extend( { + attr: function( name, value ) { + return access( this, jQuery.attr, name, value, arguments.length > 1 ); + }, + + removeAttr: function( name ) { + return this.each( function() { + jQuery.removeAttr( this, name ); + } ); + } +} ); + +jQuery.extend( { + attr: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set attributes on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + // Fallback to prop when attributes are not supported + if ( typeof elem.getAttribute === "undefined" ) { + return jQuery.prop( elem, name, value ); + } + + // Attribute hooks are determined by the lowercase version + // Grab necessary hook if one is defined + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + hooks = jQuery.attrHooks[ name.toLowerCase() ] || + ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); + } + + if ( value !== undefined ) { + if ( value === null ) { + jQuery.removeAttr( elem, name ); + return; + } + + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + elem.setAttribute( name, value + "" ); + return value; + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + ret = jQuery.find.attr( elem, name ); + + // Non-existent attributes return null, we normalize to undefined + return ret == null ? undefined : ret; + }, + + attrHooks: { + type: { + set: function( elem, value ) { + if ( !support.radioValue && value === "radio" && + nodeName( elem, "input" ) ) { + var val = elem.value; + elem.setAttribute( "type", value ); + if ( val ) { + elem.value = val; + } + return value; + } + } + } + }, + + removeAttr: function( elem, value ) { + var name, + i = 0, + + // Attribute names can contain non-HTML whitespace characters + // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 + attrNames = value && value.match( rnothtmlwhite ); + + if ( attrNames && elem.nodeType === 1 ) { + while ( ( name = attrNames[ i++ ] ) ) { + elem.removeAttribute( name ); + } + } + } +} ); + +// Hooks for boolean attributes +boolHook = { + set: function( elem, value, name ) { + if ( value === false ) { + + // Remove boolean attributes when set to false + jQuery.removeAttr( elem, name ); + } else { + elem.setAttribute( name, name ); + } + return name; + } +}; + +jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { + var getter = attrHandle[ name ] || jQuery.find.attr; + + attrHandle[ name ] = function( elem, name, isXML ) { + var ret, handle, + lowercaseName = name.toLowerCase(); + + if ( !isXML ) { + + // Avoid an infinite loop by temporarily removing this function from the getter + handle = attrHandle[ lowercaseName ]; + attrHandle[ lowercaseName ] = ret; + ret = getter( elem, name, isXML ) != null ? + lowercaseName : + null; + attrHandle[ lowercaseName ] = handle; + } + return ret; + }; +} ); + + + + +var rfocusable = /^(?:input|select|textarea|button)$/i, + rclickable = /^(?:a|area)$/i; + +jQuery.fn.extend( { + prop: function( name, value ) { + return access( this, jQuery.prop, name, value, arguments.length > 1 ); + }, + + removeProp: function( name ) { + return this.each( function() { + delete this[ jQuery.propFix[ name ] || name ]; + } ); + } +} ); + +jQuery.extend( { + prop: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set properties on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + + // Fix name and attach hooks + name = jQuery.propFix[ name ] || name; + hooks = jQuery.propHooks[ name ]; + } + + if ( value !== undefined ) { + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + return ( elem[ name ] = value ); + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + return elem[ name ]; + }, + + propHooks: { + tabIndex: { + get: function( elem ) { + + // Support: IE <=9 - 11 only + // elem.tabIndex doesn't always return the + // correct value when it hasn't been explicitly set + // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ + // Use proper attribute retrieval(#12072) + var tabindex = jQuery.find.attr( elem, "tabindex" ); + + if ( tabindex ) { + return parseInt( tabindex, 10 ); + } + + if ( + rfocusable.test( elem.nodeName ) || + rclickable.test( elem.nodeName ) && + elem.href + ) { + return 0; + } + + return -1; + } + } + }, + + propFix: { + "for": "htmlFor", + "class": "className" + } +} ); + +// Support: IE <=11 only +// Accessing the selectedIndex property +// forces the browser to respect setting selected +// on the option +// The getter ensures a default option is selected +// when in an optgroup +// eslint rule "no-unused-expressions" is disabled for this code +// since it considers such accessions noop +if ( !support.optSelected ) { + jQuery.propHooks.selected = { + get: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent && parent.parentNode ) { + parent.parentNode.selectedIndex; + } + return null; + }, + set: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent ) { + parent.selectedIndex; + + if ( parent.parentNode ) { + parent.parentNode.selectedIndex; + } + } + } + }; +} + +jQuery.each( [ + "tabIndex", + "readOnly", + "maxLength", + "cellSpacing", + "cellPadding", + "rowSpan", + "colSpan", + "useMap", + "frameBorder", + "contentEditable" +], function() { + jQuery.propFix[ this.toLowerCase() ] = this; +} ); + + + + + // Strip and collapse whitespace according to HTML spec + // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace + function stripAndCollapse( value ) { + var tokens = value.match( rnothtmlwhite ) || []; + return tokens.join( " " ); + } + + +function getClass( elem ) { + return elem.getAttribute && elem.getAttribute( "class" ) || ""; +} + +function classesToArray( value ) { + if ( Array.isArray( value ) ) { + return value; + } + if ( typeof value === "string" ) { + return value.match( rnothtmlwhite ) || []; + } + return []; +} + +jQuery.fn.extend( { + addClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + if ( cur.indexOf( " " + clazz + " " ) < 0 ) { + cur += clazz + " "; + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + removeClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + if ( !arguments.length ) { + return this.attr( "class", "" ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + + // This expression is here for better compressibility (see addClass) + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + + // Remove *all* instances + while ( cur.indexOf( " " + clazz + " " ) > -1 ) { + cur = cur.replace( " " + clazz + " ", " " ); + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + toggleClass: function( value, stateVal ) { + var type = typeof value, + isValidValue = type === "string" || Array.isArray( value ); + + if ( typeof stateVal === "boolean" && isValidValue ) { + return stateVal ? this.addClass( value ) : this.removeClass( value ); + } + + if ( isFunction( value ) ) { + return this.each( function( i ) { + jQuery( this ).toggleClass( + value.call( this, i, getClass( this ), stateVal ), + stateVal + ); + } ); + } + + return this.each( function() { + var className, i, self, classNames; + + if ( isValidValue ) { + + // Toggle individual class names + i = 0; + self = jQuery( this ); + classNames = classesToArray( value ); + + while ( ( className = classNames[ i++ ] ) ) { + + // Check each className given, space separated list + if ( self.hasClass( className ) ) { + self.removeClass( className ); + } else { + self.addClass( className ); + } + } + + // Toggle whole class name + } else if ( value === undefined || type === "boolean" ) { + className = getClass( this ); + if ( className ) { + + // Store className if set + dataPriv.set( this, "__className__", className ); + } + + // If the element has a class name or if we're passed `false`, + // then remove the whole classname (if there was one, the above saved it). + // Otherwise bring back whatever was previously saved (if anything), + // falling back to the empty string if nothing was stored. + if ( this.setAttribute ) { + this.setAttribute( "class", + className || value === false ? + "" : + dataPriv.get( this, "__className__" ) || "" + ); + } + } + } ); + }, + + hasClass: function( selector ) { + var className, elem, + i = 0; + + className = " " + selector + " "; + while ( ( elem = this[ i++ ] ) ) { + if ( elem.nodeType === 1 && + ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { + return true; + } + } + + return false; + } +} ); + + + + +var rreturn = /\r/g; + +jQuery.fn.extend( { + val: function( value ) { + var hooks, ret, valueIsFunction, + elem = this[ 0 ]; + + if ( !arguments.length ) { + if ( elem ) { + hooks = jQuery.valHooks[ elem.type ] || + jQuery.valHooks[ elem.nodeName.toLowerCase() ]; + + if ( hooks && + "get" in hooks && + ( ret = hooks.get( elem, "value" ) ) !== undefined + ) { + return ret; + } + + ret = elem.value; + + // Handle most common string cases + if ( typeof ret === "string" ) { + return ret.replace( rreturn, "" ); + } + + // Handle cases where value is null/undef or number + return ret == null ? "" : ret; + } + + return; + } + + valueIsFunction = isFunction( value ); + + return this.each( function( i ) { + var val; + + if ( this.nodeType !== 1 ) { + return; + } + + if ( valueIsFunction ) { + val = value.call( this, i, jQuery( this ).val() ); + } else { + val = value; + } + + // Treat null/undefined as ""; convert numbers to string + if ( val == null ) { + val = ""; + + } else if ( typeof val === "number" ) { + val += ""; + + } else if ( Array.isArray( val ) ) { + val = jQuery.map( val, function( value ) { + return value == null ? "" : value + ""; + } ); + } + + hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; + + // If set returns undefined, fall back to normal setting + if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { + this.value = val; + } + } ); + } +} ); + +jQuery.extend( { + valHooks: { + option: { + get: function( elem ) { + + var val = jQuery.find.attr( elem, "value" ); + return val != null ? + val : + + // Support: IE <=10 - 11 only + // option.text throws exceptions (#14686, #14858) + // Strip and collapse whitespace + // https://html.spec.whatwg.org/#strip-and-collapse-whitespace + stripAndCollapse( jQuery.text( elem ) ); + } + }, + select: { + get: function( elem ) { + var value, option, i, + options = elem.options, + index = elem.selectedIndex, + one = elem.type === "select-one", + values = one ? null : [], + max = one ? index + 1 : options.length; + + if ( index < 0 ) { + i = max; + + } else { + i = one ? index : 0; + } + + // Loop through all the selected options + for ( ; i < max; i++ ) { + option = options[ i ]; + + // Support: IE <=9 only + // IE8-9 doesn't update selected after form reset (#2551) + if ( ( option.selected || i === index ) && + + // Don't return options that are disabled or in a disabled optgroup + !option.disabled && + ( !option.parentNode.disabled || + !nodeName( option.parentNode, "optgroup" ) ) ) { + + // Get the specific value for the option + value = jQuery( option ).val(); + + // We don't need an array for one selects + if ( one ) { + return value; + } + + // Multi-Selects return an array + values.push( value ); + } + } + + return values; + }, + + set: function( elem, value ) { + var optionSet, option, + options = elem.options, + values = jQuery.makeArray( value ), + i = options.length; + + while ( i-- ) { + option = options[ i ]; + + /* eslint-disable no-cond-assign */ + + if ( option.selected = + jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 + ) { + optionSet = true; + } + + /* eslint-enable no-cond-assign */ + } + + // Force browsers to behave consistently when non-matching value is set + if ( !optionSet ) { + elem.selectedIndex = -1; + } + return values; + } + } + } +} ); + +// Radios and checkboxes getter/setter +jQuery.each( [ "radio", "checkbox" ], function() { + jQuery.valHooks[ this ] = { + set: function( elem, value ) { + if ( Array.isArray( value ) ) { + return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); + } + } + }; + if ( !support.checkOn ) { + jQuery.valHooks[ this ].get = function( elem ) { + return elem.getAttribute( "value" ) === null ? "on" : elem.value; + }; + } +} ); + + + + +// Return jQuery for attributes-only inclusion + + +support.focusin = "onfocusin" in window; + + +var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, + stopPropagationCallback = function( e ) { + e.stopPropagation(); + }; + +jQuery.extend( jQuery.event, { + + trigger: function( event, data, elem, onlyHandlers ) { + + var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, + eventPath = [ elem || document ], + type = hasOwn.call( event, "type" ) ? event.type : event, + namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; + + cur = lastElement = tmp = elem = elem || document; + + // Don't do events on text and comment nodes + if ( elem.nodeType === 3 || elem.nodeType === 8 ) { + return; + } + + // focus/blur morphs to focusin/out; ensure we're not firing them right now + if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { + return; + } + + if ( type.indexOf( "." ) > -1 ) { + + // Namespaced trigger; create a regexp to match event type in handle() + namespaces = type.split( "." ); + type = namespaces.shift(); + namespaces.sort(); + } + ontype = type.indexOf( ":" ) < 0 && "on" + type; + + // Caller can pass in a jQuery.Event object, Object, or just an event type string + event = event[ jQuery.expando ] ? + event : + new jQuery.Event( type, typeof event === "object" && event ); + + // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) + event.isTrigger = onlyHandlers ? 2 : 3; + event.namespace = namespaces.join( "." ); + event.rnamespace = event.namespace ? + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : + null; + + // Clean up the event in case it is being reused + event.result = undefined; + if ( !event.target ) { + event.target = elem; + } + + // Clone any incoming data and prepend the event, creating the handler arg list + data = data == null ? + [ event ] : + jQuery.makeArray( data, [ event ] ); + + // Allow special events to draw outside the lines + special = jQuery.event.special[ type ] || {}; + if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { + return; + } + + // Determine event propagation path in advance, per W3C events spec (#9951) + // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) + if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { + + bubbleType = special.delegateType || type; + if ( !rfocusMorph.test( bubbleType + type ) ) { + cur = cur.parentNode; + } + for ( ; cur; cur = cur.parentNode ) { + eventPath.push( cur ); + tmp = cur; + } + + // Only add window if we got to document (e.g., not plain obj or detached DOM) + if ( tmp === ( elem.ownerDocument || document ) ) { + eventPath.push( tmp.defaultView || tmp.parentWindow || window ); + } + } + + // Fire handlers on the event path + i = 0; + while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { + lastElement = cur; + event.type = i > 1 ? + bubbleType : + special.bindType || type; + + // jQuery handler + handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] && + dataPriv.get( cur, "handle" ); + if ( handle ) { + handle.apply( cur, data ); + } + + // Native handler + handle = ontype && cur[ ontype ]; + if ( handle && handle.apply && acceptData( cur ) ) { + event.result = handle.apply( cur, data ); + if ( event.result === false ) { + event.preventDefault(); + } + } + } + event.type = type; + + // If nobody prevented the default action, do it now + if ( !onlyHandlers && !event.isDefaultPrevented() ) { + + if ( ( !special._default || + special._default.apply( eventPath.pop(), data ) === false ) && + acceptData( elem ) ) { + + // Call a native DOM method on the target with the same name as the event. + // Don't do default actions on window, that's where global variables be (#6170) + if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { + + // Don't re-trigger an onFOO event when we call its FOO() method + tmp = elem[ ontype ]; + + if ( tmp ) { + elem[ ontype ] = null; + } + + // Prevent re-triggering of the same event, since we already bubbled it above + jQuery.event.triggered = type; + + if ( event.isPropagationStopped() ) { + lastElement.addEventListener( type, stopPropagationCallback ); + } + + elem[ type ](); + + if ( event.isPropagationStopped() ) { + lastElement.removeEventListener( type, stopPropagationCallback ); + } + + jQuery.event.triggered = undefined; + + if ( tmp ) { + elem[ ontype ] = tmp; + } + } + } + } + + return event.result; + }, + + // Piggyback on a donor event to simulate a different one + // Used only for `focus(in | out)` events + simulate: function( type, elem, event ) { + var e = jQuery.extend( + new jQuery.Event(), + event, + { + type: type, + isSimulated: true + } + ); + + jQuery.event.trigger( e, null, elem ); + } + +} ); + +jQuery.fn.extend( { + + trigger: function( type, data ) { + return this.each( function() { + jQuery.event.trigger( type, data, this ); + } ); + }, + triggerHandler: function( type, data ) { + var elem = this[ 0 ]; + if ( elem ) { + return jQuery.event.trigger( type, data, elem, true ); + } + } +} ); + + +// Support: Firefox <=44 +// Firefox doesn't have focus(in | out) events +// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 +// +// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 +// focus(in | out) events fire after focus & blur events, +// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order +// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 +if ( !support.focusin ) { + jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { + + // Attach a single capturing handler on the document while someone wants focusin/focusout + var handler = function( event ) { + jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); + }; + + jQuery.event.special[ fix ] = { + setup: function() { + + // Handle: regular nodes (via `this.ownerDocument`), window + // (via `this.document`) & document (via `this`). + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ); + + if ( !attaches ) { + doc.addEventListener( orig, handler, true ); + } + dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); + }, + teardown: function() { + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ) - 1; + + if ( !attaches ) { + doc.removeEventListener( orig, handler, true ); + dataPriv.remove( doc, fix ); + + } else { + dataPriv.access( doc, fix, attaches ); + } + } + }; + } ); +} +var location = window.location; + +var nonce = { guid: Date.now() }; + +var rquery = ( /\?/ ); + + + +// Cross-browser xml parsing +jQuery.parseXML = function( data ) { + var xml, parserErrorElem; + if ( !data || typeof data !== "string" ) { + return null; + } + + // Support: IE 9 - 11 only + // IE throws on parseFromString with invalid input. + try { + xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); + } catch ( e ) {} + + parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ]; + if ( !xml || parserErrorElem ) { + jQuery.error( "Invalid XML: " + ( + parserErrorElem ? + jQuery.map( parserErrorElem.childNodes, function( el ) { + return el.textContent; + } ).join( "\n" ) : + data + ) ); + } + return xml; +}; + + +var + rbracket = /\[\]$/, + rCRLF = /\r?\n/g, + rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, + rsubmittable = /^(?:input|select|textarea|keygen)/i; + +function buildParams( prefix, obj, traditional, add ) { + var name; + + if ( Array.isArray( obj ) ) { + + // Serialize array item. + jQuery.each( obj, function( i, v ) { + if ( traditional || rbracket.test( prefix ) ) { + + // Treat each array item as a scalar. + add( prefix, v ); + + } else { + + // Item is non-scalar (array or object), encode its numeric index. + buildParams( + prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", + v, + traditional, + add + ); + } + } ); + + } else if ( !traditional && toType( obj ) === "object" ) { + + // Serialize object item. + for ( name in obj ) { + buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); + } + + } else { + + // Serialize scalar item. + add( prefix, obj ); + } +} + +// Serialize an array of form elements or a set of +// key/values into a query string +jQuery.param = function( a, traditional ) { + var prefix, + s = [], + add = function( key, valueOrFunction ) { + + // If value is a function, invoke it and use its return value + var value = isFunction( valueOrFunction ) ? + valueOrFunction() : + valueOrFunction; + + s[ s.length ] = encodeURIComponent( key ) + "=" + + encodeURIComponent( value == null ? "" : value ); + }; + + if ( a == null ) { + return ""; + } + + // If an array was passed in, assume that it is an array of form elements. + if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { + + // Serialize the form elements + jQuery.each( a, function() { + add( this.name, this.value ); + } ); + + } else { + + // If traditional, encode the "old" way (the way 1.3.2 or older + // did it), otherwise encode params recursively. + for ( prefix in a ) { + buildParams( prefix, a[ prefix ], traditional, add ); + } + } + + // Return the resulting serialization + return s.join( "&" ); +}; + +jQuery.fn.extend( { + serialize: function() { + return jQuery.param( this.serializeArray() ); + }, + serializeArray: function() { + return this.map( function() { + + // Can add propHook for "elements" to filter or add form elements + var elements = jQuery.prop( this, "elements" ); + return elements ? jQuery.makeArray( elements ) : this; + } ).filter( function() { + var type = this.type; + + // Use .is( ":disabled" ) so that fieldset[disabled] works + return this.name && !jQuery( this ).is( ":disabled" ) && + rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && + ( this.checked || !rcheckableType.test( type ) ); + } ).map( function( _i, elem ) { + var val = jQuery( this ).val(); + + if ( val == null ) { + return null; + } + + if ( Array.isArray( val ) ) { + return jQuery.map( val, function( val ) { + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ); + } + + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ).get(); + } +} ); + + +var + r20 = /%20/g, + rhash = /#.*$/, + rantiCache = /([?&])_=[^&]*/, + rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, + + // #7653, #8125, #8152: local protocol detection + rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, + rnoContent = /^(?:GET|HEAD)$/, + rprotocol = /^\/\//, + + /* Prefilters + * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) + * 2) These are called: + * - BEFORE asking for a transport + * - AFTER param serialization (s.data is a string if s.processData is true) + * 3) key is the dataType + * 4) the catchall symbol "*" can be used + * 5) execution will start with transport dataType and THEN continue down to "*" if needed + */ + prefilters = {}, + + /* Transports bindings + * 1) key is the dataType + * 2) the catchall symbol "*" can be used + * 3) selection will start with transport dataType and THEN go to "*" if needed + */ + transports = {}, + + // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression + allTypes = "*/".concat( "*" ), + + // Anchor tag for parsing the document origin + originAnchor = document.createElement( "a" ); + +originAnchor.href = location.href; + +// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport +function addToPrefiltersOrTransports( structure ) { + + // dataTypeExpression is optional and defaults to "*" + return function( dataTypeExpression, func ) { + + if ( typeof dataTypeExpression !== "string" ) { + func = dataTypeExpression; + dataTypeExpression = "*"; + } + + var dataType, + i = 0, + dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; + + if ( isFunction( func ) ) { + + // For each dataType in the dataTypeExpression + while ( ( dataType = dataTypes[ i++ ] ) ) { + + // Prepend if requested + if ( dataType[ 0 ] === "+" ) { + dataType = dataType.slice( 1 ) || "*"; + ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); + + // Otherwise append + } else { + ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); + } + } + } + }; +} + +// Base inspection function for prefilters and transports +function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { + + var inspected = {}, + seekingTransport = ( structure === transports ); + + function inspect( dataType ) { + var selected; + inspected[ dataType ] = true; + jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { + var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); + if ( typeof dataTypeOrTransport === "string" && + !seekingTransport && !inspected[ dataTypeOrTransport ] ) { + + options.dataTypes.unshift( dataTypeOrTransport ); + inspect( dataTypeOrTransport ); + return false; + } else if ( seekingTransport ) { + return !( selected = dataTypeOrTransport ); + } + } ); + return selected; + } + + return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); +} + +// A special extend for ajax options +// that takes "flat" options (not to be deep extended) +// Fixes #9887 +function ajaxExtend( target, src ) { + var key, deep, + flatOptions = jQuery.ajaxSettings.flatOptions || {}; + + for ( key in src ) { + if ( src[ key ] !== undefined ) { + ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; + } + } + if ( deep ) { + jQuery.extend( true, target, deep ); + } + + return target; +} + +/* Handles responses to an ajax request: + * - finds the right dataType (mediates between content-type and expected dataType) + * - returns the corresponding response + */ +function ajaxHandleResponses( s, jqXHR, responses ) { + + var ct, type, finalDataType, firstDataType, + contents = s.contents, + dataTypes = s.dataTypes; + + // Remove auto dataType and get content-type in the process + while ( dataTypes[ 0 ] === "*" ) { + dataTypes.shift(); + if ( ct === undefined ) { + ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); + } + } + + // Check if we're dealing with a known content-type + if ( ct ) { + for ( type in contents ) { + if ( contents[ type ] && contents[ type ].test( ct ) ) { + dataTypes.unshift( type ); + break; + } + } + } + + // Check to see if we have a response for the expected dataType + if ( dataTypes[ 0 ] in responses ) { + finalDataType = dataTypes[ 0 ]; + } else { + + // Try convertible dataTypes + for ( type in responses ) { + if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { + finalDataType = type; + break; + } + if ( !firstDataType ) { + firstDataType = type; + } + } + + // Or just use first one + finalDataType = finalDataType || firstDataType; + } + + // If we found a dataType + // We add the dataType to the list if needed + // and return the corresponding response + if ( finalDataType ) { + if ( finalDataType !== dataTypes[ 0 ] ) { + dataTypes.unshift( finalDataType ); + } + return responses[ finalDataType ]; + } +} + +/* Chain conversions given the request and the original response + * Also sets the responseXXX fields on the jqXHR instance + */ +function ajaxConvert( s, response, jqXHR, isSuccess ) { + var conv2, current, conv, tmp, prev, + converters = {}, + + // Work with a copy of dataTypes in case we need to modify it for conversion + dataTypes = s.dataTypes.slice(); + + // Create converters map with lowercased keys + if ( dataTypes[ 1 ] ) { + for ( conv in s.converters ) { + converters[ conv.toLowerCase() ] = s.converters[ conv ]; + } + } + + current = dataTypes.shift(); + + // Convert to each sequential dataType + while ( current ) { + + if ( s.responseFields[ current ] ) { + jqXHR[ s.responseFields[ current ] ] = response; + } + + // Apply the dataFilter if provided + if ( !prev && isSuccess && s.dataFilter ) { + response = s.dataFilter( response, s.dataType ); + } + + prev = current; + current = dataTypes.shift(); + + if ( current ) { + + // There's only work to do if current dataType is non-auto + if ( current === "*" ) { + + current = prev; + + // Convert response if prev dataType is non-auto and differs from current + } else if ( prev !== "*" && prev !== current ) { + + // Seek a direct converter + conv = converters[ prev + " " + current ] || converters[ "* " + current ]; + + // If none found, seek a pair + if ( !conv ) { + for ( conv2 in converters ) { + + // If conv2 outputs current + tmp = conv2.split( " " ); + if ( tmp[ 1 ] === current ) { + + // If prev can be converted to accepted input + conv = converters[ prev + " " + tmp[ 0 ] ] || + converters[ "* " + tmp[ 0 ] ]; + if ( conv ) { + + // Condense equivalence converters + if ( conv === true ) { + conv = converters[ conv2 ]; + + // Otherwise, insert the intermediate dataType + } else if ( converters[ conv2 ] !== true ) { + current = tmp[ 0 ]; + dataTypes.unshift( tmp[ 1 ] ); + } + break; + } + } + } + } + + // Apply converter (if not an equivalence) + if ( conv !== true ) { + + // Unless errors are allowed to bubble, catch and return them + if ( conv && s.throws ) { + response = conv( response ); + } else { + try { + response = conv( response ); + } catch ( e ) { + return { + state: "parsererror", + error: conv ? e : "No conversion from " + prev + " to " + current + }; + } + } + } + } + } + } + + return { state: "success", data: response }; +} + +jQuery.extend( { + + // Counter for holding the number of active queries + active: 0, + + // Last-Modified header cache for next request + lastModified: {}, + etag: {}, + + ajaxSettings: { + url: location.href, + type: "GET", + isLocal: rlocalProtocol.test( location.protocol ), + global: true, + processData: true, + async: true, + contentType: "application/x-www-form-urlencoded; charset=UTF-8", + + /* + timeout: 0, + data: null, + dataType: null, + username: null, + password: null, + cache: null, + throws: false, + traditional: false, + headers: {}, + */ + + accepts: { + "*": allTypes, + text: "text/plain", + html: "text/html", + xml: "application/xml, text/xml", + json: "application/json, text/javascript" + }, + + contents: { + xml: /\bxml\b/, + html: /\bhtml/, + json: /\bjson\b/ + }, + + responseFields: { + xml: "responseXML", + text: "responseText", + json: "responseJSON" + }, + + // Data converters + // Keys separate source (or catchall "*") and destination types with a single space + converters: { + + // Convert anything to text + "* text": String, + + // Text to html (true = no transformation) + "text html": true, + + // Evaluate text as a json expression + "text json": JSON.parse, + + // Parse text as xml + "text xml": jQuery.parseXML + }, + + // For options that shouldn't be deep extended: + // you can add your own custom options here if + // and when you create one that shouldn't be + // deep extended (see ajaxExtend) + flatOptions: { + url: true, + context: true + } + }, + + // Creates a full fledged settings object into target + // with both ajaxSettings and settings fields. + // If target is omitted, writes into ajaxSettings. + ajaxSetup: function( target, settings ) { + return settings ? + + // Building a settings object + ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : + + // Extending ajaxSettings + ajaxExtend( jQuery.ajaxSettings, target ); + }, + + ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), + ajaxTransport: addToPrefiltersOrTransports( transports ), + + // Main method + ajax: function( url, options ) { + + // If url is an object, simulate pre-1.5 signature + if ( typeof url === "object" ) { + options = url; + url = undefined; + } + + // Force options to be an object + options = options || {}; + + var transport, + + // URL without anti-cache param + cacheURL, + + // Response headers + responseHeadersString, + responseHeaders, + + // timeout handle + timeoutTimer, + + // Url cleanup var + urlAnchor, + + // Request state (becomes false upon send and true upon completion) + completed, + + // To know if global events are to be dispatched + fireGlobals, + + // Loop variable + i, + + // uncached part of the url + uncached, + + // Create the final options object + s = jQuery.ajaxSetup( {}, options ), + + // Callbacks context + callbackContext = s.context || s, + + // Context for global events is callbackContext if it is a DOM node or jQuery collection + globalEventContext = s.context && + ( callbackContext.nodeType || callbackContext.jquery ) ? + jQuery( callbackContext ) : + jQuery.event, + + // Deferreds + deferred = jQuery.Deferred(), + completeDeferred = jQuery.Callbacks( "once memory" ), + + // Status-dependent callbacks + statusCode = s.statusCode || {}, + + // Headers (they are sent all at once) + requestHeaders = {}, + requestHeadersNames = {}, + + // Default abort message + strAbort = "canceled", + + // Fake xhr + jqXHR = { + readyState: 0, + + // Builds headers hashtable if needed + getResponseHeader: function( key ) { + var match; + if ( completed ) { + if ( !responseHeaders ) { + responseHeaders = {}; + while ( ( match = rheaders.exec( responseHeadersString ) ) ) { + responseHeaders[ match[ 1 ].toLowerCase() + " " ] = + ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) + .concat( match[ 2 ] ); + } + } + match = responseHeaders[ key.toLowerCase() + " " ]; + } + return match == null ? null : match.join( ", " ); + }, + + // Raw string + getAllResponseHeaders: function() { + return completed ? responseHeadersString : null; + }, + + // Caches the header + setRequestHeader: function( name, value ) { + if ( completed == null ) { + name = requestHeadersNames[ name.toLowerCase() ] = + requestHeadersNames[ name.toLowerCase() ] || name; + requestHeaders[ name ] = value; + } + return this; + }, + + // Overrides response content-type header + overrideMimeType: function( type ) { + if ( completed == null ) { + s.mimeType = type; + } + return this; + }, + + // Status-dependent callbacks + statusCode: function( map ) { + var code; + if ( map ) { + if ( completed ) { + + // Execute the appropriate callbacks + jqXHR.always( map[ jqXHR.status ] ); + } else { + + // Lazy-add the new callbacks in a way that preserves old ones + for ( code in map ) { + statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; + } + } + } + return this; + }, + + // Cancel the request + abort: function( statusText ) { + var finalText = statusText || strAbort; + if ( transport ) { + transport.abort( finalText ); + } + done( 0, finalText ); + return this; + } + }; + + // Attach deferreds + deferred.promise( jqXHR ); + + // Add protocol if not provided (prefilters might expect it) + // Handle falsy url in the settings object (#10093: consistency with old signature) + // We also use the url parameter if available + s.url = ( ( url || s.url || location.href ) + "" ) + .replace( rprotocol, location.protocol + "//" ); + + // Alias method option to type as per ticket #12004 + s.type = options.method || options.type || s.method || s.type; + + // Extract dataTypes list + s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; + + // A cross-domain request is in order when the origin doesn't match the current origin. + if ( s.crossDomain == null ) { + urlAnchor = document.createElement( "a" ); + + // Support: IE <=8 - 11, Edge 12 - 15 + // IE throws exception on accessing the href property if url is malformed, + // e.g. http://example.com:80x/ + try { + urlAnchor.href = s.url; + + // Support: IE <=8 - 11 only + // Anchor's host property isn't correctly set when s.url is relative + urlAnchor.href = urlAnchor.href; + s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== + urlAnchor.protocol + "//" + urlAnchor.host; + } catch ( e ) { + + // If there is an error parsing the URL, assume it is crossDomain, + // it can be rejected by the transport if it is invalid + s.crossDomain = true; + } + } + + // Convert data if not already a string + if ( s.data && s.processData && typeof s.data !== "string" ) { + s.data = jQuery.param( s.data, s.traditional ); + } + + // Apply prefilters + inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); + + // If request was aborted inside a prefilter, stop there + if ( completed ) { + return jqXHR; + } + + // We can fire global events as of now if asked to + // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) + fireGlobals = jQuery.event && s.global; + + // Watch for a new set of requests + if ( fireGlobals && jQuery.active++ === 0 ) { + jQuery.event.trigger( "ajaxStart" ); + } + + // Uppercase the type + s.type = s.type.toUpperCase(); + + // Determine if request has content + s.hasContent = !rnoContent.test( s.type ); + + // Save the URL in case we're toying with the If-Modified-Since + // and/or If-None-Match header later on + // Remove hash to simplify url manipulation + cacheURL = s.url.replace( rhash, "" ); + + // More options handling for requests with no content + if ( !s.hasContent ) { + + // Remember the hash so we can put it back + uncached = s.url.slice( cacheURL.length ); + + // If data is available and should be processed, append data to url + if ( s.data && ( s.processData || typeof s.data === "string" ) ) { + cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; + + // #9682: remove data so that it's not used in an eventual retry + delete s.data; + } + + // Add or update anti-cache param if needed + if ( s.cache === false ) { + cacheURL = cacheURL.replace( rantiCache, "$1" ); + uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + + uncached; + } + + // Put hash and anti-cache on the URL that will be requested (gh-1732) + s.url = cacheURL + uncached; + + // Change '%20' to '+' if this is encoded form body content (gh-2658) + } else if ( s.data && s.processData && + ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { + s.data = s.data.replace( r20, "+" ); + } + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + if ( jQuery.lastModified[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); + } + if ( jQuery.etag[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); + } + } + + // Set the correct header, if data is being sent + if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { + jqXHR.setRequestHeader( "Content-Type", s.contentType ); + } + + // Set the Accepts header for the server, depending on the dataType + jqXHR.setRequestHeader( + "Accept", + s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? + s.accepts[ s.dataTypes[ 0 ] ] + + ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : + s.accepts[ "*" ] + ); + + // Check for headers option + for ( i in s.headers ) { + jqXHR.setRequestHeader( i, s.headers[ i ] ); + } + + // Allow custom headers/mimetypes and early abort + if ( s.beforeSend && + ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { + + // Abort if not done already and return + return jqXHR.abort(); + } + + // Aborting is no longer a cancellation + strAbort = "abort"; + + // Install callbacks on deferreds + completeDeferred.add( s.complete ); + jqXHR.done( s.success ); + jqXHR.fail( s.error ); + + // Get transport + transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); + + // If no transport, we auto-abort + if ( !transport ) { + done( -1, "No Transport" ); + } else { + jqXHR.readyState = 1; + + // Send global event + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); + } + + // If request was aborted inside ajaxSend, stop there + if ( completed ) { + return jqXHR; + } + + // Timeout + if ( s.async && s.timeout > 0 ) { + timeoutTimer = window.setTimeout( function() { + jqXHR.abort( "timeout" ); + }, s.timeout ); + } + + try { + completed = false; + transport.send( requestHeaders, done ); + } catch ( e ) { + + // Rethrow post-completion exceptions + if ( completed ) { + throw e; + } + + // Propagate others as results + done( -1, e ); + } + } + + // Callback for when everything is done + function done( status, nativeStatusText, responses, headers ) { + var isSuccess, success, error, response, modified, + statusText = nativeStatusText; + + // Ignore repeat invocations + if ( completed ) { + return; + } + + completed = true; + + // Clear timeout if it exists + if ( timeoutTimer ) { + window.clearTimeout( timeoutTimer ); + } + + // Dereference transport for early garbage collection + // (no matter how long the jqXHR object will be used) + transport = undefined; + + // Cache response headers + responseHeadersString = headers || ""; + + // Set readyState + jqXHR.readyState = status > 0 ? 4 : 0; + + // Determine if successful + isSuccess = status >= 200 && status < 300 || status === 304; + + // Get response data + if ( responses ) { + response = ajaxHandleResponses( s, jqXHR, responses ); + } + + // Use a noop converter for missing script but not if jsonp + if ( !isSuccess && + jQuery.inArray( "script", s.dataTypes ) > -1 && + jQuery.inArray( "json", s.dataTypes ) < 0 ) { + s.converters[ "text script" ] = function() {}; + } + + // Convert no matter what (that way responseXXX fields are always set) + response = ajaxConvert( s, response, jqXHR, isSuccess ); + + // If successful, handle type chaining + if ( isSuccess ) { + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + modified = jqXHR.getResponseHeader( "Last-Modified" ); + if ( modified ) { + jQuery.lastModified[ cacheURL ] = modified; + } + modified = jqXHR.getResponseHeader( "etag" ); + if ( modified ) { + jQuery.etag[ cacheURL ] = modified; + } + } + + // if no content + if ( status === 204 || s.type === "HEAD" ) { + statusText = "nocontent"; + + // if not modified + } else if ( status === 304 ) { + statusText = "notmodified"; + + // If we have data, let's convert it + } else { + statusText = response.state; + success = response.data; + error = response.error; + isSuccess = !error; + } + } else { + + // Extract error from statusText and normalize for non-aborts + error = statusText; + if ( status || !statusText ) { + statusText = "error"; + if ( status < 0 ) { + status = 0; + } + } + } + + // Set data for the fake xhr object + jqXHR.status = status; + jqXHR.statusText = ( nativeStatusText || statusText ) + ""; + + // Success/Error + if ( isSuccess ) { + deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); + } else { + deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); + } + + // Status-dependent callbacks + jqXHR.statusCode( statusCode ); + statusCode = undefined; + + if ( fireGlobals ) { + globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", + [ jqXHR, s, isSuccess ? success : error ] ); + } + + // Complete + completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); + + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); + + // Handle the global AJAX counter + if ( !( --jQuery.active ) ) { + jQuery.event.trigger( "ajaxStop" ); + } + } + } + + return jqXHR; + }, + + getJSON: function( url, data, callback ) { + return jQuery.get( url, data, callback, "json" ); + }, + + getScript: function( url, callback ) { + return jQuery.get( url, undefined, callback, "script" ); + } +} ); + +jQuery.each( [ "get", "post" ], function( _i, method ) { + jQuery[ method ] = function( url, data, callback, type ) { + + // Shift arguments if data argument was omitted + if ( isFunction( data ) ) { + type = type || callback; + callback = data; + data = undefined; + } + + // The url can be an options object (which then must have .url) + return jQuery.ajax( jQuery.extend( { + url: url, + type: method, + dataType: type, + data: data, + success: callback + }, jQuery.isPlainObject( url ) && url ) ); + }; +} ); + +jQuery.ajaxPrefilter( function( s ) { + var i; + for ( i in s.headers ) { + if ( i.toLowerCase() === "content-type" ) { + s.contentType = s.headers[ i ] || ""; + } + } +} ); + + +jQuery._evalUrl = function( url, options, doc ) { + return jQuery.ajax( { + url: url, + + // Make this explicit, since user can override this through ajaxSetup (#11264) + type: "GET", + dataType: "script", + cache: true, + async: false, + global: false, + + // Only evaluate the response if it is successful (gh-4126) + // dataFilter is not invoked for failure responses, so using it instead + // of the default converter is kludgy but it works. + converters: { + "text script": function() {} + }, + dataFilter: function( response ) { + jQuery.globalEval( response, options, doc ); + } + } ); +}; + + +jQuery.fn.extend( { + wrapAll: function( html ) { + var wrap; + + if ( this[ 0 ] ) { + if ( isFunction( html ) ) { + html = html.call( this[ 0 ] ); + } + + // The elements to wrap the target around + wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); + + if ( this[ 0 ].parentNode ) { + wrap.insertBefore( this[ 0 ] ); + } + + wrap.map( function() { + var elem = this; + + while ( elem.firstElementChild ) { + elem = elem.firstElementChild; + } + + return elem; + } ).append( this ); + } + + return this; + }, + + wrapInner: function( html ) { + if ( isFunction( html ) ) { + return this.each( function( i ) { + jQuery( this ).wrapInner( html.call( this, i ) ); + } ); + } + + return this.each( function() { + var self = jQuery( this ), + contents = self.contents(); + + if ( contents.length ) { + contents.wrapAll( html ); + + } else { + self.append( html ); + } + } ); + }, + + wrap: function( html ) { + var htmlIsFunction = isFunction( html ); + + return this.each( function( i ) { + jQuery( this ).wrapAll( htmlIsFunction ? 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    +
    +
    +

    +modsem +

    +

    This is a package which allows you to perform interactions between latent variables (i.e., moderation) in CB-SEM. See https://kss2k.github.io/intro_modsem/ for a tutorial.

    +
    +
    +

    To Install +

    +
    # From CRAN
+install.packages("modsem")
+
+# Latest version from Github
+install.packages("devtools")
+devtools::install_github("kss2k/modsem", build_vignettes = TRUE)
    +
    +
    +

    Methods/Approaches +

    +

    There are a number of approaches for estimating interaction effects in SEM. In modsem(), the method = "method" argument allows you to choose which to use.

    +
      +
    • +"ca" = constrained approach (Algina & Moulder, 2001) +
        +
      • Note that constraints can become quite complicated for complex models, particularly when there is an interaction including enodgenous variables. The method can therefore be quite slow.
        • +
      +
      • +
    • +"uca" = unconstrained approach (Marsh, 2004)
      • +
    • +"rca" = residual centering approach (Little et al., 2006)
      • +
    • +"dblcent" = double centering approach (Marsh., 2013) +
        +
      • default
        • +
      +
      • +
    • +"pind" = basic product indicator approach (not recommended)
      • +
    • +"lms" = The Latent Moderated Structural equations (LMS) approach, see the vignette +
      • +
    • +"qml" = The Quasi Maximum Likelihood (QML) approach, see the vignette +
      • +
    • +"mplus" +
        +
      • estimates model through Mplus, if it is installed
        • +
      +
      • +
    +
    +
    +

    Examples +

    +
    +

    One interaction +

    +
    library(modsem)
+m1 <- '
+  # Outer Model
+  X =~ x1 + x2 +x3
+  Y =~ y1 + y2 + y3
+  Z =~ z1 + z2 + z3
+
+  # Inner model
+  Y ~ X + Z + X:Z
+'
+
+# Double centering approach
+est1_dca <- modsem(m1, oneInt)
+summary(est1_dca)
+
+# Constrained approach
+est1_ca <- modsem(m1, oneInt, method = "ca")
+summary(est1_ca)
+
+# QML approach
+est1_qml <- modsem(m1, oneInt, method = "qml")
+summary(est1_qml, standardized = TRUE)
+
+# LMS approach
+est1_lms <- modsem(m1, oneInt, method = "lms")
+summary(est1_lms)
    +
    +
    +

    Theory Of Planned Behavior +

    +
    tpb <- "
+# Outer Model (Based on Hagger et al., 2007)
+  ATT =~ att1 + att2 + att3 + att4 + att5
+  SN =~ sn1 + sn2
+  PBC =~ pbc1 + pbc2 + pbc3
+  INT =~ int1 + int2 + int3
+  BEH =~ b1 + b2
+
+# Inner Model (Based on Steinmetz et al., 2011)
+  # Causal Relationsships
+  INT ~ ATT + SN + PBC
+  BEH ~ INT + PBC
+  BEH ~ PBC:INT
+"
+
+# double centering approach
+est_tpb_dca <- modsem(tpb, data = TPB, method = "dblcent")
+summary(est_tpb_dca)
+
+# Constrained approach using Wrigths path tracing rules for generating
+# the appropriate constraints
+est_tpb_ca <- modsem(tpb, data = TPB, method = "ca")
+summary(est_tpb_ca)
+
+# LMS approach
+est_tpb_lms <- modsem(tpb, data = TPB, method = "lms")
+summary(est_tpb_lms, standardized = TRUE)
+
+# QML approach
+est_tpb_qml <- modsem(tpb, data = TPB, method = "qml")
+summary(est_tpb_qml, standardized = TRUE)
    +
    +
    +

    Interactions between two observed variables +

    +
    est2 <- modsem('y1 ~ x1 + z1 + x1:z1', data = oneInt, method = "pind")
+summary(est2)
+
+## Interaction between an obsereved and a latent variable
+m3 <- '
+  # Outer Model
+  X =~ x1 + x2 +x3
+  Y =~ y1 + y2 + y3
+
+  # Inner model
+  Y ~ X + z1 + X:z1
+'
+
+est3 <- modsem(m3, oneInt, method = "pind")
+summary(est3)
    +
    +
    + +
    +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    +
    + + + + + + + diff --git a/katex-auto.js b/katex-auto.js new file mode 100644 index 0000000..20651d9 --- /dev/null +++ b/katex-auto.js @@ -0,0 +1,14 @@ +// https://github.com/jgm/pandoc/blob/29fa97ab96b8e2d62d48326e1b949a71dc41f47a/src/Text/Pandoc/Writers/HTML.hs#L332-L345 +document.addEventListener("DOMContentLoaded", function () { + var mathElements = document.getElementsByClassName("math"); + var macros = []; + for (var i = 0; i < mathElements.length; i++) { + var texText = mathElements[i].firstChild; + if (mathElements[i].tagName == "SPAN") { + katex.render(texText.data, mathElements[i], { + displayMode: mathElements[i].classList.contains("display"), + throwOnError: false, + macros: macros, + fleqn: false + }); + }}}); diff --git a/lightswitch.js b/lightswitch.js new file mode 100644 index 0000000..9467125 --- /dev/null +++ b/lightswitch.js @@ -0,0 +1,85 @@ + +/*! + * Color mode toggler for Bootstrap's docs (https://getbootstrap.com/) + * Copyright 2011-2023 The Bootstrap Authors + * Licensed under the Creative Commons Attribution 3.0 Unported License. + * Updates for {pkgdown} by the {bslib} authors, also licensed under CC-BY-3.0. + */ + +const getStoredTheme = () => localStorage.getItem('theme') +const setStoredTheme = theme => localStorage.setItem('theme', theme) + +const getPreferredTheme = () => { + const storedTheme = getStoredTheme() + if (storedTheme) { + return storedTheme + } + + return window.matchMedia('(prefers-color-scheme: dark)').matches ? 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(2023). -Obtained from https://doi.org/10.23668/psycharchives.12187 -} -\description{ -A dataset based on the Theory of Planned Behaviour from a -UK sample. 4 variables with high communality were selected for each -latent variable (ATT, SN, PBC, INT, BEH), from two time points (t1 and t2). -} -\examples{ - -tpb_uk <- ' -# Outer Model (Based on Hagger et al., 2007) - ATT =~ att3 + att2 + att1 + att4 - SN =~ sn4 + sn2 + sn3 + sn1 - PBC =~ pbc2 + pbc1 + pbc3 + pbc4 - INT =~ int2 + int1 + int3 + int4 - BEH =~ beh3 + beh2 + beh1 + beh4 - -# Inner Model (Based on Steinmetz et al., 2011) - # Causal Relationsships - INT ~ ATT + SN + PBC - BEH ~ INT + PBC - BEH ~ INT:PBC -' - -est <- modsem(tpb_uk, data = TPB_UK) -} diff --git a/man/coef_modsem_da.Rd b/man/coef_modsem_da.Rd deleted file mode 100644 index 87fb844..0000000 --- a/man/coef_modsem_da.Rd +++ /dev/null @@ -1,17 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/generics_modsem_da.R -\name{coef_modsem_da} -\alias{coef_modsem_da} -\title{Wrapper for coef} -\usage{ -coef_modsem_da(object, ...) -} -\arguments{ -\item{object}{fittet model to inspect} - -\item{...}{additional arguments} -} -\description{ -wrapper for coef, to be used with modsem::coef_modsem_da, -since coef is not in the namespace of modsem, but stats -} diff --git a/man/compare_fit.Rd b/man/compare_fit.Rd deleted file mode 100644 index 40ad895..0000000 --- a/man/compare_fit.Rd +++ /dev/null @@ -1,50 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/generics_modsem_da.R -\name{compare_fit} -\alias{compare_fit} -\title{compare model fit for qml and lms models} -\usage{ -compare_fit(estH0, estH1) -} -\arguments{ -\item{estH0}{object of class `modsem_da` representing the -null hypothesis model} - -\item{estH1}{object of class `modsem_da` representing the} -} -\description{ -Compare the fit of two models using the likelihood ratio test. -`estH0` representing the null -hypothesis model, and `estH1` the alternative hypothesis model. Importantly, -the function assumes that `estH0` does not have more free parameters -(i.e., degrees of freedom) than `estH1`. -alternative hypothesis model -} -\examples{ -\dontrun{ -H0 <- " - # Outer Model - X =~ x1 + x2 + x3 - Y =~ y1 + y2 + y3 - Z =~ z1 + z2 + z3 - - # Inner model - Y ~ X + Z -" - -estH0 <- modsem(m1, oneInt, "lms") - -H1 <- " - # Outer Model - X =~ x1 + x2 + x3 - Y =~ y1 + y2 + y3 - Z =~ z1 + z2 + z3 - - # Inner model - Y ~ X + Z + X:Z -" - -estH1 <- modsem(m1, oneInt, "lms") -compare_fit(estH0, estH1) -} -} diff --git a/man/default_settings_da.Rd b/man/default_settings_da.Rd deleted file mode 100644 index 451615b..0000000 --- a/man/default_settings_da.Rd +++ /dev/null @@ -1,21 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/method_settings_da.R -\name{default_settings_da} -\alias{default_settings_da} -\title{default arguments fro LMS and QML approach} -\usage{ -default_settings_da(method = c("lms", "qml")) -} -\arguments{ -\item{method}{which method to get the settings for} -} -\value{ -list -} -\description{ -This function returns the default settings for the LMS and QML approach. -} -\examples{ -library(modsem) -default_settings_da() -} diff --git a/man/default_settings_pi.Rd b/man/default_settings_pi.Rd deleted file mode 100644 index 805ee62..0000000 --- a/man/default_settings_pi.Rd +++ /dev/null @@ -1,21 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/method_settings_pi.R -\name{default_settings_pi} -\alias{default_settings_pi} -\title{default arguments for product indicator approaches} -\usage{ -default_settings_pi(method = c("rca", "uca", "pind", "dblcent", "ca")) -} -\arguments{ -\item{method}{which method to get the settings for} -} -\value{ -list -} -\description{ -This function returns the default settings for the product indicator approaches -} -\examples{ -library(modsem) -default_settings_pi() -} diff --git a/man/extract_lavaan.Rd b/man/extract_lavaan.Rd deleted file mode 100644 index e4f29e6..0000000 --- a/man/extract_lavaan.Rd +++ /dev/null @@ -1,31 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/modsem_pi.R -\name{extract_lavaan} -\alias{extract_lavaan} -\title{extract lavaan object from modsem object estimated using product indicators} -\usage{ -extract_lavaan(object) -} -\arguments{ -\item{object}{modsem object} -} -\value{ -lavaan object -} -\description{ -extract lavaan object from modsem object estimated using product indicators -} -\examples{ -library(modsem) -m1 <- ' - # Outer Model - X =~ x1 + x2 + x3 - Y =~ y1 + y2 + y3 - Z =~ z1 + z2 + z3 - - # Inner model - Y ~ X + Z + X:Z -' -est <- modsem_pi(m1, oneInt) -lav_est <- extract_lavaan(est) -} diff --git a/man/fit_modsem_da.Rd b/man/fit_modsem_da.Rd deleted file mode 100644 index fcae706..0000000 --- a/man/fit_modsem_da.Rd +++ /dev/null @@ -1,21 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/fit_modsem_da.R -\name{fit_modsem_da} -\alias{fit_modsem_da} -\title{Fit measures for QML and LMS models} -\usage{ -fit_modsem_da(model, chisq = TRUE) -} -\arguments{ -\item{model}{fitted model. Thereafter, you can use 'compare_fit()' -to assess the comparative fit of the models. If the interaction effect makes -the model better, and e.g., the RMSEA is good for the baseline model, -the interaction model likely has a good RMSEA as well.} - -\item{chisq}{should Chi-Square based fit-measures be calculated?} -} -\description{ -Calculates chi-sq test and p-value, as well as RMSEA for -the LMS and QML models. Note that the Chi-Square based fit measures should be calculated -for the baseline model, i.e., the model without the interaction effect -} diff --git a/man/get_pi_data.Rd b/man/get_pi_data.Rd deleted file mode 100644 index 3d5fb1a..0000000 --- a/man/get_pi_data.Rd +++ /dev/null @@ -1,47 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/modsem_pi.R -\name{get_pi_data} -\alias{get_pi_data} -\title{Get data with product indicators for different approaches} -\usage{ -get_pi_data(model.syntax, data, method = "dblcent", match = FALSE, ...) -} -\arguments{ -\item{model.syntax}{lavaan syntax} - -\item{data}{data to create product indicators from} - -\item{method}{method to use: -"rca" = residual centering approach, -"uca" = unconstrained approach, -"dblcent" = double centering approach, -"pind" = prod ind approach, with no constraints or centering, -"custom" = use parameters specified in the function call} - -\item{match}{should product indicators be made using the match strategy} - -\item{...}{arguments passed to other functions (e.g., modsem_pi)} -} -\value{ -data.frame -} -\description{ -get_pi_syntax is a function for creating the lavaan syntax used for estimating -latent interaction models using one of the product indiactors in lavaan. -} -\examples{ -library(modsem) -library(lavaan) -m1 <- ' - # Outer Model - X =~ x1 + x2 +x3 - Y =~ y1 + y2 + y3 - Z =~ z1 + z2 + z3 - - # Inner model - Y ~ X + Z + X:Z -' -syntax <- get_pi_syntax(m1) -data <- get_pi_data(m1, oneInt) -est <- sem(syntax, data) -} diff --git a/man/get_pi_syntax.Rd b/man/get_pi_syntax.Rd deleted file mode 100644 index 0363d4f..0000000 --- a/man/get_pi_syntax.Rd +++ /dev/null @@ -1,45 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/modsem_pi.R -\name{get_pi_syntax} -\alias{get_pi_syntax} -\title{Get lavaan syntax for product indicator approaches} -\usage{ -get_pi_syntax(model.syntax, method = "dblcent", match = FALSE, ...) -} -\arguments{ -\item{model.syntax}{lavaan syntax} - -\item{method}{method to use: -"rca" = residual centering approach, -"uca" = unconstrained approach, -"dblcent" = double centering approach, -"pind" = prod ind approach, with no constraints or centering, -"custom" = use parameters specified in the function call} - -\item{match}{should product indicators be made using the match strategy} - -\item{...}{arguments passed to other functions (e.g., modsem_pi)} -} -\value{ -character vector -} -\description{ -get_pi_syntax is a function for creating the lavaan syntax used for estimating -latent interaction models using one of the product indiactors in lavaan. -} -\examples{ -library(modsem) -library(lavaan) -m1 <- ' - # Outer Model - X =~ x1 + x2 +x3 - Y =~ y1 + y2 + y3 - Z =~ z1 + z2 + z3 - - # Inner model - Y ~ X + Z + X:Z -' -syntax <- get_pi_syntax(m1) -data <- get_pi_data(m1, oneInt) -est <- sem(syntax, data) -} diff --git a/man/jordan.Rd b/man/jordan.Rd deleted file mode 100644 index 219384d..0000000 --- a/man/jordan.Rd +++ /dev/null @@ -1,84 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/datasets.R -\docType{data} -\name{jordan} -\alias{jordan} -\title{Jordan subset of PISA 2006 data} -\format{ -A data frame of fifteen variables and 6,038 observations: - -enjoy1 -indicator for enjoyment of science, item ST16Q01: I generally have fun when I am learning topics. - -enjoy2 -indicator for enjoyment of science, item ST16Q02: I like reading about . - -enjoy3 -indicator for enjoyment of science, item ST16Q03: I am happy doing problems. - -enjoy4 -indicator for enjoyment of science, item ST16Q04: I enjoy acquiring new knowledge in . - -enjoy5 -indicator for enjoyment of science, item ST16Q05: I am interested in learning about . - -academic1 -indicator for academic self-concept in science, item ST37Q01: I can easily understand new ideas in . - -academic2 -indicator for academic self-concept in science, item ST37Q02: Learning advanced topics would be easy for me. - -academic3 -indicator for academic self-concept in science, item ST37Q03: I can usually give good answers to on topics. - -academic4 -indicator for academic self-concept in science, item ST37Q04: I learn topics quickly. - -academic5 -indicator for academic self-concept in science, item ST37Q05: topics are easy for me. - -academic6 -indicator for academic self-concept in science, item ST37Q06: When I am being taught , I can understand the concepts very well. - -career1 -indicator for career aspirations in science, item ST29Q01: I would like to work in a career involving . - -career2 -indicator for career aspirations in science, item ST29Q02: I would like to study after . - -career3 -indicator for career aspirations in science, item ST29Q03: I would like to spend my life doing advanced . - -career4 -indicator for career aspirations in science, item ST29Q04: I would like to work on projects as an adult. -} -\source{ -This version of the dataset, as well as the description was gathered from the -documentation of the 'nlsem' package (https://cran.r-project.org/package=nlsem), -where the only difference is that the names of the variables were changed - -Originally the dataset was gathered by the Organisation for Economic Co-Operation and Development (2009). -Pisa 2006: Science competencies for tomorrow's world (Tech. Rep.). -Paris, France. Obtained from: https://www.oecd.org/pisa/pisaproducts/database-pisa2006.htm -} -\description{ -The data stem from the large-scale assessment study PISA 2006 -(Organisation for Economic Co-Operation and Development, 2009) where -competencies of 15-year-old students in reading, mathematics, and science -are assessed using nationally representative samples in 3-year cycles. -In this eacademicample, data from the student background questionnaire from the -Jordan sample of PISA 2006 were used. Only data of students with complete -responses to all 15 items (N = 6,038) were considered. -} -\examples{ -\dontrun{ -m1 <- ' - ENJ =~ enjoy1 + enjoy2 + enjoy3 + enjoy4 + enjoy5 - CAREER =~ career1 + career2 + career3 + career4 - SC =~ academic1 + academic2 + academic3 + academic4 + academic5 + academic6 - CAREER ~ ENJ + SC + ENJ:ENJ + SC:SC + ENJ:SC -' - -est <- modsem(m1, data = jordan) -} -} diff --git a/man/modsem-package.Rd b/man/modsem-package.Rd deleted file mode 100644 index f52ec87..0000000 --- a/man/modsem-package.Rd +++ /dev/null @@ -1,21 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/modsem-package.R -\docType{package} -\name{modsem-package} -\alias{modsem-package} -\title{modsem: Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM)} -\description{ -Estimation of interaction (i.e., moderation) effects between latent variables in structural equation models (SEM). The supported methods are: The constrained approach (Algina & Moulder, 2001). The unconstrained approach (Marsh et al., 2004). The residual centering approach (Little et al., 2006). The double centering approach (Lin et al., 2010). The latent moderated structural equations (LMS) approach (Klein & Moosbrugger, 2000). The quasi-maximum likelihood (QML) approach (Klein & Muthén, 2007) (temporarily unavailable) The constrained- unconstrained, residual- and double centering- approaches are estimated via 'lavaan' (Rosseel, 2012), whilst the LMS- and QML- approaches are estimated via by modsem it self. Alternatively model can be estimated via 'Mplus' (Muthén & Muthén, 1998-2017). References: Algina, J., & Moulder, B. C. (2001). \doi{10.1207/S15328007SEM0801_3}. "A note on estimating the Jöreskog-Yang model for latent variable interaction using 'LISREL' 8.3." Klein, A., & Moosbrugger, H. (2000). \doi{10.1007/BF02296338}. "Maximum likelihood estimation of latent interaction effects with the LMS method." Klein, A. G., & Muthén, B. O. (2007). \doi{10.1080/00273170701710205}. "Quasi-maximum likelihood estimation of structural equation models with multiple interaction and quadratic effects." Lin, G. C., Wen, Z., Marsh, H. W., & Lin, H. S. (2010). \doi{10.1080/10705511.2010.488999}. "Structural equation models of latent interactions: Clarification of orthogonalizing and double-mean-centering strategies." Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). \doi{10.1207/s15328007sem1304_1}. "On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables." Marsh, H. W., Wen, Z., & Hau, K. T. (2004). \doi{10.1037/1082-989X.9.3.275}. "Structural equation models of latent interactions: evaluation of alternative estimation strategies and indicator construction." Muthén, L.K. and Muthén, B.O. (1998-2017). "'Mplus' User’s Guide. Eighth Edition." \url{https://www.statmodel.com/}. Rosseel Y (2012). \doi{10.18637/jss.v048.i02}. "'lavaan': An R Package for Structural Equation Modeling." -} -\seealso{ -Useful links: -\itemize{ - \item \url{https://github.com/Kss2k/modsem} -} - -} -\author{ -\strong{Maintainer}: Kjell Solem Slupphaug \email{slupphaugkjell@gmail.com} (\href{https://orcid.org/0009-0005-8324-2834}{ORCID}) - -} -\keyword{internal} diff --git a/man/modsem.Rd b/man/modsem.Rd deleted file mode 100644 index 737fa7f..0000000 --- a/man/modsem.Rd +++ /dev/null @@ -1,108 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/modsem-package.R, R/modsem.R -\name{modsem} -\alias{modsem} -\title{Interaction between latent variables} -\usage{ -modsem(model.syntax = NULL, data = NULL, method = "dblcent", ...) -} -\arguments{ -\item{model.syntax}{lavaan syntax} - -\item{data}{dataframe} - -\item{method}{method to use: -"rca" = residual centering approach (passed to lavaan), -"uca" = unconstrained approach (passed to lavaan), -"dblcent" = double centering approach (passed to lavaan), -"pind" = prod ind approach, with no constraints or centering (passed to lavaan), -"lms" = laten model structural equations (not passed to lavaan). -"qml" = quasi maximum likelihood estimation of laten model structural equations (not passed to lavaan). -"custom" = use parameters specified in the function call (passed to lavaan)} - -\item{...}{arguments passed to other functions depending on method (see modsem_pi, modsem_da, and modsem_mplus)} -} -\value{ -modsem object -} -\description{ -modsem is a function for estimating interaction effects between latent variables, -in structural equation models (SEM's). -Methods for estimating interaction effects in SEM's can basically be split into -two frameworks: 1. Product Indicator based approaches ("dblcent", "rca", "uca", -"ca", "pind"), and 2. Distributionally based approaches ("lms", "qml"). -For the product indicator based approaces, modsem() is essentially a just -a fancy wrapper for lavaan::sem() which generates the -necessary syntax, and variables for the estimation of models with latent product indicators. -The distributionally based approaches are implemented in seperately, and are -are not estimated using lavaan::sem(), but rather using custom functions (largely) -written in C++ for performance reasons. For greater control, it is advised that -you use one of the sub-functions (modsem_pi, modsem_da, modsem_mplus) directly, -as passing additional arguments to them via modsem() can lead to unexpected behavior. -} -\examples{ -library(modsem) -# For more examples check README and/or GitHub. -# One interaction -m1 <- ' - # Outer Model - X =~ x1 + x2 +x3 - Y =~ y1 + y2 + y3 - Z =~ z1 + z2 + z3 - - # Inner model - Y ~ X + Z + X:Z -' - -# Double centering approach -est1 <- modsem(m1, oneInt) -summary(est1) - -\dontrun{ -# The Constrained Approach -est1_ca <- modsem(m1, oneInt, method = "ca") -summary(est1_ca) - -# LMS approach -est1_lms <- modsem(m1, oneInt, method = "lms") -summary(est1_lms) - -# QML approach -est1_qml <- modsem(m1, oneInt, method = "qml") -summary(est1_qml) - -} - -# Theory Of Planned Behavior -tpb <- ' -# Outer Model (Based on Hagger et al., 2007) - ATT =~ att1 + att2 + att3 + att4 + att5 - SN =~ sn1 + sn2 - PBC =~ pbc1 + pbc2 + pbc3 - INT =~ int1 + int2 + int3 - BEH =~ b1 + b2 - -# Inner Model (Based on Steinmetz et al., 2011) - INT ~ ATT + SN + PBC - BEH ~ INT + PBC - BEH ~ INT:PBC -' - -# double centering approach -est_tpb <- modsem(tpb, data = TPB) -summary(est_tpb) - -\dontrun{ -# The Constrained Approach -est_tpb_ca <- modsem(tpb, data = TPB, method = "ca") -summary(est_tpb_ca) - -# LMS approach -est_tpb_lms <- modsem(tpb, data = TPB, method = "lms") -summary(est_tpb_lms) - -# QML approach -est_tpb_qml <- modsem(tpb, data = TPB, method = "qml") -summary(est_tpb_qml) -} -} diff --git a/man/modsem_da.Rd b/man/modsem_da.Rd deleted file mode 100644 index 1c4b934..0000000 --- a/man/modsem_da.Rd +++ /dev/null @@ -1,190 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/modsem_da.R -\name{modsem_da} -\alias{modsem_da} -\title{Interaction between latent variables using lms and qml approaches} -\usage{ -modsem_da( - model.syntax = NULL, - data = NULL, - method = "lms", - verbose = NULL, - optimize = NULL, - nodes = NULL, - convergence = NULL, - optimizer = NULL, - center.data = NULL, - standardize.data = NULL, - standardize.out = NULL, - standardize = NULL, - mean.observed = NULL, - cov.syntax = NULL, - double = NULL, - calc.se = NULL, - FIM = NULL, - EFIM.S = NULL, - OFIM.hessian = NULL, - EFIM.parametric = NULL, - robust.se = NULL, - max.iter = NULL, - max.step = NULL, - fix.estep = NULL, - start = NULL, - epsilon = NULL, - quad.range = NULL, - n.threads = NULL, - ... -) -} -\arguments{ -\item{model.syntax}{lavaan syntax} - -\item{data}{dataframe} - -\item{method}{method to use: -"lms" = laten model structural equations (not passed to lavaan). -"qml" = quasi maximum likelihood estimation of laten model structural equations (not passed to lavaan).} - -\item{verbose}{should estimation progress be shown} - -\item{optimize}{should starting parameters be optimized} - -\item{nodes}{number of quadrature nodes (points of integration) used in lms, -increased number gives better estimates but slower computation. How many is needed, depends on the complexity of the model -For simple models you somwhere between 16-24 should be enough, for more complex higher numbers may be needed. -For models where there is an interaction effects between and endogenous and exogenous variable -the number of nodes should at least be 32, but practically (e.g., ordinal/skewed data) more than 32 is recommended. In cases, -where data is non-normal it might be better to use the qml approach instead. For large -numbers of nodes, you might want to change the 'quad.range' argument.} - -\item{convergence}{convergence criterion. Lower values give better estimates but slower computation.} - -\item{optimizer}{optimizer to use, can be either "nlminb" or "L-BFGS-B". For LMS, "nlminb" is recommended. -For QML, "L-BFGS-B" may be faster if there is a large number of iterations, but slower if there are few iterations.} - -\item{center.data}{should data be centered before fitting model} - -\item{standardize.data}{should data be scaled before fitting model, will be overridden by -standardize if standardize is set to TRUE. -NOTE: It is recommended that you estimate the model normally and then standardize the output using -`standardized_estimates()`.} - -\item{standardize.out}{should output be standardized (note will alter the relationsships of -parameter constraints, since to parameters are scaled unevenly, even if they -have the same label). This does not alter the estimation of the model, only the -output. -NOTE: It is recommended that you estimate the model normally and then standardize the output using -`standardized_estimates()`.} - -\item{standardize}{will standardize the data before fitting the model, remove the mean -structure of the observed variables, and standardize the output. Note that standardize.data -mean.observed, standardize.out will be overridden by standardize if standardize is set to TRUE. -NOTE: It is recommended that you estimate the model normally and then standardize the output using -`standardized_estimates()`.} - -\item{mean.observed}{should mean structure of the observed variables be estimated, -will be overridden by standardize if standardize is set to TRUE. -NOTE: Not recommended unless you know what you are doing.} - -\item{cov.syntax}{model syntax for implied covariance matrix (see 'vignette("interaction_two_etas", "modsem")')} - -\item{double}{try to double the number of dimensions of integrations used in LMS, -this will be extremely slow, but should be more similar to mplus.} - -\item{calc.se}{should standard errros be computed, NOTE: If 'FALSE' information matrix will not be computed either} - -\item{FIM}{should fisher information matrix be calculated using observed of expected. must be either "observed" or "expected"} - -\item{EFIM.S}{if expected fisher information matrix is computed, EFIM.S selects the sample size of the generated data} - -\item{OFIM.hessian}{should observed fisher information be computed using hessian? if FALSE, it is computed using gradient} - -\item{EFIM.parametric}{should data for calculating expected fisher information matrix be -simulated parametrically (simulated based on the assumptions- and implied parameters -from the model), or non-parametrically (stochastically sampled). If you believe that -normality assumptions are violated, 'EFIM.parametric = FALSE' might be the better option.} - -\item{robust.se}{should robust standard errors be computed? Meant to be used for QML, -can be unreliable with the LMS-approach.} - -\item{max.iter}{max numebr of iterations} - -\item{max.step}{max steps for the M-step in the EM algorithm (LMS)} - -\item{fix.estep}{if TRUE, E-step will be fixed and the prior probabilities are set to the best prior probabilities, -if loglikelihood is decreasing for more than 30 iterations.} - -\item{start}{starting parameters} - -\item{epsilon}{finite difference for numerical derivatives} - -\item{quad.range}{range in z-scores to perform numerical integration in LMS using -Gaussian-Hermite Quadratures. By default Inf, such that f(t) is integrated from -Inf to Inf, -but this will likely be inefficient and pointless at large number of nodes. Nodes outside -+/- quad.range will be ignored.} - -\item{n.threads}{number of cores to use for parallel processing, if NULL, it will use <= 2 threads, -if an integer is specified, it will use that number of threads (e.g., `n.threads = 4`, will use 4 threads) -if = "default" it will use the default number of threads (2). -if = "max" it will use all available threads, "min" will use 1 thread.} - -\item{...}{additional arguments to be passed to the estimation function} -} -\value{ -modsem_da object -} -\description{ -modsem_da is a function for estimating interaction effects between latent variables, -in structural equation models (SEMs), using distributional analytic (DA) approaches. -Methods for estimating interaction effects in SEM's can basically be split into -two frameworks: 1. Product Indicator based approaches ("dblcent", "rca", "uca", -"ca", "pind"), and 2. Distributionally based approaches ("lms", "qml"). -modsem_da() handles the latter, and can estimate models using both qml and lms -necessary syntax, and variables for the estimation of models with latent product indicators. -NOTE: run 'default_settings_da()' to see default arguments. -} -\examples{ -library(modsem) -# For more examples check README and/or GitHub. -# One interaction -m1 <- " - # Outer Model - X =~ x1 + x2 +x3 - Y =~ y1 + y2 + y3 - Z =~ z1 + z2 + z3 - - # Inner model - Y ~ X + Z + X:Z -" - -\dontrun{ -# QML Approach -est1 <- modsem_da(m1, oneInt, method = "qml") -summary(est1) - - -# Theory Of Planned Behavior -tpb <- " -# Outer Model (Based on Hagger et al., 2007) - ATT =~ att1 + att2 + att3 + att4 + att5 - SN =~ sn1 + sn2 - PBC =~ pbc1 + pbc2 + pbc3 - INT =~ int1 + int2 + int3 - BEH =~ b1 + b2 - -# Inner Model (Based on Steinmetz et al., 2011) - # Covariances - ATT ~~ SN + PBC - PBC ~~ SN - # Causal Relationsships - INT ~ ATT + SN + PBC - BEH ~ INT + PBC - BEH ~ INT:PBC -" - -# lms approach -estTpb <- modsem_da(tpb, data = TPB, method = lms) -summary(estTpb) -} - -} diff --git a/man/modsem_inspect.Rd b/man/modsem_inspect.Rd deleted file mode 100644 index 4cf6637..0000000 --- a/man/modsem_inspect.Rd +++ /dev/null @@ -1,25 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/generics.R -\name{modsem_inspect} -\alias{modsem_inspect} -\title{Inspect model information} -\usage{ -modsem_inspect(object, what = NULL, ...) -} -\arguments{ -\item{object}{fittet model to inspect} - -\item{what}{what to inspect} - -\item{...}{Additional arguments passed to other functions} -} -\description{ -function used to inspect fittet object. similar to `lavInspect()` -argument 'what' decides what to inspect -} -\details{ -for `modsem_da`, and `modsem_lavaan` -for `modsem_lavaan`, it is just a wrapper for `lavInspect()` -for `modsem_da` and `` what can either be "all", "matrices", "optim", -or just the name of what to extract. -} diff --git a/man/modsem_mplus.Rd b/man/modsem_mplus.Rd deleted file mode 100644 index 2eea0be..0000000 --- a/man/modsem_mplus.Rd +++ /dev/null @@ -1,63 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/modsem_mplus.R -\name{modsem_mplus} -\alias{modsem_mplus} -\title{Estimation latent interactions through mplus} -\usage{ -modsem_mplus( - model.syntax, - data, - estimator = "ml", - type = "random", - algorithm = "integration", - process = "8", - ... -) -} -\arguments{ -\item{model.syntax}{lavaan/modsem syntax} - -\item{data}{dataset} - -\item{estimator}{estimator argument passed to mplus} - -\item{type}{type argument passed to mplus} - -\item{algorithm}{algorithm argument passed to mplus} - -\item{process}{process argument passed to mplus} - -\item{...}{arguments passed to other functions} -} -\value{ -modsem_mplus object -} -\description{ -Estimation latent interactions through mplus -} -\examples{ -# Theory Of Planned Behavior -tpb <- ' -# Outer Model (Based on Hagger et al., 2007) - ATT =~ att1 + att2 + att3 + att4 + att5 - SN =~ sn1 + sn2 - PBC =~ pbc1 + pbc2 + pbc3 - INT =~ int1 + int2 + int3 - BEH =~ b1 + b2 - -# Inner Model (Based on Steinmetz et al., 2011) - # Covariances - ATT ~~ SN + PBC - PBC ~~ SN - # Causal Relationsships - INT ~ ATT + SN + PBC - BEH ~ INT + PBC - BEH ~ INT:PBC -' - -\dontrun{ -estTpbMplus <- modsem_mplus(tpb, data = TPB) -summary(estTpbLMS) -} - -} diff --git a/man/modsem_pi.Rd b/man/modsem_pi.Rd deleted file mode 100644 index 49e2057..0000000 --- a/man/modsem_pi.Rd +++ /dev/null @@ -1,148 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/modsem_pi.R -\name{modsem_pi} -\alias{modsem_pi} -\title{Interaction between latent variables using product indicators} -\usage{ -modsem_pi( - model.syntax = NULL, - data = NULL, - method = "dblcent", - match = NULL, - standardize.data = FALSE, - center.data = FALSE, - first.loading.fixed = TRUE, - center.before = NULL, - center.after = NULL, - residuals.prods = NULL, - residual.cov.syntax = NULL, - constrained.prod.mean = NULL, - constrained.loadings = NULL, - constrained.var = NULL, - constrained.res.cov.method = NULL, - auto.scale = "none", - auto.center = "none", - estimator = "ML", - group = NULL, - run = TRUE, - suppress.warnings.lavaan = FALSE, - ... -) -} -\arguments{ -\item{model.syntax}{lavaan syntax} - -\item{data}{dataframe} - -\item{method}{method to use: -"rca" = residual centering approach (passed to lavaan), -"uca" = unconstrained approach (passed to lavaan), -"dblcent" = double centering approach (passed to lavaan), -"pind" = prod ind approach, with no constraints or centering (passed to lavaan), -"custom" = use parameters specified in the function call (passed to lavaan)} - -\item{match}{should the product indicators be created by using the match-strategy} - -\item{standardize.data}{should data be scaled before fitting model} - -\item{center.data}{should data be centered before fitting model} - -\item{first.loading.fixed}{Sould the first factorloading in the latent prod be fixed to one?} - -\item{center.before}{should inds in prods be centered before computing prods (overwritten by method, if method != NULL)} - -\item{center.after}{should ind prods be centered after they have been computed?} - -\item{residuals.prods}{should ind prods be centered using residuals (overwritten by method, if method != NULL)} - -\item{residual.cov.syntax}{should syntax for residual covariances be produced (overwritten by method, if method != NULL)} - -\item{constrained.prod.mean}{should syntax prod mean be produced (overwritten by method, if method != NULL)} - -\item{constrained.loadings}{should syntax for constrained loadings be produced (overwritten by method, if method != NULL)} - -\item{constrained.var}{should syntax for constrained variances be produced (overwritten by method, if method != NULL)} - -\item{constrained.res.cov.method}{method for constraining residual covariances} - -\item{auto.scale}{methods which should be scaled automatically (usually not useful)} - -\item{auto.center}{methods which should be centered automatically (usually not useful)} - -\item{estimator}{estimator to use in lavaan} - -\item{group}{group variable for multigroup analysis} - -\item{run}{should the model be run via lavaan, if FALSE only modified syntax and data is returned} - -\item{suppress.warnings.lavaan}{should warnings from lavaan be supressed?} - -\item{...}{arguments passed to other functions, e.g,. lavaan} -} -\value{ -modsem object -} -\description{ -modsem_pi is a function for estimating interaction effects between latent variables, -in structural equation models (SEMs), using product indicators. -Methods for estimating interaction effects in SEM's can basically be split into -two frameworks: 1. Product Indicator based approaches ("dblcent", "rca", "uca", -"ca", "pind"), and 2. Distributionally based approaches ("lms", "qml"). -modsem_pi() is essentially a just -a fancy wrapper for lavaan::sem() which generates the -necessary syntax, and variables for the estimation of models with latent product indicators. -use `default_settings_pi()` to get the default settings for the different methods. -} -\examples{ -library(modsem) -# For more examples check README and/or GitHub. -# One interaction -m1 <- ' - # Outer Model - X =~ x1 + x2 +x3 - Y =~ y1 + y2 + y3 - Z =~ z1 + z2 + z3 - - # Inner model - Y ~ X + Z + X:Z -' - -# Double centering approach -est1 <- modsem_pi(m1, oneInt) -summary(est1) - -\dontrun{ -# The Constrained Approach -est1Constrained <- modsem_pi(m1, oneInt, method = "ca") -summary(est1Constrained) -} - -# Theory Of Planned Behavior -tpb <- ' -# Outer Model (Based on Hagger et al., 2007) - ATT =~ att1 + att2 + att3 + att4 + att5 - SN =~ sn1 + sn2 - PBC =~ pbc1 + pbc2 + pbc3 - INT =~ int1 + int2 + int3 - BEH =~ b1 + b2 - -# Inner Model (Based on Steinmetz et al., 2011) - # Covariances - ATT ~~ SN + PBC - PBC ~~ SN - # Causal Relationsships - INT ~ ATT + SN + PBC - BEH ~ INT + PBC - BEH ~ INT:PBC -' - -# double centering approach -estTpb <- modsem_pi(tpb, data = TPB) -summary(estTpb) - -\dontrun{ -# The Constrained Approach -estTpbConstrained <- modsem_pi(tpb, data = TPB, method = "ca") -summary(estTpbConstrained) -} -} diff --git a/man/modsemify.Rd b/man/modsemify.Rd deleted file mode 100644 index 8d61fb4..0000000 --- a/man/modsemify.Rd +++ /dev/null @@ -1,30 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/parser.R -\name{modsemify} -\alias{modsemify} -\title{Generate parameter table for lavaan syntax} -\usage{ -modsemify(syntax) -} -\arguments{ -\item{syntax}{model syntax} -} -\value{ -data.frame with columns lhs, op, rhs, mod -} -\description{ -Generate parameter table for lavaan syntax -} -\examples{ -library(modsem) -m1 <- ' - # Outer Model - X =~ x1 + x2 +x3 - Y =~ y1 + y2 + y3 - Z =~ z1 + z2 + z3 - - # Inner model - Y ~ X + Z + X:Z -' -modsemify(m1) -} diff --git a/man/multiplyIndicatorsCpp.Rd b/man/multiplyIndicatorsCpp.Rd deleted file mode 100644 index 38535e8..0000000 --- a/man/multiplyIndicatorsCpp.Rd +++ /dev/null @@ -1,17 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RcppExports.R -\name{multiplyIndicatorsCpp} -\alias{multiplyIndicatorsCpp} -\title{Multiply indicators} -\usage{ -multiplyIndicatorsCpp(df) -} -\arguments{ -\item{df}{A data DataFrame} -} -\value{ -A NumericVector -} -\description{ -Multiply indicators -} diff --git a/man/oneInt.Rd b/man/oneInt.Rd deleted file mode 100644 index 1031a36..0000000 --- a/man/oneInt.Rd +++ /dev/null @@ -1,9 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/datasets.R -\docType{data} -\name{oneInt} -\alias{oneInt} -\title{oneInt} -\description{ -A simulated dataset with one interaction effect -} diff --git a/man/parameter_estimates.Rd b/man/parameter_estimates.Rd deleted file mode 100644 index 33430f5..0000000 --- a/man/parameter_estimates.Rd +++ /dev/null @@ -1,16 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/generics.R -\name{parameter_estimates} -\alias{parameter_estimates} -\title{Extract parameterEstimates from an estimated model} -\usage{ -parameter_estimates(object, ...) -} -\arguments{ -\item{object}{An object of class `modsem_pi`, `modsem_da`, or `modsem_mplus`} - -\item{...}{Additional arguments passed to other functions} -} -\description{ -Extract parameterEstimates from an estimated model -} diff --git a/man/plot_interaction.Rd b/man/plot_interaction.Rd deleted file mode 100644 index 1beddff..0000000 --- a/man/plot_interaction.Rd +++ /dev/null @@ -1,83 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/plot_interaction.R -\name{plot_interaction} -\alias{plot_interaction} -\title{Plot Interaction Effects} -\usage{ -plot_interaction( - x, - z, - y, - xz = NULL, - vals_x = seq(-3, 3, 0.001), - vals_z, - model, - alpha_se = 0.15, - ... -) -} -\arguments{ -\item{x}{The name of the variable on the x-axis} - -\item{z}{The name of the moderator variable} - -\item{y}{The name of the outcome variable} - -\item{xz}{The name of the interaction term. If the interaction term is not specified, it -it will be created using `x` and `z`.} - -\item{vals_x}{The values of the x variable to plot, the more values the smoother the std.error-area will be} - -\item{vals_z}{The values of the moderator variable to plot. A seperate regression -line ("y ~ x | z") will be plotted for each value of the moderator variable} - -\item{model}{An object of class `modsem_pi`, `modsem_da`, or `modsem_mplus`} - -\item{alpha_se}{The alpha level for the std.error area} - -\item{...}{Additional arguments passed to other functions} -} -\value{ -A ggplot object -} -\description{ -Plot Interaction Effects -} -\examples{ -library(modsem) -\dontrun{ -m1 <- " -# Outer Model - X =~ x1 - X =~ x2 + x3 - Z =~ z1 + z2 + z3 - Y =~ y1 + y2 + y3 - -# Inner model - Y ~ X + Z + X:Z -" -est1 <- modsem(m1, data = oneInt) -plot_interaction("X", "Z", "Y", "X:Z", -3:3, c(-0.2, 0), est1) - -tpb <- " -# Outer Model (Based on Hagger et al., 2007) - ATT =~ att1 + att2 + att3 + att4 + att5 - SN =~ sn1 + sn2 - PBC =~ pbc1 + pbc2 + pbc3 - INT =~ int1 + int2 + int3 - BEH =~ b1 + b2 - -# Inner Model (Based on Steinmetz et al., 2011) - # Causal Relationsships - INT ~ ATT + SN + PBC - BEH ~ INT + PBC - # BEH ~ ATT:PBC - BEH ~ PBC:INT - # BEH ~ PBC:PBC -" - -est2 <- modsem(tpb, TPB, method = "lms") -plot_interaction(x = "INT", z = "PBC", y = "BEH", xz = "PBC:INT", - vals_z = c(-0.5, 0.5), model = est2) -} -} diff --git a/man/standardized_estimates.Rd b/man/standardized_estimates.Rd deleted file mode 100644 index c174a89..0000000 --- a/man/standardized_estimates.Rd +++ /dev/null @@ -1,29 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/generics.R -\name{standardized_estimates} -\alias{standardized_estimates} -\title{Get standardized estimates} -\usage{ -standardized_estimates(object, ...) -} -\arguments{ -\item{object}{An object of class `modsem_da`, `modsem_mplus`, -or a parTable of class `data.frame`} - -\item{...}{Additional arguments passed to other functions} -} -\description{ -Get standardized estimates -} -\details{ -for `modsem_da`, and `modsem_mplus` objects, -the interaction term is not standardized such that var(xz) = 1. -The interaction term is not an actual variable in the model, meaning that it does not -have a variance. It must therefore be calculated from the other parameters in the model. -Assuming normality and zero-means the variance is calculated as -`var(xz) = var(x) * var(z) + cov(x, z)^2`. Thus setting the variance of the interaction -term to 1, would only be 'correct' if the correlation between x and z is zero. -This means that the standardized estimates for the interaction term will -be different from those using lavaan, since there the interaction term is an -actual latent variable in the model, with a standardized variance of 1. -} diff --git a/man/summary.Rd b/man/summary.Rd deleted file mode 100644 index 2f8682d..0000000 --- a/man/summary.Rd +++ /dev/null @@ -1,102 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/generics_modsem_da.R, -% R/generics_modsem_mplus.R, R/generics_modsem_pi.R -\name{summary.modsem_da} -\alias{summary.modsem_da} -\alias{summary.modsem_mplus} -\alias{summary.modsem_pi} -\title{summary for modsem objects} -\usage{ -\method{summary}{modsem_da}( - object, - H0 = TRUE, - verbose = TRUE, - r.squared = TRUE, - adjusted.stat = FALSE, - digits = 3, - scientific = FALSE, - ci = FALSE, - standardized = FALSE, - loadings = TRUE, - regressions = TRUE, - covariances = TRUE, - intercepts = TRUE, - variances = TRUE, - var.interaction = FALSE, - ... -) - -\method{summary}{modsem_mplus}( - object, - scientific = FALSE, - standardize = FALSE, - ci = FALSE, - digits = 3, - loadings = TRUE, - regressions = TRUE, - covariances = TRUE, - intercepts = TRUE, - variances = TRUE, - ... -) - -\method{summary}{modsem_pi}(object, ...) -} -\arguments{ -\item{object}{modsem object to summarized} - -\item{H0}{should a null model be estimated (used for comparison)} - -\item{verbose}{print progress for the estimation of null model} - -\item{r.squared}{calculate R-squared} - -\item{adjusted.stat}{should sample size corrected/adjustes AIC and BIC be reported?} - -\item{digits}{number of digits to print} - -\item{scientific}{print p-values in scientific notation} - -\item{ci}{print confidence intervals} - -\item{standardized}{print standardized estimates} - -\item{loadings}{print loadings} - -\item{regressions}{print regressions} - -\item{covariances}{print covariances} - -\item{intercepts}{print intercepts} - -\item{variances}{print variances} - -\item{var.interaction}{if FALSE (default) variances for interaction terms will be removed (if present)} - -\item{...}{arguments passed to lavaan::summary()} - -\item{standardize}{standardize estimates} -} -\description{ -summary for modsem objects - -summary for modsem objects - -summary for modsem objects -} -\examples{ -\dontrun{ -m1 <- " - # Outer Model - X =~ x1 + x2 + x3 - Y =~ y1 + y2 + y3 - Z =~ z1 + z2 + z3 - - # Inner model - Y ~ X + Z + X:Z -" - -est1 <- modsem(m1, oneInt, "qml") -summary(est1, ci = TRUE, scientific = TRUE) -} -} diff --git a/man/trace_path.Rd b/man/trace_path.Rd deleted file mode 100644 index 22b95ac..0000000 --- a/man/trace_path.Rd +++ /dev/null @@ -1,58 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/trace_paths_wright.R -\name{trace_path} -\alias{trace_path} -\title{Estimate formulas for (co-)variance paths using Wright's path tracing rules} -\usage{ -trace_path( - pt, - x, - y, - parenthesis = TRUE, - missing.cov = FALSE, - measurement.model = FALSE, - maxlen = 100, - ... -) -} -\arguments{ -\item{pt}{A data frame with columns lhs, op, rhs, and mod, from modsemify(syntax)} - -\item{x}{source variable} - -\item{y}{destination variable} - -\item{parenthesis}{if TRUE, the output will be enclosed in parenthesis} - -\item{missing.cov}{if TRUE covariances missing from the model syntax will be added} - -\item{measurement.model}{if TRUE, the function will use the measurement model} - -\item{maxlen}{maximum length of a path before aborting} - -\item{...}{additional arguments passed to trace_path} -} -\value{ -A string with the estimated path (simplified if possible) -} -\description{ -This function estimates the path from x to y using the path tracing rules, -note that it only works with structural parameters, so "=~" are ignored. unless -measurement.model = TRUE. -you want to use the measurement model, -"~" in the mod column of pt. -} -\examples{ -library(modsem) -m1 <- ' - # Outer Model - X =~ x1 + x2 +x3 - Y =~ y1 + y2 + y3 - Z =~ z1 + z2 + z3 - - # Inner model - Y ~ X + Z + X:Z -' -pt <- modsemify(m1) -trace_path(pt, x = "Y", y = "Y", missing.cov = TRUE) # variance of Y -} diff --git a/man/var_interactions.Rd b/man/var_interactions.Rd deleted file mode 100644 index f018303..0000000 --- a/man/var_interactions.Rd +++ /dev/null @@ -1,17 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/generics.R -\name{var_interactions} -\alias{var_interactions} -\title{Extract or modify parTable from an estimated model with estimated variances of interaction terms} -\usage{ -var_interactions(object, ...) -} -\arguments{ -\item{object}{An object of class `modsem_da`, `modsem_mplus`, -or a parTable of class `data.frame`} - -\item{...}{Additional arguments passed to other functions} -} -\description{ -Extract or modify parTable from an estimated model with estimated variances of interaction terms -} diff --git a/man/vcov_modsem_da.Rd b/man/vcov_modsem_da.Rd deleted file mode 100644 index f722ee0..0000000 --- a/man/vcov_modsem_da.Rd +++ /dev/null @@ -1,17 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/generics_modsem_da.R -\name{vcov_modsem_da} -\alias{vcov_modsem_da} -\title{Wrapper for vcov} -\usage{ -vcov_modsem_da(object, ...) -} -\arguments{ -\item{object}{fittet model to inspect} - -\item{...}{additional arguments} -} -\description{ -wrapper for vcov, to be used with modsem::vcov_modsem_da, -since vcov is not in the namespace of modsem, but stats -} diff --git a/pkgdown.js b/pkgdown.js new file mode 100644 index 0000000..9757bf9 --- /dev/null +++ b/pkgdown.js @@ -0,0 +1,154 @@ +/* http://gregfranko.com/blog/jquery-best-practices/ */ +(function($) { + $(function() { + + $('nav.navbar').headroom(); + + Toc.init({ + $nav: $("#toc"), + $scope: $("main h2, main h3, main h4, main h5, main h6") + }); + + if ($('#toc').length) { + $('body').scrollspy({ + target: '#toc', + offset: $("nav.navbar").outerHeight() + 1 + }); + } + + // Activate popovers + $('[data-bs-toggle="popover"]').popover({ + container: 'body', + html: true, + trigger: 'focus', + placement: "top", + sanitize: false, + }); + + $('[data-bs-toggle="tooltip"]').tooltip(); + + /* Clipboard --------------------------*/ + + function changeTooltipMessage(element, msg) { + var tooltipOriginalTitle=element.getAttribute('data-bs-original-title'); + element.setAttribute('data-bs-original-title', msg); + $(element).tooltip('show'); + element.setAttribute('data-bs-original-title', tooltipOriginalTitle); + } + + if(ClipboardJS.isSupported()) { + $(document).ready(function() { + var copyButton = ""; + + $("div.sourceCode").addClass("hasCopyButton"); + + // Insert copy buttons: + $(copyButton).prependTo(".hasCopyButton"); + + // Initialize tooltips: + $('.btn-copy-ex').tooltip({container: 'body'}); + + // Initialize clipboard: + var clipboard = new ClipboardJS('[data-clipboard-copy]', { + text: function(trigger) { + return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, ""); + } + }); + + clipboard.on('success', function(e) { + changeTooltipMessage(e.trigger, 'Copied!'); + e.clearSelection(); + }); + + clipboard.on('error', function(e) { + changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); + }); + + }); + } + + /* Search marking --------------------------*/ + var url = new URL(window.location.href); + var toMark = url.searchParams.get("q"); + var mark = new Mark("main#main"); + if (toMark) { + mark.mark(toMark, { + accuracy: { + value: "complementary", + limiters: [",", ".", ":", "/"], + } + }); + } + + /* Search --------------------------*/ + /* Adapted from https://github.com/rstudio/bookdown/blob/2d692ba4b61f1e466c92e78fd712b0ab08c11d31/inst/resources/bs4_book/bs4_book.js#L25 */ + // Initialise search index on focus + var fuse; + $("#search-input").focus(async function(e) { + if (fuse) { + return; + } + + $(e.target).addClass("loading"); + var response = await fetch($("#search-input").data("search-index")); + var data = await response.json(); + + var options = { + keys: ["what", "text", "code"], + ignoreLocation: true, + threshold: 0.1, + includeMatches: true, + includeScore: true, + }; + fuse = new Fuse(data, options); + + $(e.target).removeClass("loading"); + }); + + // Use algolia autocomplete + var options = { + autoselect: true, + debug: true, + hint: false, + minLength: 2, + }; + var q; +async function searchFuse(query, callback) { + await fuse; + + var items; + if (!fuse) { + items = []; + } else { + q = query; + var results = fuse.search(query, { limit: 20 }); + items = results + .filter((x) => x.score <= 0.75) + .map((x) => x.item); + if (items.length === 0) { + items = [{dir:"Sorry 😿",previous_headings:"",title:"No results found.",what:"No results found.",path:window.location.href}]; + } + } + callback(items); +} + $("#search-input").autocomplete(options, [ + { + name: "content", + source: searchFuse, + templates: { + suggestion: (s) => { + if (s.title == s.what) { + return `${s.dir} >
    ${s.title}
    `; + } else if (s.previous_headings == "") { + return `${s.dir} >
    ${s.title}
    > ${s.what}`; + } else { + return `${s.dir} >
    ${s.title}
    > ${s.previous_headings} > ${s.what}`; + } + }, + }, + }, + ]).on('autocomplete:selected', function(event, s) { + window.location.href = s.path + "?q=" + q + "#" + s.id; + }); + }); +})(window.jQuery || window.$) diff --git a/pkgdown.yml b/pkgdown.yml new file mode 100644 index 0000000..2e75d63 --- /dev/null +++ b/pkgdown.yml @@ -0,0 +1,14 @@ +pandoc: 3.1.8 +pkgdown: 2.1.0 +pkgdown_sha: ~ +articles: + customizing: customizing.html + interaction_two_etas: interaction_two_etas.html + lavaan: lavaan.html + lms_qml: lms_qml.html + methods: methods.html + modsem: modsem.html + observed_lms_qml: observed_lms_qml.html + plot_interactions: plot_interactions.html + quadratic: quadratic.html +last_built: 2024-09-09T19:31Z diff --git a/reference/Rplot001.png b/reference/Rplot001.png new file mode 100644 index 0000000..17a3580 Binary files /dev/null and b/reference/Rplot001.png differ diff --git a/reference/TPB.html b/reference/TPB.html new file mode 100644 index 0000000..2d052c9 --- /dev/null +++ b/reference/TPB.html @@ -0,0 +1,90 @@ + +TPB — TPB • modsem + Skip to contents + + +
    +
    +
    + +

    TPB

    + Source: R/datasets.R +
    TPB.Rd
    +
    + +
    +

    A simulated dataset based on the Theory of Planned Behaviour

    +
    + + + +
    +

    Examples

    + 
    
+tpb <- ' 
+# Outer Model (Based on Hagger et al., 2007)
+  ATT =~ att1 + att2 + att3 + att4 + att5
+  SN =~ sn1 + sn2
+  PBC =~ pbc1 + pbc2 + pbc3
+  INT =~ int1 + int2 + int3
+  BEH =~ b1 + b2
+
+# Inner Model (Based on Steinmetz et al., 2011)
+  INT ~ ATT + SN + PBC
+  BEH ~ INT + PBC + INT:PBC  
+'
+
+est <- modsem(tpb, data = TPB)
+
    +
    +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/TPB_UK.html b/reference/TPB_UK.html new file mode 100644 index 0000000..cfc021e --- /dev/null +++ b/reference/TPB_UK.html @@ -0,0 +1,104 @@ + +TPB_UK — TPB_UK • modsem + Skip to contents + + +
    +
    +
    + +

    TPB_UK

    + Source: R/datasets.R +
    TPB_UK.Rd
    +
    + +
    +

    A dataset based on the Theory of Planned Behaviour from a +UK sample. 4 variables with high communality were selected for each +latent variable (ATT, SN, PBC, INT, BEH), from two time points (t1 and t2).

    +
    + + +
    +

    Source

    +

    Gathered from a replciation study of the original by Hagger et al. (2023). +Obtained from https://doi.org/10.23668/psycharchives.12187

    +
    + +
    +

    Examples

    + 
    
+tpb_uk <- ' 
+# Outer Model (Based on Hagger et al., 2007)
+ ATT =~ att3 + att2 + att1 + att4
+ SN =~ sn4 + sn2 + sn3 + sn1
+ PBC =~ pbc2 + pbc1 + pbc3 + pbc4
+ INT =~ int2 + int1 + int3 + int4
+ BEH =~ beh3 + beh2 + beh1 + beh4
+
+# Inner Model (Based on Steinmetz et al., 2011)
+ # Causal Relationsships
+ INT ~ ATT + SN + PBC
+ BEH ~ INT + PBC 
+ BEH ~ INT:PBC  
+'
+
+est <- modsem(tpb_uk, data = TPB_UK)
+
    +
    +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/coef_modsem_da.html b/reference/coef_modsem_da.html new file mode 100644 index 0000000..d815352 --- /dev/null +++ b/reference/coef_modsem_da.html @@ -0,0 +1,91 @@ + +Wrapper for coef — coef_modsem_da • modsem + Skip to contents + + +
    +
    +
    + +

    Wrapper for coef

    + Source: R/generics_modsem_da.R +
    coef_modsem_da.Rd
    +
    + +
    +

    wrapper for coef, to be used with modsem::coef_modsem_da, +since coef is not in the namespace of modsem, but stats

    +
    + +
    +

    Usage

    + 
    coef_modsem_da(object, ...)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    fittet model to inspect

    + + +
    ...
    +

    additional arguments

    + +
    + +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/compare_fit.html b/reference/compare_fit.html new file mode 100644 index 0000000..d33dd73 --- /dev/null +++ b/reference/compare_fit.html @@ -0,0 +1,134 @@ + +compare model fit for qml and lms models — compare_fit • modsem + Skip to contents + + +
    +
    +
    + +

    compare model fit for qml and lms models

    + Source: R/generics_modsem_da.R +
    compare_fit.Rd
    +
    + +
    +

    Compare the fit of two models using the likelihood ratio test. +`estH0` representing the null +hypothesis model, and `estH1` the alternative hypothesis model. Importantly, +the function assumes that `estH0` does not have more free parameters +(i.e., degrees of freedom) than `estH1`. +alternative hypothesis model

    +
    + +
    +

    Usage

    + 
    compare_fit(estH0, estH1)
    +
    + +
    +

    Arguments

    + + +
    estH0
    +

    object of class `modsem_da` representing the +null hypothesis model

    + + +
    estH1
    +

    object of class `modsem_da` representing the

    + +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+H0 <- "
+ # Outer Model
+ X =~ x1 + x2 + x3
+ Y =~ y1 + y2 + y3
+ Z =~ z1 + z2 + z3
+
+ # Inner model
+ Y ~ X + Z
+"
+
+estH0 <- modsem(m1, oneInt, "lms")
+
+H1 <- "
+ # Outer Model
+ X =~ x1 + x2 + x3
+ Y =~ y1 + y2 + y3
+ Z =~ z1 + z2 + z3
+
+ # Inner model
+ Y ~ X + Z + X:Z
+"
+
+estH1 <- modsem(m1, oneInt, "lms")
+compare_fit(estH0, estH1)
+} # }
+
    +
    +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/default_settings_da.html b/reference/default_settings_da.html new file mode 100644 index 0000000..65baee0 --- /dev/null +++ b/reference/default_settings_da.html @@ -0,0 +1,236 @@ + +default arguments fro LMS and QML approach — default_settings_da • modsem + Skip to contents + + +
    +
    +
    + +

    default arguments fro LMS and QML approach

    + Source: R/method_settings_da.R +
    default_settings_da.Rd
    +
    + +
    +

    This function returns the default settings for the LMS and QML approach.

    +
    + +
    +

    Usage

    + 
    default_settings_da(method = c("lms", "qml"))
    +
    + +
    +

    Arguments

    + + +
    method
    +

    which method to get the settings for

    + +
    +
    +

    Value

    +

    list

    +
    + +
    +

    Examples

    + 
    library(modsem)
+default_settings_da()
+#> $lms
+#> $lms$verbose
+#> [1] FALSE
+#> 
+#> $lms$optimize
+#> [1] TRUE
+#> 
+#> $lms$nodes
+#> [1] 24
+#> 
+#> $lms$convergence
+#> [1] 1e-04
+#> 
+#> $lms$optimizer
+#> [1] "nlminb"
+#> 
+#> $lms$center.data
+#> [1] FALSE
+#> 
+#> $lms$standardize.data
+#> [1] FALSE
+#> 
+#> $lms$standardize.out
+#> [1] FALSE
+#> 
+#> $lms$standardize
+#> [1] FALSE
+#> 
+#> $lms$mean.observed
+#> [1] TRUE
+#> 
+#> $lms$double
+#> [1] FALSE
+#> 
+#> $lms$calc.se
+#> [1] TRUE
+#> 
+#> $lms$FIM
+#> [1] "expected"
+#> 
+#> $lms$OFIM.hessian
+#> [1] FALSE
+#> 
+#> $lms$EFIM.S
+#> [1] 30000
+#> 
+#> $lms$EFIM.parametric
+#> [1] TRUE
+#> 
+#> $lms$robust.se
+#> [1] FALSE
+#> 
+#> $lms$max.iter
+#> [1] 500
+#> 
+#> $lms$max.step
+#> [1] 1
+#> 
+#> $lms$fix.estep
+#> [1] TRUE
+#> 
+#> $lms$epsilon
+#> [1] 1e-04
+#> 
+#> $lms$quad.range
+#> [1] Inf
+#> 
+#> $lms$n.threads
+#> NULL
+#> 
+#> 
+#> $qml
+#> $qml$verbose
+#> [1] FALSE
+#> 
+#> $qml$optimize
+#> [1] TRUE
+#> 
+#> $qml$nodes
+#> [1] 0
+#> 
+#> $qml$convergence
+#> [1] 1e-06
+#> 
+#> $qml$optimizer
+#> [1] "nlminb"
+#> 
+#> $qml$center.data
+#> [1] FALSE
+#> 
+#> $qml$standardize
+#> [1] FALSE
+#> 
+#> $qml$standardize.data
+#> [1] FALSE
+#> 
+#> $qml$standardize.out
+#> [1] FALSE
+#> 
+#> $qml$mean.observed
+#> [1] TRUE
+#> 
+#> $qml$double
+#> [1] FALSE
+#> 
+#> $qml$calc.se
+#> [1] TRUE
+#> 
+#> $qml$FIM
+#> [1] "observed"
+#> 
+#> $qml$OFIM.hessian
+#> [1] TRUE
+#> 
+#> $qml$EFIM.S
+#> [1] 30000
+#> 
+#> $qml$EFIM.parametric
+#> [1] TRUE
+#> 
+#> $qml$robust.se
+#> [1] FALSE
+#> 
+#> $qml$max.iter
+#> [1] 500
+#> 
+#> $qml$max.step
+#> NULL
+#> 
+#> $qml$fix.estep
+#> NULL
+#> 
+#> $qml$epsilon
+#> [1] 1e-08
+#> 
+#> $qml$quad.range
+#> [1] Inf
+#> 
+#> $qml$n.threads
+#> NULL
+#> 
+#> 
+
    +
    +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/default_settings_pi.html b/reference/default_settings_pi.html new file mode 100644 index 0000000..93e5cc8 --- /dev/null +++ b/reference/default_settings_pi.html @@ -0,0 +1,239 @@ + +default arguments for product indicator approaches — default_settings_pi • modsem + Skip to contents + + +
    +
    +
    + +

    default arguments for product indicator approaches

    + Source: R/method_settings_pi.R +
    default_settings_pi.Rd
    +
    + +
    +

    This function returns the default settings for the product indicator approaches

    +
    + +
    +

    Usage

    + 
    default_settings_pi(method = c("rca", "uca", "pind", "dblcent", "ca"))
    +
    + +
    +

    Arguments

    + + +
    method
    +

    which method to get the settings for

    + +
    +
    +

    Value

    +

    list

    +
    + +
    +

    Examples

    + 
    library(modsem)
+default_settings_pi()
+#> $rca
+#> $rca$center.before
+#> [1] FALSE
+#> 
+#> $rca$center.after
+#> [1] FALSE
+#> 
+#> $rca$residuals.prods
+#> [1] TRUE
+#> 
+#> $rca$residual.cov.syntax
+#> [1] TRUE
+#> 
+#> $rca$constrained.prod.mean
+#> [1] FALSE
+#> 
+#> $rca$constrained.loadings
+#> [1] FALSE
+#> 
+#> $rca$constrained.var
+#> [1] FALSE
+#> 
+#> $rca$constrained.res.cov.method
+#> [1] "simple"
+#> 
+#> $rca$match
+#> [1] FALSE
+#> 
+#> 
+#> $uca
+#> $uca$center.before
+#> [1] TRUE
+#> 
+#> $uca$center.after
+#> [1] FALSE
+#> 
+#> $uca$residuals.prods
+#> [1] FALSE
+#> 
+#> $uca$residual.cov.syntax
+#> [1] TRUE
+#> 
+#> $uca$constrained.prod.mean
+#> [1] TRUE
+#> 
+#> $uca$constrained.loadings
+#> [1] FALSE
+#> 
+#> $uca$constrained.var
+#> [1] FALSE
+#> 
+#> $uca$constrained.res.cov.method
+#> [1] "simple"
+#> 
+#> $uca$match
+#> [1] FALSE
+#> 
+#> 
+#> $pind
+#> $pind$center.before
+#> [1] FALSE
+#> 
+#> $pind$center.after
+#> [1] FALSE
+#> 
+#> $pind$residuals.prods
+#> [1] FALSE
+#> 
+#> $pind$residual.cov.syntax
+#> [1] FALSE
+#> 
+#> $pind$constrained.prod.mean
+#> [1] FALSE
+#> 
+#> $pind$constrained.loadings
+#> [1] FALSE
+#> 
+#> $pind$constrained.var
+#> [1] FALSE
+#> 
+#> $pind$constrained.res.cov.method
+#> [1] "simple"
+#> 
+#> $pind$match
+#> [1] FALSE
+#> 
+#> 
+#> $dblcent
+#> $dblcent$center.before
+#> [1] TRUE
+#> 
+#> $dblcent$center.after
+#> [1] TRUE
+#> 
+#> $dblcent$residuals.prods
+#> [1] FALSE
+#> 
+#> $dblcent$residual.cov.syntax
+#> [1] TRUE
+#> 
+#> $dblcent$constrained.prod.mean
+#> [1] FALSE
+#> 
+#> $dblcent$constrained.loadings
+#> [1] FALSE
+#> 
+#> $dblcent$constrained.var
+#> [1] FALSE
+#> 
+#> $dblcent$constrained.res.cov.method
+#> [1] "simple"
+#> 
+#> $dblcent$match
+#> [1] FALSE
+#> 
+#> 
+#> $ca
+#> $ca$center.before
+#> [1] TRUE
+#> 
+#> $ca$center.after
+#> [1] FALSE
+#> 
+#> $ca$residuals.prods
+#> [1] FALSE
+#> 
+#> $ca$residual.cov.syntax
+#> [1] TRUE
+#> 
+#> $ca$constrained.prod.mean
+#> [1] TRUE
+#> 
+#> $ca$constrained.loadings
+#> [1] TRUE
+#> 
+#> $ca$constrained.var
+#> [1] TRUE
+#> 
+#> $ca$constrained.res.cov.method
+#> [1] "ca"
+#> 
+#> $ca$match
+#> [1] TRUE
+#> 
+#> 
+
    +
    +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/extract_lavaan.html b/reference/extract_lavaan.html new file mode 100644 index 0000000..8866b6c --- /dev/null +++ b/reference/extract_lavaan.html @@ -0,0 +1,104 @@ + +extract lavaan object from modsem object estimated using product indicators — extract_lavaan • modsem + Skip to contents + + +
    +
    +
    + +

    extract lavaan object from modsem object estimated using product indicators

    + Source: R/modsem_pi.R +
    extract_lavaan.Rd
    +
    + +
    +

    extract lavaan object from modsem object estimated using product indicators

    +
    + +
    +

    Usage

    + 
    extract_lavaan(object)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    modsem object

    + +
    +
    +

    Value

    +

    lavaan object

    +
    + +
    +

    Examples

    + 
    library(modsem)
+m1 <- '
+  # Outer Model
+  X =~ x1 + x2 + x3
+  Y =~ y1 + y2 + y3
+  Z =~ z1 + z2 + z3
+  
+  # Inner model
+  Y ~ X + Z + X:Z 
+'
+est <- modsem_pi(m1, oneInt)
+lav_est <- extract_lavaan(est) 
+
    +
    +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/man/figures/modsem.png b/reference/figures/modsem.png similarity index 100% rename from man/figures/modsem.png rename to reference/figures/modsem.png diff --git a/reference/fit_modsem_da.html b/reference/fit_modsem_da.html new file mode 100644 index 0000000..c16252a --- /dev/null +++ b/reference/fit_modsem_da.html @@ -0,0 +1,97 @@ + +Fit measures for QML and LMS models — fit_modsem_da • modsem + Skip to contents + + +
    +
    +
    + +

    Fit measures for QML and LMS models

    + Source: R/fit_modsem_da.R +
    fit_modsem_da.Rd
    +
    + +
    +

    Calculates chi-sq test and p-value, as well as RMSEA for +the LMS and QML models. Note that the Chi-Square based fit measures should be calculated +for the baseline model, i.e., the model without the interaction effect

    +
    + +
    +

    Usage

    + 
    fit_modsem_da(model, chisq = TRUE)
    +
    + +
    +

    Arguments

    + + +
    model
    +

    fitted model. Thereafter, you can use 'compare_fit()' +to assess the comparative fit of the models. If the interaction effect makes +the model better, and e.g., the RMSEA is good for the baseline model, +the interaction model likely has a good RMSEA as well.

    + + +
    chisq
    +

    should Chi-Square based fit-measures be calculated?

    + +
    + +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/get_pi_data.html b/reference/get_pi_data.html new file mode 100644 index 0000000..8c9e359 --- /dev/null +++ b/reference/get_pi_data.html @@ -0,0 +1,132 @@ + +Get data with product indicators for different approaches — get_pi_data • modsem + Skip to contents + + +
    +
    +
    + +

    Get data with product indicators for different approaches

    + Source: R/modsem_pi.R +
    get_pi_data.Rd
    +
    + +
    +

    get_pi_syntax is a function for creating the lavaan syntax used for estimating +latent interaction models using one of the product indiactors in lavaan.

    +
    + +
    +

    Usage

    + 
    get_pi_data(model.syntax, data, method = "dblcent", match = FALSE, ...)
    +
    + +
    +

    Arguments

    + + +
    model.syntax
    +

    lavaan syntax

    + + +
    data
    +

    data to create product indicators from

    + + +
    method
    +

    method to use: +"rca" = residual centering approach, +"uca" = unconstrained approach, +"dblcent" = double centering approach, +"pind" = prod ind approach, with no constraints or centering, +"custom" = use parameters specified in the function call

    + + +
    match
    +

    should product indicators be made using the match strategy

    + + +
    ...
    +

    arguments passed to other functions (e.g., modsem_pi)

    + +
    +
    +

    Value

    +

    data.frame

    +
    + +
    +

    Examples

    + 
    library(modsem)
+library(lavaan)
+#> This is lavaan 0.6-18
+#> lavaan is FREE software! Please report any bugs.
+m1 <- '
+  # Outer Model
+  X =~ x1 + x2 +x3
+  Y =~ y1 + y2 + y3
+  Z =~ z1 + z2 + z3
+  
+  # Inner model
+  Y ~ X + Z + X:Z 
+'
+syntax <- get_pi_syntax(m1)
+data <- get_pi_data(m1, oneInt)
+est <- sem(syntax, data)
+
    +
    +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/get_pi_syntax.html b/reference/get_pi_syntax.html new file mode 100644 index 0000000..c3a31d8 --- /dev/null +++ b/reference/get_pi_syntax.html @@ -0,0 +1,126 @@ + +Get lavaan syntax for product indicator approaches — get_pi_syntax • modsem + Skip to contents + + +
    +
    +
    + +

    Get lavaan syntax for product indicator approaches

    + Source: R/modsem_pi.R +
    get_pi_syntax.Rd
    +
    + +
    +

    get_pi_syntax is a function for creating the lavaan syntax used for estimating +latent interaction models using one of the product indiactors in lavaan.

    +
    + +
    +

    Usage

    + 
    get_pi_syntax(model.syntax, method = "dblcent", match = FALSE, ...)
    +
    + +
    +

    Arguments

    + + +
    model.syntax
    +

    lavaan syntax

    + + +
    method
    +

    method to use: +"rca" = residual centering approach, +"uca" = unconstrained approach, +"dblcent" = double centering approach, +"pind" = prod ind approach, with no constraints or centering, +"custom" = use parameters specified in the function call

    + + +
    match
    +

    should product indicators be made using the match strategy

    + + +
    ...
    +

    arguments passed to other functions (e.g., modsem_pi)

    + +
    +
    +

    Value

    +

    character vector

    +
    + +
    +

    Examples

    + 
    library(modsem)
+library(lavaan)
+m1 <- '
+  # Outer Model
+  X =~ x1 + x2 +x3
+  Y =~ y1 + y2 + y3
+  Z =~ z1 + z2 + z3
+  
+  # Inner model
+  Y ~ X + Z + X:Z 
+'
+syntax <- get_pi_syntax(m1)
+data <- get_pi_data(m1, oneInt)
+est <- sem(syntax, data)
+
    +
    +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/index.html b/reference/index.html new file mode 100644 index 0000000..5271dec --- /dev/null +++ b/reference/index.html @@ -0,0 +1,231 @@ + +Package index • modsem + Skip to contents + + +
    +
    +
    + +

    Package index

    +
    + +
    +

    All functions

    + + + + +
    + + + + +
    + + TPB + +
    +
    TPB
    +
    + + TPB_UK + +
    +
    TPB_UK
    +
    + + coef_modsem_da() + +
    +
    Wrapper for coef
    +
    + + compare_fit() + +
    +
    compare model fit for qml and lms models
    +
    + + default_settings_da() + +
    +
    default arguments fro LMS and QML approach
    +
    + + default_settings_pi() + +
    +
    default arguments for product indicator approaches
    +
    + + extract_lavaan() + +
    +
    extract lavaan object from modsem object estimated using product indicators
    +
    + + fit_modsem_da() + +
    +
    Fit measures for QML and LMS models
    +
    + + get_pi_data() + +
    +
    Get data with product indicators for different approaches
    +
    + + get_pi_syntax() + +
    +
    Get lavaan syntax for product indicator approaches
    +
    + + jordan + +
    +
    Jordan subset of PISA 2006 data
    +
    + + modsem() + +
    +
    Interaction between latent variables
    +
    + + modsem_da() + +
    +
    Interaction between latent variables using lms and qml approaches
    +
    + + modsem_inspect() + +
    +
    Inspect model information
    +
    + + modsem_mplus() + +
    +
    Estimation latent interactions through mplus
    +
    + + modsem_pi() + +
    +
    Interaction between latent variables using product indicators
    +
    + + modsemify() + +
    +
    Generate parameter table for lavaan syntax
    +
    + + multiplyIndicatorsCpp() + +
    +
    Multiply indicators
    +
    + + oneInt + +
    +
    oneInt
    +
    + + parameter_estimates() + +
    +
    Extract parameterEstimates from an estimated model
    +
    + + plot_interaction() + +
    +
    Plot Interaction Effects
    +
    + + standardized_estimates() + +
    +
    Get standardized estimates
    +
    + + summary(<modsem_da>) summary(<modsem_mplus>) summary(<modsem_pi>) + +
    +
    summary for modsem objects
    +
    + + trace_path() + +
    +
    Estimate formulas for (co-)variance paths using Wright's path tracing rules
    +
    + + var_interactions() + +
    +
    Extract or modify parTable from an estimated model with estimated variances of interaction terms
    +
    + + vcov_modsem_da() + +
    +
    Wrapper for vcov
    +
    +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/jordan.html b/reference/jordan.html new file mode 100644 index 0000000..8444903 --- /dev/null +++ b/reference/jordan.html @@ -0,0 +1,147 @@ + +Jordan subset of PISA 2006 data — jordan • modsem + Skip to contents + + +
    +
    +
    + +

    Jordan subset of PISA 2006 data

    + Source: R/datasets.R +
    jordan.Rd
    +
    + +
    +

    The data stem from the large-scale assessment study PISA 2006 +(Organisation for Economic Co-Operation and Development, 2009) where +competencies of 15-year-old students in reading, mathematics, and science +are assessed using nationally representative samples in 3-year cycles. +In this eacademicample, data from the student background questionnaire from the +Jordan sample of PISA 2006 were used. Only data of students with complete +responses to all 15 items (N = 6,038) were considered.

    +
    + + +
    +

    Format

    +

    A data frame of fifteen variables and 6,038 observations:

    +

    enjoy1 +indicator for enjoyment of science, item ST16Q01: I generally have fun when I am learning <broad science> topics.

    +

    enjoy2 +indicator for enjoyment of science, item ST16Q02: I like reading about <broad science>.

    +

    enjoy3 +indicator for enjoyment of science, item ST16Q03: I am happy doing <broad science> problems.

    +

    enjoy4 +indicator for enjoyment of science, item ST16Q04: I enjoy acquiring new knowledge in <broad science>.

    +

    enjoy5 +indicator for enjoyment of science, item ST16Q05: I am interested in learning about <broad science>.

    +

    academic1 +indicator for academic self-concept in science, item ST37Q01: I can easily understand new ideas in <school science>.

    +

    academic2 +indicator for academic self-concept in science, item ST37Q02: Learning advanced <school science> topics would be easy for me.

    +

    academic3 +indicator for academic self-concept in science, item ST37Q03: I can usually give good answers to <test questions> on <school science> topics.

    +

    academic4 +indicator for academic self-concept in science, item ST37Q04: I learn <school science> topics quickly.

    +

    academic5 +indicator for academic self-concept in science, item ST37Q05: <School science> topics are easy for me.

    +

    academic6 +indicator for academic self-concept in science, item ST37Q06: When I am being taught <school science>, I can understand the concepts very well.

    +

    career1 +indicator for career aspirations in science, item ST29Q01: I would like to work in a career involving <broad science>.

    +

    career2 +indicator for career aspirations in science, item ST29Q02: I would like to study <broad science> after <secondary school>.

    +

    career3 +indicator for career aspirations in science, item ST29Q03: I would like to spend my life doing advanced <broad science>.

    +

    career4 +indicator for career aspirations in science, item ST29Q04: I would like to work on <broad science> projects as an adult.

    +
    +
    +

    Source

    +

    This version of the dataset, as well as the description was gathered from the +documentation of the 'nlsem' package (https://cran.r-project.org/package=nlsem), +where the only difference is that the names of the variables were changed

    +

    Originally the dataset was gathered by the Organisation for Economic Co-Operation and Development (2009). +Pisa 2006: Science competencies for tomorrow's world (Tech. Rep.). +Paris, France. Obtained from: https://www.oecd.org/pisa/pisaproducts/database-pisa2006.htm

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+m1 <- '
+  ENJ =~ enjoy1 + enjoy2 + enjoy3 + enjoy4 + enjoy5
+  CAREER =~ career1 + career2 + career3 + career4
+  SC =~ academic1 + academic2 + academic3 + academic4 + academic5 + academic6
+  CAREER ~ ENJ + SC + ENJ:ENJ + SC:SC + ENJ:SC
+'
+
+est <- modsem(m1, data = jordan)
+} # }
+
    +
    +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/modsem-package.html b/reference/modsem-package.html new file mode 100644 index 0000000..b239286 --- /dev/null +++ b/reference/modsem-package.html @@ -0,0 +1,102 @@ + +modsem: Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM) — modsem-package • modsem + Skip to contents + + +
    +
    +
    + +

    modsem: Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM)

    + Source: R/modsem-package.R +
    modsem-package.Rd
    +
    + +
    +

    Estimation of interaction (i.e., moderation) effects between latent variables in structural equation models (SEM). The supported methods are: The constrained approach (Algina & Moulder, 2001). The unconstrained approach (Marsh et al., 2004). The residual centering approach (Little et al., 2006). The double centering approach (Lin et al., 2010). The latent moderated structural equations (LMS) approach (Klein & Moosbrugger, 2000). The quasi-maximum likelihood (QML) approach (Klein & Muthén, 2007) (temporarily unavailable) The constrained- unconstrained, residual- and double centering- approaches are estimated via 'lavaan' (Rosseel, 2012), whilst the LMS- and QML- approaches are estimated via by modsem it self. Alternatively model can be estimated via 'Mplus' (Muthén & Muthén, 1998-2017). References: Algina, J., & Moulder, B. C. (2001). doi:10.1207/S15328007SEM0801_3 +. "A note on estimating the Jöreskog-Yang model for latent variable interaction using 'LISREL' 8.3." Klein, A., & Moosbrugger, H. (2000). doi:10.1007/BF02296338 +. "Maximum likelihood estimation of latent interaction effects with the LMS method." Klein, A. G., & Muthén, B. O. (2007). doi:10.1080/00273170701710205 +. "Quasi-maximum likelihood estimation of structural equation models with multiple interaction and quadratic effects." Lin, G. C., Wen, Z., Marsh, H. W., & Lin, H. S. (2010). doi:10.1080/10705511.2010.488999 +. "Structural equation models of latent interactions: Clarification of orthogonalizing and double-mean-centering strategies." Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). doi:10.1207/s15328007sem1304_1 +. "On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables." Marsh, H. W., Wen, Z., & Hau, K. T. (2004). doi:10.1037/1082-989X.9.3.275 +. "Structural equation models of latent interactions: evaluation of alternative estimation strategies and indicator construction." Muthén, L.K. and Muthén, B.O. (1998-2017). "'Mplus' User’s Guide. Eighth Edition." https://www.statmodel.com/. Rosseel Y (2012). doi:10.18637/jss.v048.i02 +. "'lavaan': An R Package for Structural Equation Modeling."

    +
    + + +
    +

    See also

    +

    Useful links:

    +
    +
    +

    Author

    +

    Maintainer: Kjell Solem Slupphaug slupphaugkjell@gmail.com (ORCID)

    +
    + +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/modsem.html b/reference/modsem.html new file mode 100644 index 0000000..e02f4b7 --- /dev/null +++ b/reference/modsem.html @@ -0,0 +1,525 @@ + +Interaction between latent variables — modsem • modsem + Skip to contents + + +
    +
    +
    + +

    Interaction between latent variables

    + Source: R/modsem-package.R, R/modsem.R +
    modsem.Rd
    +
    + +
    +

    modsem is a function for estimating interaction effects between latent variables, +in structural equation models (SEM's). +Methods for estimating interaction effects in SEM's can basically be split into +two frameworks: 1. Product Indicator based approaches ("dblcent", "rca", "uca", +"ca", "pind"), and 2. Distributionally based approaches ("lms", "qml"). +For the product indicator based approaces, modsem() is essentially a just +a fancy wrapper for lavaan::sem() which generates the +necessary syntax, and variables for the estimation of models with latent product indicators. +The distributionally based approaches are implemented in seperately, and are +are not estimated using lavaan::sem(), but rather using custom functions (largely) +written in C++ for performance reasons. For greater control, it is advised that +you use one of the sub-functions (modsem_pi, modsem_da, modsem_mplus) directly, +as passing additional arguments to them via modsem() can lead to unexpected behavior.

    +
    + +
    +

    Usage

    + 
    modsem(model.syntax = NULL, data = NULL, method = "dblcent", ...)
    +
    + +
    +

    Arguments

    + + +
    model.syntax
    +

    lavaan syntax

    + + +
    data
    +

    dataframe

    + + +
    method
    +

    method to use: +"rca" = residual centering approach (passed to lavaan), +"uca" = unconstrained approach (passed to lavaan), +"dblcent" = double centering approach (passed to lavaan), +"pind" = prod ind approach, with no constraints or centering (passed to lavaan), +"lms" = laten model structural equations (not passed to lavaan). +"qml" = quasi maximum likelihood estimation of laten model structural equations (not passed to lavaan). +"custom" = use parameters specified in the function call (passed to lavaan)

    + + +
    ...
    +

    arguments passed to other functions depending on method (see modsem_pi, modsem_da, and modsem_mplus)

    + +
    +
    +

    Value

    +

    modsem object

    +
    + +
    +

    Examples

    + 
    library(modsem)
+# For more examples check README and/or GitHub.
+# One interaction
+m1 <- '
+  # Outer Model
+  X =~ x1 + x2 +x3
+  Y =~ y1 + y2 + y3
+  Z =~ z1 + z2 + z3
+  
+  # Inner model
+  Y ~ X + Z + X:Z 
+'
+
+# Double centering approach
+est1 <- modsem(m1, oneInt)
+summary(est1)
+#> modsem: 
+#> Method = dblcent 
+#> lavaan 0.6-18 ended normally after 159 iterations
+#> 
+#>   Estimator                                         ML
+#>   Optimization method                           NLMINB
+#>   Number of model parameters                        60
+#> 
+#>   Number of observations                          2000
+#> 
+#> Model Test User Model:
+#>                                                       
+#>   Test statistic                               122.924
+#>   Degrees of freedom                               111
+#>   P-value (Chi-square)                           0.207
+#> 
+#> Parameter Estimates:
+#> 
+#>   Standard errors                             Standard
+#>   Information                                 Expected
+#>   Information saturated (h1) model          Structured
+#> 
+#> Latent Variables:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>   X =~                                                
+#>     x1                1.000                           
+#>     x2                0.804    0.013   63.612    0.000
+#>     x3                0.916    0.014   67.144    0.000
+#>   Y =~                                                
+#>     y1                1.000                           
+#>     y2                0.798    0.007  107.428    0.000
+#>     y3                0.899    0.008  112.453    0.000
+#>   Z =~                                                
+#>     z1                1.000                           
+#>     z2                0.812    0.013   64.763    0.000
+#>     z3                0.882    0.013   67.014    0.000
+#>   XZ =~                                               
+#>     x1z1              1.000                           
+#>     x2z1              0.805    0.013   60.636    0.000
+#>     x3z1              0.877    0.014   62.680    0.000
+#>     x1z2              0.793    0.013   59.343    0.000
+#>     x2z2              0.646    0.015   43.672    0.000
+#>     x3z2              0.706    0.016   44.292    0.000
+#>     x1z3              0.887    0.014   63.700    0.000
+#>     x2z3              0.716    0.016   45.645    0.000
+#>     x3z3              0.781    0.017   45.339    0.000
+#> 
+#> Regressions:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>   Y ~                                                 
+#>     X                 0.675    0.027   25.379    0.000
+#>     Z                 0.561    0.026   21.606    0.000
+#>     XZ                0.702    0.027   26.360    0.000
+#> 
+#> Covariances:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>  .x1z1 ~~                                             
+#>    .x2z2              0.000                           
+#>    .x2z3              0.000                           
+#>    .x3z2              0.000                           
+#>    .x3z3              0.000                           
+#>  .x2z1 ~~                                             
+#>    .x1z2              0.000                           
+#>  .x1z2 ~~                                             
+#>    .x2z3              0.000                           
+#>  .x3z1 ~~                                             
+#>    .x1z2              0.000                           
+#>  .x1z2 ~~                                             
+#>    .x3z3              0.000                           
+#>  .x2z1 ~~                                             
+#>    .x1z3              0.000                           
+#>  .x2z2 ~~                                             
+#>    .x1z3              0.000                           
+#>  .x3z1 ~~                                             
+#>    .x1z3              0.000                           
+#>  .x3z2 ~~                                             
+#>    .x1z3              0.000                           
+#>  .x2z1 ~~                                             
+#>    .x3z2              0.000                           
+#>    .x3z3              0.000                           
+#>  .x3z1 ~~                                             
+#>    .x2z2              0.000                           
+#>  .x2z2 ~~                                             
+#>    .x3z3              0.000                           
+#>  .x3z1 ~~                                             
+#>    .x2z3              0.000                           
+#>  .x3z2 ~~                                             
+#>    .x2z3              0.000                           
+#>  .x1z1 ~~                                             
+#>    .x1z2              0.115    0.008   14.802    0.000
+#>    .x1z3              0.114    0.008   13.947    0.000
+#>    .x2z1              0.125    0.008   16.095    0.000
+#>    .x3z1              0.140    0.009   16.135    0.000
+#>  .x1z2 ~~                                             
+#>    .x1z3              0.103    0.007   14.675    0.000
+#>    .x2z2              0.128    0.006   20.850    0.000
+#>    .x3z2              0.146    0.007   21.243    0.000
+#>  .x1z3 ~~                                             
+#>    .x2z3              0.116    0.007   17.818    0.000
+#>    .x3z3              0.135    0.007   18.335    0.000
+#>  .x2z1 ~~                                             
+#>    .x2z2              0.135    0.006   20.905    0.000
+#>    .x2z3              0.145    0.007   21.145    0.000
+#>    .x3z1              0.114    0.007   16.058    0.000
+#>  .x2z2 ~~                                             
+#>    .x2z3              0.117    0.006   20.419    0.000
+#>    .x3z2              0.116    0.006   20.586    0.000
+#>  .x2z3 ~~                                             
+#>    .x3z3              0.109    0.006   18.059    0.000
+#>  .x3z1 ~~                                             
+#>    .x3z2              0.138    0.007   19.331    0.000
+#>    .x3z3              0.158    0.008   20.269    0.000
+#>  .x3z2 ~~                                             
+#>    .x3z3              0.131    0.007   19.958    0.000
+#>   X ~~                                                
+#>     Z                 0.201    0.024    8.271    0.000
+#>     XZ                0.016    0.025    0.628    0.530
+#>   Z ~~                                                
+#>     XZ                0.062    0.025    2.449    0.014
+#> 
+#> Variances:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>    .x1                0.160    0.009   17.871    0.000
+#>    .x2                0.162    0.007   22.969    0.000
+#>    .x3                0.163    0.008   20.161    0.000
+#>    .y1                0.159    0.009   17.896    0.000
+#>    .y2                0.154    0.007   22.640    0.000
+#>    .y3                0.164    0.008   20.698    0.000
+#>    .z1                0.168    0.009   18.143    0.000
+#>    .z2                0.158    0.007   22.264    0.000
+#>    .z3                0.158    0.008   20.389    0.000
+#>    .x1z1              0.311    0.014   22.227    0.000
+#>    .x2z1              0.292    0.011   27.287    0.000
+#>    .x3z1              0.327    0.012   26.275    0.000
+#>    .x1z2              0.290    0.011   26.910    0.000
+#>    .x2z2              0.239    0.008   29.770    0.000
+#>    .x3z2              0.270    0.009   29.117    0.000
+#>    .x1z3              0.272    0.012   23.586    0.000
+#>    .x2z3              0.245    0.009   27.979    0.000
+#>    .x3z3              0.297    0.011   28.154    0.000
+#>     X                 0.981    0.036   26.895    0.000
+#>    .Y                 0.990    0.038   25.926    0.000
+#>     Z                 1.016    0.038   26.856    0.000
+#>     XZ                1.045    0.044   24.004    0.000
+#> 
+
+if (FALSE) { # \dontrun{
+# The Constrained Approach 
+est1_ca <- modsem(m1, oneInt, method = "ca")
+summary(est1_ca)
+
+# LMS approach
+est1_lms <- modsem(m1, oneInt, method = "lms")
+summary(est1_lms)
+
+# QML approach
+est1_qml <- modsem(m1, oneInt, method = "qml")
+summary(est1_qml)
+
+} # }
+
+# Theory Of Planned Behavior
+tpb <- ' 
+# Outer Model (Based on Hagger et al., 2007)
+  ATT =~ att1 + att2 + att3 + att4 + att5
+  SN =~ sn1 + sn2
+  PBC =~ pbc1 + pbc2 + pbc3
+  INT =~ int1 + int2 + int3
+  BEH =~ b1 + b2
+
+# Inner Model (Based on Steinmetz et al., 2011)
+  INT ~ ATT + SN + PBC
+  BEH ~ INT + PBC 
+  BEH ~ INT:PBC  
+'
+
+# double centering approach
+est_tpb <- modsem(tpb, data = TPB)
+summary(est_tpb)
+#> modsem: 
+#> Method = dblcent 
+#> lavaan 0.6-18 ended normally after 171 iterations
+#> 
+#>   Estimator                                         ML
+#>   Optimization method                           NLMINB
+#>   Number of model parameters                        78
+#> 
+#>   Number of observations                          2000
+#> 
+#> Model Test User Model:
+#>                                                       
+#>   Test statistic                               207.615
+#>   Degrees of freedom                               222
+#>   P-value (Chi-square)                           0.747
+#> 
+#> Parameter Estimates:
+#> 
+#>   Standard errors                             Standard
+#>   Information                                 Expected
+#>   Information saturated (h1) model          Structured
+#> 
+#> Latent Variables:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>   ATT =~                                              
+#>     att1              1.000                           
+#>     att2              0.878    0.012   71.509    0.000
+#>     att3              0.789    0.012   66.368    0.000
+#>     att4              0.695    0.011   61.017    0.000
+#>     att5              0.887    0.013   70.884    0.000
+#>   SN =~                                               
+#>     sn1               1.000                           
+#>     sn2               0.889    0.017   52.553    0.000
+#>   PBC =~                                              
+#>     pbc1              1.000                           
+#>     pbc2              0.912    0.013   69.500    0.000
+#>     pbc3              0.801    0.012   65.830    0.000
+#>   INT =~                                              
+#>     int1              1.000                           
+#>     int2              0.914    0.016   58.982    0.000
+#>     int3              0.808    0.015   55.547    0.000
+#>   BEH =~                                              
+#>     b1                1.000                           
+#>     b2                0.960    0.030   31.561    0.000
+#>   INTPBC =~                                           
+#>     int1pbc1          1.000                           
+#>     int2pbc1          0.931    0.015   63.809    0.000
+#>     int3pbc1          0.774    0.013   60.107    0.000
+#>     int1pbc2          0.893    0.013   68.173    0.000
+#>     int2pbc2          0.826    0.017   48.845    0.000
+#>     int3pbc2          0.690    0.015   45.300    0.000
+#>     int1pbc3          0.799    0.012   67.008    0.000
+#>     int2pbc3          0.738    0.015   47.809    0.000
+#>     int3pbc3          0.622    0.014   45.465    0.000
+#> 
+#> Regressions:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>   INT ~                                               
+#>     ATT               0.213    0.026    8.170    0.000
+#>     SN                0.177    0.028    6.416    0.000
+#>     PBC               0.217    0.030    7.340    0.000
+#>   BEH ~                                               
+#>     INT               0.191    0.024    7.817    0.000
+#>     PBC               0.230    0.022   10.507    0.000
+#>     INTPBC            0.204    0.018   11.425    0.000
+#> 
+#> Covariances:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>  .int1pbc1 ~~                                         
+#>    .int2pbc2          0.000                           
+#>    .int2pbc3          0.000                           
+#>    .int3pbc2          0.000                           
+#>    .int3pbc3          0.000                           
+#>  .int2pbc1 ~~                                         
+#>    .int1pbc2          0.000                           
+#>  .int1pbc2 ~~                                         
+#>    .int2pbc3          0.000                           
+#>  .int3pbc1 ~~                                         
+#>    .int1pbc2          0.000                           
+#>  .int1pbc2 ~~                                         
+#>    .int3pbc3          0.000                           
+#>  .int2pbc1 ~~                                         
+#>    .int1pbc3          0.000                           
+#>  .int2pbc2 ~~                                         
+#>    .int1pbc3          0.000                           
+#>  .int3pbc1 ~~                                         
+#>    .int1pbc3          0.000                           
+#>  .int3pbc2 ~~                                         
+#>    .int1pbc3          0.000                           
+#>  .int2pbc1 ~~                                         
+#>    .int3pbc2          0.000                           
+#>    .int3pbc3          0.000                           
+#>  .int3pbc1 ~~                                         
+#>    .int2pbc2          0.000                           
+#>  .int2pbc2 ~~                                         
+#>    .int3pbc3          0.000                           
+#>  .int3pbc1 ~~                                         
+#>    .int2pbc3          0.000                           
+#>  .int3pbc2 ~~                                         
+#>    .int2pbc3          0.000                           
+#>  .int1pbc1 ~~                                         
+#>    .int1pbc2          0.126    0.009   14.768    0.000
+#>    .int1pbc3          0.102    0.007   13.794    0.000
+#>    .int2pbc1          0.104    0.007   14.608    0.000
+#>    .int3pbc1          0.091    0.006   14.109    0.000
+#>  .int1pbc2 ~~                                         
+#>    .int1pbc3          0.095    0.007   13.852    0.000
+#>    .int2pbc2          0.128    0.007   19.320    0.000
+#>    .int3pbc2          0.119    0.006   19.402    0.000
+#>  .int1pbc3 ~~                                         
+#>    .int2pbc3          0.110    0.006   19.911    0.000
+#>    .int3pbc3          0.097    0.005   19.415    0.000
+#>  .int2pbc1 ~~                                         
+#>    .int2pbc2          0.152    0.008   18.665    0.000
+#>    .int2pbc3          0.138    0.007   18.779    0.000
+#>    .int3pbc1          0.082    0.006   13.951    0.000
+#>  .int2pbc2 ~~                                         
+#>    .int2pbc3          0.121    0.007   18.361    0.000
+#>    .int3pbc2          0.104    0.005   19.047    0.000
+#>  .int2pbc3 ~~                                         
+#>    .int3pbc3          0.087    0.005   19.180    0.000
+#>  .int3pbc1 ~~                                         
+#>    .int3pbc2          0.139    0.007   21.210    0.000
+#>    .int3pbc3          0.123    0.006   21.059    0.000
+#>  .int3pbc2 ~~                                         
+#>    .int3pbc3          0.114    0.005   21.021    0.000
+#>   ATT ~~                                              
+#>     SN                0.629    0.029   21.977    0.000
+#>     PBC               0.678    0.029   23.721    0.000
+#>     INTPBC            0.086    0.024    3.519    0.000
+#>   SN ~~                                               
+#>     PBC               0.678    0.029   23.338    0.000
+#>     INTPBC            0.055    0.025    2.230    0.026
+#>   PBC ~~                                              
+#>     INTPBC            0.087    0.024    3.609    0.000
+#> 
+#> Variances:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>    .att1              0.167    0.007   23.528    0.000
+#>    .att2              0.150    0.006   24.693    0.000
+#>    .att3              0.160    0.006   26.378    0.000
+#>    .att4              0.163    0.006   27.649    0.000
+#>    .att5              0.159    0.006   24.930    0.000
+#>    .sn1               0.178    0.015   12.110    0.000
+#>    .sn2               0.156    0.012   13.221    0.000
+#>    .pbc1              0.145    0.008   18.440    0.000
+#>    .pbc2              0.160    0.007   21.547    0.000
+#>    .pbc3              0.154    0.007   23.716    0.000
+#>    .int1              0.158    0.009   18.152    0.000
+#>    .int2              0.160    0.008   20.345    0.000
+#>    .int3              0.167    0.007   23.414    0.000
+#>    .b1                0.186    0.018   10.058    0.000
+#>    .b2                0.135    0.017    8.080    0.000
+#>    .int1pbc1          0.266    0.013   20.971    0.000
+#>    .int2pbc1          0.292    0.012   24.421    0.000
+#>    .int3pbc1          0.251    0.010   26.305    0.000
+#>    .int1pbc2          0.290    0.012   24.929    0.000
+#>    .int2pbc2          0.269    0.010   26.701    0.000
+#>    .int3pbc2          0.253    0.009   29.445    0.000
+#>    .int1pbc3          0.223    0.009   24.431    0.000
+#>    .int2pbc3          0.234    0.008   27.633    0.000
+#>    .int3pbc3          0.203    0.007   29.288    0.000
+#>     ATT               0.998    0.037   27.138    0.000
+#>     SN                0.987    0.039   25.394    0.000
+#>     PBC               0.962    0.035   27.260    0.000
+#>    .INT               0.490    0.020   24.638    0.000
+#>    .BEH               0.455    0.023   20.068    0.000
+#>     INTPBC            1.020    0.041   24.612    0.000
+#> 
+
+if (FALSE) { # \dontrun{
+# The Constrained Approach 
+est_tpb_ca <- modsem(tpb, data = TPB, method = "ca")
+summary(est_tpb_ca)
+
+# LMS approach
+est_tpb_lms <- modsem(tpb, data = TPB, method = "lms")
+summary(est_tpb_lms)
+
+# QML approach
+est_tpb_qml <- modsem(tpb, data = TPB, method = "qml")
+summary(est_tpb_qml)
+} # }
+
    +
    +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/modsem_da.html b/reference/modsem_da.html new file mode 100644 index 0000000..0e85493 --- /dev/null +++ b/reference/modsem_da.html @@ -0,0 +1,333 @@ + +Interaction between latent variables using lms and qml approaches — modsem_da • modsem + Skip to contents + + +
    +
    +
    + +

    Interaction between latent variables using lms and qml approaches

    + Source: R/modsem_da.R +
    modsem_da.Rd
    +
    + +
    +

    modsem_da is a function for estimating interaction effects between latent variables, +in structural equation models (SEMs), using distributional analytic (DA) approaches. +Methods for estimating interaction effects in SEM's can basically be split into +two frameworks: 1. Product Indicator based approaches ("dblcent", "rca", "uca", +"ca", "pind"), and 2. Distributionally based approaches ("lms", "qml"). +modsem_da() handles the latter, and can estimate models using both qml and lms +necessary syntax, and variables for the estimation of models with latent product indicators. +NOTE: run 'default_settings_da()' to see default arguments.

    +
    + +
    +

    Usage

    + 
    modsem_da(
+  model.syntax = NULL,
+  data = NULL,
+  method = "lms",
+  verbose = NULL,
+  optimize = NULL,
+  nodes = NULL,
+  convergence = NULL,
+  optimizer = NULL,
+  center.data = NULL,
+  standardize.data = NULL,
+  standardize.out = NULL,
+  standardize = NULL,
+  mean.observed = NULL,
+  cov.syntax = NULL,
+  double = NULL,
+  calc.se = NULL,
+  FIM = NULL,
+  EFIM.S = NULL,
+  OFIM.hessian = NULL,
+  EFIM.parametric = NULL,
+  robust.se = NULL,
+  max.iter = NULL,
+  max.step = NULL,
+  fix.estep = NULL,
+  start = NULL,
+  epsilon = NULL,
+  quad.range = NULL,
+  n.threads = NULL,
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    model.syntax
    +

    lavaan syntax

    + + +
    data
    +

    dataframe

    + + +
    method
    +

    method to use: +"lms" = laten model structural equations (not passed to lavaan). +"qml" = quasi maximum likelihood estimation of laten model structural equations (not passed to lavaan).

    + + +
    verbose
    +

    should estimation progress be shown

    + + +
    optimize
    +

    should starting parameters be optimized

    + + +
    nodes
    +

    number of quadrature nodes (points of integration) used in lms, +increased number gives better estimates but slower computation. How many is needed, depends on the complexity of the model +For simple models you somwhere between 16-24 should be enough, for more complex higher numbers may be needed. +For models where there is an interaction effects between and endogenous and exogenous variable +the number of nodes should at least be 32, but practically (e.g., ordinal/skewed data) more than 32 is recommended. In cases, +where data is non-normal it might be better to use the qml approach instead. For large +numbers of nodes, you might want to change the 'quad.range' argument.

    + + +
    convergence
    +

    convergence criterion. Lower values give better estimates but slower computation.

    + + +
    optimizer
    +

    optimizer to use, can be either "nlminb" or "L-BFGS-B". For LMS, "nlminb" is recommended. +For QML, "L-BFGS-B" may be faster if there is a large number of iterations, but slower if there are few iterations.

    + + +
    center.data
    +

    should data be centered before fitting model

    + + +
    standardize.data
    +

    should data be scaled before fitting model, will be overridden by +standardize if standardize is set to TRUE. +NOTE: It is recommended that you estimate the model normally and then standardize the output using +`standardized_estimates()`.

    + + +
    standardize.out
    +

    should output be standardized (note will alter the relationsships of +parameter constraints, since to parameters are scaled unevenly, even if they +have the same label). This does not alter the estimation of the model, only the +output. +NOTE: It is recommended that you estimate the model normally and then standardize the output using +`standardized_estimates()`.

    + + +
    standardize
    +

    will standardize the data before fitting the model, remove the mean +structure of the observed variables, and standardize the output. Note that standardize.data +mean.observed, standardize.out will be overridden by standardize if standardize is set to TRUE. +NOTE: It is recommended that you estimate the model normally and then standardize the output using +`standardized_estimates()`.

    + + +
    mean.observed
    +

    should mean structure of the observed variables be estimated, +will be overridden by standardize if standardize is set to TRUE. +NOTE: Not recommended unless you know what you are doing.

    + + +
    cov.syntax
    +

    model syntax for implied covariance matrix (see 'vignette("interaction_two_etas", "modsem")')

    + + +
    double
    +

    try to double the number of dimensions of integrations used in LMS, +this will be extremely slow, but should be more similar to mplus.

    + + +
    calc.se
    +

    should standard errros be computed, NOTE: If 'FALSE' information matrix will not be computed either

    + + +
    FIM
    +

    should fisher information matrix be calculated using observed of expected. must be either "observed" or "expected"

    + + +
    EFIM.S
    +

    if expected fisher information matrix is computed, EFIM.S selects the sample size of the generated data

    + + +
    OFIM.hessian
    +

    should observed fisher information be computed using hessian? if FALSE, it is computed using gradient

    + + +
    EFIM.parametric
    +

    should data for calculating expected fisher information matrix be +simulated parametrically (simulated based on the assumptions- and implied parameters +from the model), or non-parametrically (stochastically sampled). If you believe that +normality assumptions are violated, 'EFIM.parametric = FALSE' might be the better option.

    + + +
    robust.se
    +

    should robust standard errors be computed? Meant to be used for QML, +can be unreliable with the LMS-approach.

    + + +
    max.iter
    +

    max numebr of iterations

    + + +
    max.step
    +

    max steps for the M-step in the EM algorithm (LMS)

    + + +
    fix.estep
    +

    if TRUE, E-step will be fixed and the prior probabilities are set to the best prior probabilities, +if loglikelihood is decreasing for more than 30 iterations.

    + + +
    start
    +

    starting parameters

    + + +
    epsilon
    +

    finite difference for numerical derivatives

    + + +
    quad.range
    +

    range in z-scores to perform numerical integration in LMS using +Gaussian-Hermite Quadratures. By default Inf, such that f(t) is integrated from -Inf to Inf, +but this will likely be inefficient and pointless at large number of nodes. Nodes outside ++/- quad.range will be ignored.

    + + +
    n.threads
    +

    number of cores to use for parallel processing, if NULL, it will use <= 2 threads, +if an integer is specified, it will use that number of threads (e.g., `n.threads = 4`, will use 4 threads) +if = "default" it will use the default number of threads (2). +if = "max" it will use all available threads, "min" will use 1 thread.

    + + +
    ...
    +

    additional arguments to be passed to the estimation function

    + +
    +
    +

    Value

    +

    modsem_da object

    +
    + +
    +

    Examples

    + 
    library(modsem)
+# For more examples check README and/or GitHub.
+# One interaction
+m1 <- "
+  # Outer Model
+  X =~ x1 + x2 +x3
+  Y =~ y1 + y2 + y3
+  Z =~ z1 + z2 + z3
+
+  # Inner model
+  Y ~ X + Z + X:Z
+"
+
+if (FALSE) { # \dontrun{
+# QML Approach
+est1 <- modsem_da(m1, oneInt, method = "qml")
+summary(est1)
+
+
+# Theory Of Planned Behavior
+tpb <- "
+# Outer Model (Based on Hagger et al., 2007)
+  ATT =~ att1 + att2 + att3 + att4 + att5
+  SN =~ sn1 + sn2
+  PBC =~ pbc1 + pbc2 + pbc3
+  INT =~ int1 + int2 + int3
+  BEH =~ b1 + b2
+
+# Inner Model (Based on Steinmetz et al., 2011)
+  # Covariances
+  ATT ~~ SN + PBC
+  PBC ~~ SN
+  # Causal Relationsships
+  INT ~ ATT + SN + PBC
+  BEH ~ INT + PBC
+  BEH ~ INT:PBC
+"
+
+# lms approach
+estTpb <- modsem_da(tpb, data = TPB, method = lms)
+summary(estTpb)
+} # }
+
+
    +
    +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/modsem_inspect.html b/reference/modsem_inspect.html new file mode 100644 index 0000000..e3d3d9d --- /dev/null +++ b/reference/modsem_inspect.html @@ -0,0 +1,102 @@ + +Inspect model information — modsem_inspect • modsem + Skip to contents + + +
    +
    +
    + +

    Inspect model information

    + Source: R/generics.R +
    modsem_inspect.Rd
    +
    + +
    +

    function used to inspect fittet object. similar to `lavInspect()` +argument 'what' decides what to inspect

    +
    + +
    +

    Usage

    + 
    modsem_inspect(object, what = NULL, ...)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    fittet model to inspect

    + + +
    what
    +

    what to inspect

    + + +
    ...
    +

    Additional arguments passed to other functions

    + +
    +
    +

    Details

    +

    for `modsem_da`, and `modsem_lavaan` +for `modsem_lavaan`, it is just a wrapper for `lavInspect()` +for `modsem_da` and “ what can either be "all", "matrices", "optim", +or just the name of what to extract.

    +
    + +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/modsem_mplus.html b/reference/modsem_mplus.html new file mode 100644 index 0000000..c1ee9b0 --- /dev/null +++ b/reference/modsem_mplus.html @@ -0,0 +1,148 @@ + +Estimation latent interactions through mplus — modsem_mplus • modsem + Skip to contents + + +
    +
    +
    + +

    Estimation latent interactions through mplus

    + Source: R/modsem_mplus.R +
    modsem_mplus.Rd
    +
    + +
    +

    Estimation latent interactions through mplus

    +
    + +
    +

    Usage

    + 
    modsem_mplus(
+  model.syntax,
+  data,
+  estimator = "ml",
+  type = "random",
+  algorithm = "integration",
+  process = "8",
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    model.syntax
    +

    lavaan/modsem syntax

    + + +
    data
    +

    dataset

    + + +
    estimator
    +

    estimator argument passed to mplus

    + + +
    type
    +

    type argument passed to mplus

    + + +
    algorithm
    +

    algorithm argument passed to mplus

    + + +
    process
    +

    process argument passed to mplus

    + + +
    ...
    +

    arguments passed to other functions

    + +
    +
    +

    Value

    +

    modsem_mplus object

    +
    + +
    +

    Examples

    + 
    # Theory Of Planned Behavior
+tpb <- ' 
+# Outer Model (Based on Hagger et al., 2007)
+  ATT =~ att1 + att2 + att3 + att4 + att5
+  SN =~ sn1 + sn2
+  PBC =~ pbc1 + pbc2 + pbc3
+  INT =~ int1 + int2 + int3
+  BEH =~ b1 + b2
+
+# Inner Model (Based on Steinmetz et al., 2011)
+  # Covariances
+  ATT ~~ SN + PBC
+  PBC ~~ SN 
+  # Causal Relationsships
+  INT ~ ATT + SN + PBC
+  BEH ~ INT + PBC 
+  BEH ~ INT:PBC  
+'
+
+if (FALSE) { # \dontrun{
+estTpbMplus <- modsem_mplus(tpb, data = TPB)
+summary(estTpbLMS)
+} # }
+
+
    +
    +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/modsem_pi.html b/reference/modsem_pi.html new file mode 100644 index 0000000..16d21c9 --- /dev/null +++ b/reference/modsem_pi.html @@ -0,0 +1,595 @@ + +Interaction between latent variables using product indicators — modsem_pi • modsem + Skip to contents + + +
    +
    +
    + +

    Interaction between latent variables using product indicators

    + Source: R/modsem_pi.R +
    modsem_pi.Rd
    +
    + +
    +

    modsem_pi is a function for estimating interaction effects between latent variables, +in structural equation models (SEMs), using product indicators. +Methods for estimating interaction effects in SEM's can basically be split into +two frameworks: 1. Product Indicator based approaches ("dblcent", "rca", "uca", +"ca", "pind"), and 2. Distributionally based approaches ("lms", "qml"). +modsem_pi() is essentially a just +a fancy wrapper for lavaan::sem() which generates the +necessary syntax, and variables for the estimation of models with latent product indicators. +use `default_settings_pi()` to get the default settings for the different methods.

    +
    + +
    +

    Usage

    + 
    modsem_pi(
+  model.syntax = NULL,
+  data = NULL,
+  method = "dblcent",
+  match = NULL,
+  standardize.data = FALSE,
+  center.data = FALSE,
+  first.loading.fixed = TRUE,
+  center.before = NULL,
+  center.after = NULL,
+  residuals.prods = NULL,
+  residual.cov.syntax = NULL,
+  constrained.prod.mean = NULL,
+  constrained.loadings = NULL,
+  constrained.var = NULL,
+  constrained.res.cov.method = NULL,
+  auto.scale = "none",
+  auto.center = "none",
+  estimator = "ML",
+  group = NULL,
+  run = TRUE,
+  suppress.warnings.lavaan = FALSE,
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    model.syntax
    +

    lavaan syntax

    + + +
    data
    +

    dataframe

    + + +
    method
    +

    method to use: +"rca" = residual centering approach (passed to lavaan), +"uca" = unconstrained approach (passed to lavaan), +"dblcent" = double centering approach (passed to lavaan), +"pind" = prod ind approach, with no constraints or centering (passed to lavaan), +"custom" = use parameters specified in the function call (passed to lavaan)

    + + +
    match
    +

    should the product indicators be created by using the match-strategy

    + + +
    standardize.data
    +

    should data be scaled before fitting model

    + + +
    center.data
    +

    should data be centered before fitting model

    + + +
    first.loading.fixed
    +

    Sould the first factorloading in the latent prod be fixed to one?

    + + +
    center.before
    +

    should inds in prods be centered before computing prods (overwritten by method, if method != NULL)

    + + +
    center.after
    +

    should ind prods be centered after they have been computed?

    + + +
    residuals.prods
    +

    should ind prods be centered using residuals (overwritten by method, if method != NULL)

    + + +
    residual.cov.syntax
    +

    should syntax for residual covariances be produced (overwritten by method, if method != NULL)

    + + +
    constrained.prod.mean
    +

    should syntax prod mean be produced (overwritten by method, if method != NULL)

    + + +
    constrained.loadings
    +

    should syntax for constrained loadings be produced (overwritten by method, if method != NULL)

    + + +
    constrained.var
    +

    should syntax for constrained variances be produced (overwritten by method, if method != NULL)

    + + +
    constrained.res.cov.method
    +

    method for constraining residual covariances

    + + +
    auto.scale
    +

    methods which should be scaled automatically (usually not useful)

    + + +
    auto.center
    +

    methods which should be centered automatically (usually not useful)

    + + +
    estimator
    +

    estimator to use in lavaan

    + + +
    group
    +

    group variable for multigroup analysis

    + + +
    run
    +

    should the model be run via lavaan, if FALSE only modified syntax and data is returned

    + + +
    suppress.warnings.lavaan
    +

    should warnings from lavaan be supressed?

    + + +
    ...
    +

    arguments passed to other functions, e.g,. lavaan

    + +
    +
    +

    Value

    +

    modsem object

    +
    + +
    +

    Examples

    + 
    library(modsem)
+# For more examples check README and/or GitHub.
+# One interaction
+m1 <- '
+  # Outer Model
+  X =~ x1 + x2 +x3
+  Y =~ y1 + y2 + y3
+  Z =~ z1 + z2 + z3
+  
+  # Inner model
+  Y ~ X + Z + X:Z 
+'
+
+# Double centering approach
+est1 <- modsem_pi(m1, oneInt)
+summary(est1)
+#> modsem: 
+#> Method = dblcent 
+#> lavaan 0.6-18 ended normally after 159 iterations
+#> 
+#>   Estimator                                         ML
+#>   Optimization method                           NLMINB
+#>   Number of model parameters                        60
+#> 
+#>   Number of observations                          2000
+#> 
+#> Model Test User Model:
+#>                                                       
+#>   Test statistic                               122.924
+#>   Degrees of freedom                               111
+#>   P-value (Chi-square)                           0.207
+#> 
+#> Parameter Estimates:
+#> 
+#>   Standard errors                             Standard
+#>   Information                                 Expected
+#>   Information saturated (h1) model          Structured
+#> 
+#> Latent Variables:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>   X =~                                                
+#>     x1                1.000                           
+#>     x2                0.804    0.013   63.612    0.000
+#>     x3                0.916    0.014   67.144    0.000
+#>   Y =~                                                
+#>     y1                1.000                           
+#>     y2                0.798    0.007  107.428    0.000
+#>     y3                0.899    0.008  112.453    0.000
+#>   Z =~                                                
+#>     z1                1.000                           
+#>     z2                0.812    0.013   64.763    0.000
+#>     z3                0.882    0.013   67.014    0.000
+#>   XZ =~                                               
+#>     x1z1              1.000                           
+#>     x2z1              0.805    0.013   60.636    0.000
+#>     x3z1              0.877    0.014   62.680    0.000
+#>     x1z2              0.793    0.013   59.343    0.000
+#>     x2z2              0.646    0.015   43.672    0.000
+#>     x3z2              0.706    0.016   44.292    0.000
+#>     x1z3              0.887    0.014   63.700    0.000
+#>     x2z3              0.716    0.016   45.645    0.000
+#>     x3z3              0.781    0.017   45.339    0.000
+#> 
+#> Regressions:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>   Y ~                                                 
+#>     X                 0.675    0.027   25.379    0.000
+#>     Z                 0.561    0.026   21.606    0.000
+#>     XZ                0.702    0.027   26.360    0.000
+#> 
+#> Covariances:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>  .x1z1 ~~                                             
+#>    .x2z2              0.000                           
+#>    .x2z3              0.000                           
+#>    .x3z2              0.000                           
+#>    .x3z3              0.000                           
+#>  .x2z1 ~~                                             
+#>    .x1z2              0.000                           
+#>  .x1z2 ~~                                             
+#>    .x2z3              0.000                           
+#>  .x3z1 ~~                                             
+#>    .x1z2              0.000                           
+#>  .x1z2 ~~                                             
+#>    .x3z3              0.000                           
+#>  .x2z1 ~~                                             
+#>    .x1z3              0.000                           
+#>  .x2z2 ~~                                             
+#>    .x1z3              0.000                           
+#>  .x3z1 ~~                                             
+#>    .x1z3              0.000                           
+#>  .x3z2 ~~                                             
+#>    .x1z3              0.000                           
+#>  .x2z1 ~~                                             
+#>    .x3z2              0.000                           
+#>    .x3z3              0.000                           
+#>  .x3z1 ~~                                             
+#>    .x2z2              0.000                           
+#>  .x2z2 ~~                                             
+#>    .x3z3              0.000                           
+#>  .x3z1 ~~                                             
+#>    .x2z3              0.000                           
+#>  .x3z2 ~~                                             
+#>    .x2z3              0.000                           
+#>  .x1z1 ~~                                             
+#>    .x1z2              0.115    0.008   14.802    0.000
+#>    .x1z3              0.114    0.008   13.947    0.000
+#>    .x2z1              0.125    0.008   16.095    0.000
+#>    .x3z1              0.140    0.009   16.135    0.000
+#>  .x1z2 ~~                                             
+#>    .x1z3              0.103    0.007   14.675    0.000
+#>    .x2z2              0.128    0.006   20.850    0.000
+#>    .x3z2              0.146    0.007   21.243    0.000
+#>  .x1z3 ~~                                             
+#>    .x2z3              0.116    0.007   17.818    0.000
+#>    .x3z3              0.135    0.007   18.335    0.000
+#>  .x2z1 ~~                                             
+#>    .x2z2              0.135    0.006   20.905    0.000
+#>    .x2z3              0.145    0.007   21.145    0.000
+#>    .x3z1              0.114    0.007   16.058    0.000
+#>  .x2z2 ~~                                             
+#>    .x2z3              0.117    0.006   20.419    0.000
+#>    .x3z2              0.116    0.006   20.586    0.000
+#>  .x2z3 ~~                                             
+#>    .x3z3              0.109    0.006   18.059    0.000
+#>  .x3z1 ~~                                             
+#>    .x3z2              0.138    0.007   19.331    0.000
+#>    .x3z3              0.158    0.008   20.269    0.000
+#>  .x3z2 ~~                                             
+#>    .x3z3              0.131    0.007   19.958    0.000
+#>   X ~~                                                
+#>     Z                 0.201    0.024    8.271    0.000
+#>     XZ                0.016    0.025    0.628    0.530
+#>   Z ~~                                                
+#>     XZ                0.062    0.025    2.449    0.014
+#> 
+#> Variances:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>    .x1                0.160    0.009   17.871    0.000
+#>    .x2                0.162    0.007   22.969    0.000
+#>    .x3                0.163    0.008   20.161    0.000
+#>    .y1                0.159    0.009   17.896    0.000
+#>    .y2                0.154    0.007   22.640    0.000
+#>    .y3                0.164    0.008   20.698    0.000
+#>    .z1                0.168    0.009   18.143    0.000
+#>    .z2                0.158    0.007   22.264    0.000
+#>    .z3                0.158    0.008   20.389    0.000
+#>    .x1z1              0.311    0.014   22.227    0.000
+#>    .x2z1              0.292    0.011   27.287    0.000
+#>    .x3z1              0.327    0.012   26.275    0.000
+#>    .x1z2              0.290    0.011   26.910    0.000
+#>    .x2z2              0.239    0.008   29.770    0.000
+#>    .x3z2              0.270    0.009   29.117    0.000
+#>    .x1z3              0.272    0.012   23.586    0.000
+#>    .x2z3              0.245    0.009   27.979    0.000
+#>    .x3z3              0.297    0.011   28.154    0.000
+#>     X                 0.981    0.036   26.895    0.000
+#>    .Y                 0.990    0.038   25.926    0.000
+#>     Z                 1.016    0.038   26.856    0.000
+#>     XZ                1.045    0.044   24.004    0.000
+#> 
+
+if (FALSE) { # \dontrun{
+# The Constrained Approach 
+est1Constrained <- modsem_pi(m1, oneInt, method = "ca")
+summary(est1Constrained)
+} # }
+
+# Theory Of Planned Behavior
+tpb <- ' 
+# Outer Model (Based on Hagger et al., 2007)
+  ATT =~ att1 + att2 + att3 + att4 + att5
+  SN =~ sn1 + sn2
+  PBC =~ pbc1 + pbc2 + pbc3
+  INT =~ int1 + int2 + int3
+  BEH =~ b1 + b2
+
+# Inner Model (Based on Steinmetz et al., 2011)
+  # Covariances
+  ATT ~~ SN + PBC
+  PBC ~~ SN 
+  # Causal Relationsships
+  INT ~ ATT + SN + PBC
+  BEH ~ INT + PBC 
+  BEH ~ INT:PBC  
+'
+
+# double centering approach
+estTpb <- modsem_pi(tpb, data = TPB)
+summary(estTpb)
+#> modsem: 
+#> Method = dblcent 
+#> lavaan 0.6-18 ended normally after 169 iterations
+#> 
+#>   Estimator                                         ML
+#>   Optimization method                           NLMINB
+#>   Number of model parameters                        78
+#> 
+#>   Number of observations                          2000
+#> 
+#> Model Test User Model:
+#>                                                       
+#>   Test statistic                               207.615
+#>   Degrees of freedom                               222
+#>   P-value (Chi-square)                           0.747
+#> 
+#> Parameter Estimates:
+#> 
+#>   Standard errors                             Standard
+#>   Information                                 Expected
+#>   Information saturated (h1) model          Structured
+#> 
+#> Latent Variables:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>   ATT =~                                              
+#>     att1              1.000                           
+#>     att2              0.878    0.012   71.509    0.000
+#>     att3              0.789    0.012   66.368    0.000
+#>     att4              0.695    0.011   61.017    0.000
+#>     att5              0.887    0.013   70.884    0.000
+#>   SN =~                                               
+#>     sn1               1.000                           
+#>     sn2               0.889    0.017   52.553    0.000
+#>   PBC =~                                              
+#>     pbc1              1.000                           
+#>     pbc2              0.912    0.013   69.500    0.000
+#>     pbc3              0.801    0.012   65.830    0.000
+#>   INT =~                                              
+#>     int1              1.000                           
+#>     int2              0.914    0.016   58.982    0.000
+#>     int3              0.808    0.015   55.547    0.000
+#>   BEH =~                                              
+#>     b1                1.000                           
+#>     b2                0.960    0.030   31.561    0.000
+#>   INTPBC =~                                           
+#>     int1pbc1          1.000                           
+#>     int2pbc1          0.931    0.015   63.809    0.000
+#>     int3pbc1          0.774    0.013   60.107    0.000
+#>     int1pbc2          0.893    0.013   68.172    0.000
+#>     int2pbc2          0.826    0.017   48.845    0.000
+#>     int3pbc2          0.690    0.015   45.300    0.000
+#>     int1pbc3          0.799    0.012   67.008    0.000
+#>     int2pbc3          0.738    0.015   47.809    0.000
+#>     int3pbc3          0.622    0.014   45.465    0.000
+#> 
+#> Regressions:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>   INT ~                                               
+#>     ATT               0.213    0.026    8.170    0.000
+#>     SN                0.177    0.028    6.416    0.000
+#>     PBC               0.217    0.030    7.340    0.000
+#>   BEH ~                                               
+#>     INT               0.191    0.024    7.817    0.000
+#>     PBC               0.230    0.022   10.507    0.000
+#>     INTPBC            0.204    0.018   11.425    0.000
+#> 
+#> Covariances:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>   ATT ~~                                              
+#>     SN                0.629    0.029   21.977    0.000
+#>     PBC               0.678    0.029   23.721    0.000
+#>   SN ~~                                               
+#>     PBC               0.678    0.029   23.338    0.000
+#>  .int1pbc1 ~~                                         
+#>    .int2pbc2          0.000                           
+#>    .int2pbc3          0.000                           
+#>    .int3pbc2          0.000                           
+#>    .int3pbc3          0.000                           
+#>  .int2pbc1 ~~                                         
+#>    .int1pbc2          0.000                           
+#>  .int1pbc2 ~~                                         
+#>    .int2pbc3          0.000                           
+#>  .int3pbc1 ~~                                         
+#>    .int1pbc2          0.000                           
+#>  .int1pbc2 ~~                                         
+#>    .int3pbc3          0.000                           
+#>  .int2pbc1 ~~                                         
+#>    .int1pbc3          0.000                           
+#>  .int2pbc2 ~~                                         
+#>    .int1pbc3          0.000                           
+#>  .int3pbc1 ~~                                         
+#>    .int1pbc3          0.000                           
+#>  .int3pbc2 ~~                                         
+#>    .int1pbc3          0.000                           
+#>  .int2pbc1 ~~                                         
+#>    .int3pbc2          0.000                           
+#>    .int3pbc3          0.000                           
+#>  .int3pbc1 ~~                                         
+#>    .int2pbc2          0.000                           
+#>  .int2pbc2 ~~                                         
+#>    .int3pbc3          0.000                           
+#>  .int3pbc1 ~~                                         
+#>    .int2pbc3          0.000                           
+#>  .int3pbc2 ~~                                         
+#>    .int2pbc3          0.000                           
+#>  .int1pbc1 ~~                                         
+#>    .int1pbc2          0.126    0.009   14.768    0.000
+#>    .int1pbc3          0.102    0.007   13.794    0.000
+#>    .int2pbc1          0.104    0.007   14.608    0.000
+#>    .int3pbc1          0.091    0.006   14.109    0.000
+#>  .int1pbc2 ~~                                         
+#>    .int1pbc3          0.095    0.007   13.852    0.000
+#>    .int2pbc2          0.128    0.007   19.320    0.000
+#>    .int3pbc2          0.119    0.006   19.402    0.000
+#>  .int1pbc3 ~~                                         
+#>    .int2pbc3          0.110    0.006   19.911    0.000
+#>    .int3pbc3          0.097    0.005   19.415    0.000
+#>  .int2pbc1 ~~                                         
+#>    .int2pbc2          0.152    0.008   18.665    0.000
+#>    .int2pbc3          0.138    0.007   18.779    0.000
+#>    .int3pbc1          0.082    0.006   13.951    0.000
+#>  .int2pbc2 ~~                                         
+#>    .int2pbc3          0.121    0.007   18.361    0.000
+#>    .int3pbc2          0.104    0.005   19.047    0.000
+#>  .int2pbc3 ~~                                         
+#>    .int3pbc3          0.087    0.005   19.180    0.000
+#>  .int3pbc1 ~~                                         
+#>    .int3pbc2          0.139    0.007   21.210    0.000
+#>    .int3pbc3          0.123    0.006   21.059    0.000
+#>  .int3pbc2 ~~                                         
+#>    .int3pbc3          0.114    0.005   21.021    0.000
+#>   ATT ~~                                              
+#>     INTPBC            0.086    0.024    3.519    0.000
+#>   SN ~~                                               
+#>     INTPBC            0.055    0.025    2.230    0.026
+#>   PBC ~~                                              
+#>     INTPBC            0.087    0.024    3.609    0.000
+#> 
+#> Variances:
+#>                    Estimate  Std.Err  z-value  P(>|z|)
+#>    .att1              0.167    0.007   23.528    0.000
+#>    .att2              0.150    0.006   24.693    0.000
+#>    .att3              0.160    0.006   26.378    0.000
+#>    .att4              0.163    0.006   27.649    0.000
+#>    .att5              0.159    0.006   24.930    0.000
+#>    .sn1               0.178    0.015   12.110    0.000
+#>    .sn2               0.156    0.012   13.221    0.000
+#>    .pbc1              0.145    0.008   18.440    0.000
+#>    .pbc2              0.160    0.007   21.547    0.000
+#>    .pbc3              0.154    0.007   23.716    0.000
+#>    .int1              0.158    0.009   18.152    0.000
+#>    .int2              0.160    0.008   20.345    0.000
+#>    .int3              0.167    0.007   23.414    0.000
+#>    .b1                0.186    0.018   10.058    0.000
+#>    .b2                0.135    0.017    8.080    0.000
+#>    .int1pbc1          0.266    0.013   20.971    0.000
+#>    .int2pbc1          0.292    0.012   24.421    0.000
+#>    .int3pbc1          0.251    0.010   26.305    0.000
+#>    .int1pbc2          0.290    0.012   24.929    0.000
+#>    .int2pbc2          0.269    0.010   26.701    0.000
+#>    .int3pbc2          0.253    0.009   29.445    0.000
+#>    .int1pbc3          0.223    0.009   24.431    0.000
+#>    .int2pbc3          0.234    0.008   27.633    0.000
+#>    .int3pbc3          0.203    0.007   29.288    0.000
+#>     ATT               0.998    0.037   27.138    0.000
+#>     SN                0.987    0.039   25.394    0.000
+#>     PBC               0.962    0.035   27.260    0.000
+#>    .INT               0.490    0.020   24.638    0.000
+#>    .BEH               0.455    0.023   20.068    0.000
+#>     INTPBC            1.020    0.041   24.612    0.000
+#> 
+
+if (FALSE) { # \dontrun{
+# The Constrained Approach 
+estTpbConstrained <- modsem_pi(tpb, data = TPB, method = "ca")
+summary(estTpbConstrained)
+} # }
+
    +
    +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/modsemify.html b/reference/modsemify.html new file mode 100644 index 0000000..9a70ce8 --- /dev/null +++ b/reference/modsemify.html @@ -0,0 +1,116 @@ + +Generate parameter table for lavaan syntax — modsemify • modsem + Skip to contents + + +
    +
    +
    + +

    Generate parameter table for lavaan syntax

    + Source: R/parser.R +
    modsemify.Rd
    +
    + +
    +

    Generate parameter table for lavaan syntax

    +
    + +
    +

    Usage

    + 
    modsemify(syntax)
    +
    + +
    +

    Arguments

    + + +
    syntax
    +

    model syntax

    + +
    +
    +

    Value

    +

    data.frame with columns lhs, op, rhs, mod

    +
    + +
    +

    Examples

    + 
    library(modsem)
+m1 <- '
+  # Outer Model
+  X =~ x1 + x2 +x3
+  Y =~ y1 + y2 + y3
+  Z =~ z1 + z2 + z3
+
+  # Inner model
+  Y ~ X + Z + X:Z
+'
+modsemify(m1)
+#>    lhs op rhs mod
+#> 1    X =~  x1    
+#> 2    X =~  x2    
+#> 3    X =~  x3    
+#> 4    Y =~  y1    
+#> 5    Y =~  y2    
+#> 6    Y =~  y3    
+#> 7    Z =~  z1    
+#> 8    Z =~  z2    
+#> 9    Z =~  z3    
+#> 10   Y  ~   X    
+#> 11   Y  ~   Z    
+#> 12   Y  ~ X:Z    
+
    +
    +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/multiplyIndicatorsCpp.html b/reference/multiplyIndicatorsCpp.html new file mode 100644 index 0000000..601e552 --- /dev/null +++ b/reference/multiplyIndicatorsCpp.html @@ -0,0 +1,88 @@ + +Multiply indicators — multiplyIndicatorsCpp • modsem + Skip to contents + + +
    +
    +
    + +

    Multiply indicators

    + Source: R/RcppExports.R +
    multiplyIndicatorsCpp.Rd
    +
    + +
    +

    Multiply indicators

    +
    + +
    +

    Usage

    + 
    multiplyIndicatorsCpp(df)
    +
    + +
    +

    Arguments

    + + +
    df
    +

    A data DataFrame

    + +
    +
    +

    Value

    +

    A NumericVector

    +
    + +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/oneInt.html b/reference/oneInt.html new file mode 100644 index 0000000..c7394c1 --- /dev/null +++ b/reference/oneInt.html @@ -0,0 +1,71 @@ + +oneInt — oneInt • modsem + Skip to contents + + +
    +
    +
    + +

    oneInt

    + Source: R/datasets.R +
    oneInt.Rd
    +
    + +
    +

    A simulated dataset with one interaction effect

    +
    + + + +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/parameter_estimates.html b/reference/parameter_estimates.html new file mode 100644 index 0000000..e2eef91 --- /dev/null +++ b/reference/parameter_estimates.html @@ -0,0 +1,88 @@ + +Extract parameterEstimates from an estimated model — parameter_estimates • modsem + Skip to contents + + +
    +
    +
    + +

    Extract parameterEstimates from an estimated model

    + Source: R/generics.R +
    parameter_estimates.Rd
    +
    + +
    +

    Extract parameterEstimates from an estimated model

    +
    + +
    +

    Usage

    + 
    parameter_estimates(object, ...)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    An object of class `modsem_pi`, `modsem_da`, or `modsem_mplus`

    + + +
    ...
    +

    Additional arguments passed to other functions

    + +
    + +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/plot_interaction.html b/reference/plot_interaction.html new file mode 100644 index 0000000..3ae8acf --- /dev/null +++ b/reference/plot_interaction.html @@ -0,0 +1,172 @@ + +Plot Interaction Effects — plot_interaction • modsem + Skip to contents + + +
    +
    +
    + +

    Plot Interaction Effects

    + Source: R/plot_interaction.R +
    plot_interaction.Rd
    +
    + +
    +

    Plot Interaction Effects

    +
    + +
    +

    Usage

    + 
    plot_interaction(
+  x,
+  z,
+  y,
+  xz = NULL,
+  vals_x = seq(-3, 3, 0.001),
+  vals_z,
+  model,
+  alpha_se = 0.15,
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    The name of the variable on the x-axis

    + + +
    z
    +

    The name of the moderator variable

    + + +
    y
    +

    The name of the outcome variable

    + + +
    xz
    +

    The name of the interaction term. If the interaction term is not specified, it +it will be created using `x` and `z`.

    + + +
    vals_x
    +

    The values of the x variable to plot, the more values the smoother the std.error-area will be

    + + +
    vals_z
    +

    The values of the moderator variable to plot. A seperate regression +line ("y ~ x | z") will be plotted for each value of the moderator variable

    + + +
    model
    +

    An object of class `modsem_pi`, `modsem_da`, or `modsem_mplus`

    + + +
    alpha_se
    +

    The alpha level for the std.error area

    + + +
    ...
    +

    Additional arguments passed to other functions

    + +
    +
    +

    Value

    +

    A ggplot object

    +
    + +
    +

    Examples

    + 
    library(modsem)
+if (FALSE) { # \dontrun{
+m1 <- "
+# Outer Model
+  X =~ x1
+  X =~ x2 + x3
+  Z =~ z1 + z2 + z3
+  Y =~ y1 + y2 + y3
+
+# Inner model
+  Y ~ X + Z + X:Z
+"
+est1 <- modsem(m1, data = oneInt)
+plot_interaction("X", "Z", "Y", "X:Z", -3:3, c(-0.2, 0), est1)
+
+tpb <- "
+# Outer Model (Based on Hagger et al., 2007)
+  ATT =~ att1 + att2 + att3 + att4 + att5
+  SN =~ sn1 + sn2
+  PBC =~ pbc1 + pbc2 + pbc3
+  INT =~ int1 + int2 + int3
+  BEH =~ b1 + b2
+
+# Inner Model (Based on Steinmetz et al., 2011)
+  # Causal Relationsships
+  INT ~ ATT + SN + PBC
+  BEH ~ INT + PBC
+  # BEH ~ ATT:PBC
+  BEH ~ PBC:INT
+  # BEH ~ PBC:PBC
+"
+
+est2 <- modsem(tpb, TPB, method = "lms")
+plot_interaction(x = "INT", z = "PBC", y = "BEH", xz = "PBC:INT", 
+                 vals_z = c(-0.5, 0.5), model = est2)
+} # }
+
    +
    +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/standardized_estimates.html b/reference/standardized_estimates.html new file mode 100644 index 0000000..92636d6 --- /dev/null +++ b/reference/standardized_estimates.html @@ -0,0 +1,102 @@ + +Get standardized estimates — standardized_estimates • modsem + Skip to contents + + +
    +
    +
    + +

    Get standardized estimates

    + Source: R/generics.R +
    standardized_estimates.Rd
    +
    + +
    +

    Get standardized estimates

    +
    + +
    +

    Usage

    + 
    standardized_estimates(object, ...)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    An object of class `modsem_da`, `modsem_mplus`, +or a parTable of class `data.frame`

    + + +
    ...
    +

    Additional arguments passed to other functions

    + +
    +
    +

    Details

    +

    for `modsem_da`, and `modsem_mplus` objects, +the interaction term is not standardized such that var(xz) = 1. +The interaction term is not an actual variable in the model, meaning that it does not +have a variance. It must therefore be calculated from the other parameters in the model. +Assuming normality and zero-means the variance is calculated as +`var(xz) = var(x) * var(z) + cov(x, z)^2`. Thus setting the variance of the interaction +term to 1, would only be 'correct' if the correlation between x and z is zero. +This means that the standardized estimates for the interaction term will +be different from those using lavaan, since there the interaction term is an +actual latent variable in the model, with a standardized variance of 1.

    +
    + +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/summary.html b/reference/summary.html new file mode 100644 index 0000000..5646a02 --- /dev/null +++ b/reference/summary.html @@ -0,0 +1,208 @@ + +summary for modsem objects — summary.modsem_da • modsem + Skip to contents + + +
    +
    +
    + +

    summary for modsem objects

    + Source: R/generics_modsem_da.R, R/generics_modsem_mplus.R, R/generics_modsem_pi.R +
    summary.Rd
    +
    + +
    +

    summary for modsem objects

    +

    summary for modsem objects

    +

    summary for modsem objects

    +
    + +
    +

    Usage

    + 
    # S3 method for class 'modsem_da'
+summary(
+  object,
+  H0 = TRUE,
+  verbose = TRUE,
+  r.squared = TRUE,
+  adjusted.stat = FALSE,
+  digits = 3,
+  scientific = FALSE,
+  ci = FALSE,
+  standardized = FALSE,
+  loadings = TRUE,
+  regressions = TRUE,
+  covariances = TRUE,
+  intercepts = TRUE,
+  variances = TRUE,
+  var.interaction = FALSE,
+  ...
+)
+
+# S3 method for class 'modsem_mplus'
+summary(
+  object,
+  scientific = FALSE,
+  standardize = FALSE,
+  ci = FALSE,
+  digits = 3,
+  loadings = TRUE,
+  regressions = TRUE,
+  covariances = TRUE,
+  intercepts = TRUE,
+  variances = TRUE,
+  ...
+)
+
+# S3 method for class 'modsem_pi'
+summary(object, ...)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    modsem object to summarized

    + + +
    H0
    +

    should a null model be estimated (used for comparison)

    + + +
    verbose
    +

    print progress for the estimation of null model

    + + +
    r.squared
    +

    calculate R-squared

    + + +
    adjusted.stat
    +

    should sample size corrected/adjustes AIC and BIC be reported?

    + + +
    digits
    +

    number of digits to print

    + + +
    scientific
    +

    print p-values in scientific notation

    + + +
    ci
    +

    print confidence intervals

    + + +
    standardized
    +

    print standardized estimates

    + + +
    loadings
    +

    print loadings

    + + +
    regressions
    +

    print regressions

    + + +
    covariances
    +

    print covariances

    + + +
    intercepts
    +

    print intercepts

    + + +
    variances
    +

    print variances

    + + +
    var.interaction
    +

    if FALSE (default) variances for interaction terms will be removed (if present)

    + + +
    ...
    +

    arguments passed to lavaan::summary()

    + + +
    standardize
    +

    standardize estimates

    + +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+m1 <- "
+ # Outer Model
+ X =~ x1 + x2 + x3
+ Y =~ y1 + y2 + y3
+ Z =~ z1 + z2 + z3
+
+ # Inner model
+ Y ~ X + Z + X:Z
+"
+
+est1 <- modsem(m1, oneInt, "qml")
+summary(est1, ci = TRUE, scientific = TRUE)
+} # }
+
    +
    +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/trace_path.html b/reference/trace_path.html new file mode 100644 index 0000000..5a075e3 --- /dev/null +++ b/reference/trace_path.html @@ -0,0 +1,154 @@ + +Estimate formulas for (co-)variance paths using Wright's path tracing rules — trace_path • modsem + Skip to contents + + +
    +
    +
    + +

    Estimate formulas for (co-)variance paths using Wright's path tracing rules

    + Source: R/trace_paths_wright.R +
    trace_path.Rd
    +
    + +
    +

    This function estimates the path from x to y using the path tracing rules, +note that it only works with structural parameters, so "=~" are ignored. unless +measurement.model = TRUE. +you want to use the measurement model, +"~" in the mod column of pt.

    +
    + +
    +

    Usage

    + 
    trace_path(
+  pt,
+  x,
+  y,
+  parenthesis = TRUE,
+  missing.cov = FALSE,
+  measurement.model = FALSE,
+  maxlen = 100,
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    pt
    +

    A data frame with columns lhs, op, rhs, and mod, from modsemify(syntax)

    + + +
    x
    +

    source variable

    + + +
    y
    +

    destination variable

    + + +
    parenthesis
    +

    if TRUE, the output will be enclosed in parenthesis

    + + +
    missing.cov
    +

    if TRUE covariances missing from the model syntax will be added

    + + +
    measurement.model
    +

    if TRUE, the function will use the measurement model

    + + +
    maxlen
    +

    maximum length of a path before aborting

    + + +
    ...
    +

    additional arguments passed to trace_path

    + +
    +
    +

    Value

    +

    A string with the estimated path (simplified if possible)

    +
    + +
    +

    Examples

    + 
    library(modsem)
+m1 <- '
+  # Outer Model
+  X =~ x1 + x2 +x3
+  Y =~ y1 + y2 + y3
+  Z =~ z1 + z2 + z3
+
+  # Inner model
+  Y ~ X + Z + X:Z
+'
+pt <- modsemify(m1)
+trace_path(pt, x = "Y", y = "Y", missing.cov = TRUE) # variance of Y
+#> [1] "(X~~X * Y~X ^ 2 + 2 * X~~Z * Y~X * Y~Z + Y~Z ^ 2 * Z~~Z + Y~~Y)"
+
    +
    +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/var_interactions.html b/reference/var_interactions.html new file mode 100644 index 0000000..ab0cb17 --- /dev/null +++ b/reference/var_interactions.html @@ -0,0 +1,89 @@ + +Extract or modify parTable from an estimated model with estimated variances of interaction terms — var_interactions • modsem + Skip to contents + + +
    +
    +
    + +

    Extract or modify parTable from an estimated model with estimated variances of interaction terms

    + Source: R/generics.R +
    var_interactions.Rd
    +
    + +
    +

    Extract or modify parTable from an estimated model with estimated variances of interaction terms

    +
    + +
    +

    Usage

    + 
    var_interactions(object, ...)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    An object of class `modsem_da`, `modsem_mplus`, +or a parTable of class `data.frame`

    + + +
    ...
    +

    Additional arguments passed to other functions

    + +
    + +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/reference/vcov_modsem_da.html b/reference/vcov_modsem_da.html new file mode 100644 index 0000000..f0dcb2f --- /dev/null +++ b/reference/vcov_modsem_da.html @@ -0,0 +1,91 @@ + +Wrapper for vcov — vcov_modsem_da • modsem + Skip to contents + + +
    +
    +
    + +

    Wrapper for vcov

    + Source: R/generics_modsem_da.R +
    vcov_modsem_da.Rd
    +
    + +
    +

    wrapper for vcov, to be used with modsem::vcov_modsem_da, +since vcov is not in the namespace of modsem, but stats

    +
    + +
    +

    Usage

    + 
    vcov_modsem_da(object, ...)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    fittet model to inspect

    + + +
    ...
    +

    additional arguments

    + +
    + +
    + + +
    +

    Developed by Kjell Solem Slupphaug.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/search.json b/search.json new file mode 100644 index 0000000..5cf670e --- /dev/null +++ b/search.json @@ -0,0 +1 @@ +[{"path":"/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2024 modsem authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"/articles/customizing.html","id":"specifying-the-measurement-model","dir":"Articles","previous_headings":"","what":"Specifying The Measurement Model","title":"customizing interaction terms","text":"default, modsem() creates possible combinations different product indicators. However, another common approach match indicators order. example, let’s say interaction laten variables X Z: ‘X =~ x1 + x2’ ‘Z =~ z1 + z2’. default get ‘XZ =~ x1z1 + x1z2 + x2z1 + x2z2’. wanted use matching approach want get ‘XZ =~ x1z1 + x2z2’ instead. achieve can use ‘match = TRUE’ argument.","code":"m2 <- ' # Outer Model X =~ x1 + x2 Y =~ y1 + y2 Z =~ z1 + z2 # Inner model Y ~ X + Z + X:Z ' est2 <- modsem(m2, oneInt, match = TRUE) summary(est2) #> modsem: #> Method = dblcent #> lavaan 0.6-18 ended normally after 41 iterations #> #> Estimator ML #> Optimization method NLMINB #> Number of model parameters 22 #> #> Number of observations 2000 #> #> Model Test User Model: #> #> Test statistic 11.355 #> Degrees of freedom 14 #> P-value (Chi-square) 0.658 #> #> Parameter Estimates: #> #> Standard errors Standard #> Information Expected #> Information saturated (h1) model Structured #> #> Latent Variables: #> Estimate Std.Err z-value P(>|z|) #> X =~ #> x1 1.000 #> x2 0.819 0.021 38.127 0.000 #> Y =~ #> y1 1.000 #> y2 0.807 0.010 82.495 0.000 #> Z =~ #> z1 1.000 #> z2 0.836 0.024 35.392 0.000 #> XZ =~ #> x1z1 1.000 #> x2z2 0.645 0.024 26.904 0.000 #> #> Regressions: #> Estimate Std.Err z-value P(>|z|) #> Y ~ #> X 0.688 0.029 23.366 0.000 #> Z 0.576 0.029 20.173 0.000 #> XZ 0.706 0.032 22.405 0.000 #> #> Covariances: #> Estimate Std.Err z-value P(>|z|) #> .x1z1 ~~ #> .x2z2 0.000 #> X ~~ #> Z 0.202 0.025 8.182 0.000 #> XZ 0.003 0.026 0.119 0.905 #> Z ~~ #> XZ 0.042 0.026 1.621 0.105 #> #> Variances: #> Estimate Std.Err z-value P(>|z|) #> .x1 0.179 0.022 8.029 0.000 #> .x2 0.151 0.015 9.956 0.000 #> .y1 0.184 0.021 8.577 0.000 #> .y2 0.136 0.014 9.663 0.000 #> .z1 0.197 0.025 7.802 0.000 #> .z2 0.138 0.018 7.831 0.000 #> .x1z1 0.319 0.035 9.141 0.000 #> .x2z2 0.244 0.016 15.369 0.000 #> X 0.962 0.042 23.120 0.000 #> .Y 0.964 0.042 23.110 0.000 #> Z 0.987 0.044 22.260 0.000 #> XZ 1.041 0.054 19.441 0.000"},{"path":"/articles/customizing.html","id":"more-complicated-models","dir":"Articles","previous_headings":"","what":"More complicated models","title":"customizing interaction terms","text":"want even control can use get_pi_syntax() get_pi_data() functions, can extract modified syntax data modsem, alter accordingly. can particularly useful cases want estimate model using feature lavaan, isn’t available modsem. example, (yet) syntax ordered- multigroup models isn’t flexible lavaan. Thus can modify auto-generated syntax (altered dataset) modsem suit needs.","code":"m3 <- ' # Outer Model X =~ x1 + x2 Y =~ y1 + y2 Z =~ z1 + z2 # Inner model Y ~ X + Z + X:Z ' syntax <- get_pi_syntax(m3) cat(syntax) #> X =~ x1 #> X =~ x2 #> Y =~ y1 #> Y =~ y2 #> Z =~ z1 #> Z =~ z2 #> Y ~ X #> Y ~ Z #> Y ~ XZ #> XZ =~ 1*x1z1 #> XZ =~ x2z1 #> XZ =~ x1z2 #> XZ =~ x2z2 #> x1z1 ~~ 0*x2z2 #> x1z2 ~~ 0*x2z1 #> x1z1 ~~ x1z2 #> x1z1 ~~ x2z1 #> x1z2 ~~ x2z2 #> x2z1 ~~ x2z2 data <- get_pi_data(m3, oneInt) head(data) #> x1 x2 y1 y2 z1 z2 x1z1 #> 1 2.4345722 1.3578655 1.4526897 0.9560888 0.8184825 1.60708140 -0.4823019 #> 2 0.2472734 0.2723201 0.5496756 0.7115311 3.6649148 2.60983102 -2.2680403 #> 3 -1.3647759 -0.5628205 -0.9835467 -0.6697747 1.7249386 2.10981827 -1.9137416 #> 4 3.0432836 2.2153763 6.4641465 4.7805981 2.5697116 3.26335379 2.9385205 #> 5 2.8148841 2.7029616 2.2860280 2.1457643 0.3467850 0.07164577 -1.4009548 #> 6 -0.5453450 -0.7530642 1.1294876 1.1998472 -0.2362958 0.60252657 1.7465860 #> x2z1 x1z2 x2z2 #> 1 -0.1884837 0.3929380 -0.0730934 #> 2 -2.6637694 -1.2630544 -1.4547433 #> 3 -1.4299711 -2.3329864 -1.7383407 #> 4 1.3971422 3.9837389 1.9273102 #> 5 -1.1495704 -2.2058995 -1.8169042 #> 6 2.2950753 0.7717365 1.0568143"},{"path":"/articles/interaction_two_etas.html","id":"the-problem","dir":"Articles","previous_headings":"","what":"The Problem","title":"interaction effects between endogenous variables","text":"Interaction effects two endogenous (.e., dependent) variables work expect product indicator methods (\"dblcent\", \"rca\", \"ca\", \"uca\"). lms- qml approach however, straight forward. lms- qml approach can (default) handle interaction effects endogenous exogenous (.e., independent) variables, interaction effects two endogenous variables. interaction effect two endogenous variables, equations easily written ‘reduced’ form – meaning normal estimation procedures won’t work.","code":""},{"path":"/articles/interaction_two_etas.html","id":"the-solution","dir":"Articles","previous_headings":"","what":"The Solution","title":"interaction effects between endogenous variables","text":"said, work-around limitations lms- qml-approach. essence, model can split two parts, one linear one non-linear. Basically, can replace covariance matrix used estimation non-linear model, model-implied covariance matrix linear model. Thus can treat endogenous variable exogenous – given can expressed linear model.","code":""},{"path":"/articles/interaction_two_etas.html","id":"example","dir":"Articles","previous_headings":"","what":"Example","title":"interaction effects between endogenous variables","text":"Let’s consider theory planned behaviour (TPB) wish estimate quadratic effect INT BEH (INT:INT). following model: Since INT endogenous variable, quadratic term (.e., interaction effect ) include two endogenous variables. Thus ordinarily able estimate model using lms- qml-approach. However, can split model two parts, one linear one non-linear. INT endogenous variable, can expressed linear model – since affected interaction terms: remove part original model, giving us: now just estimate non-linear model, since INT now exogenous variable. however incorporate structural model INT. address , can make modsem replace covariance matrix (phi) (INT, PBC, ATT, SN) model-implied covariance matrix linear model, whilst estimating models simultaneously. acheive , can use cov.syntax argument modsem:","code":"tpb <- ' # Outer Model (Based on Hagger et al., 2007) ATT =~ att1 + att2 + att3 + att4 + att5 SN =~ sn1 + sn2 PBC =~ pbc1 + pbc2 + pbc3 INT =~ int1 + int2 + int3 BEH =~ b1 + b2 # Inner Model (Based on Steinmetz et al., 2011) INT ~ ATT + SN + PBC BEH ~ INT + PBC BEH ~ INT:INT ' tpb_linear <- 'INT ~ PBC + ATT + SN' tpb_nonlinear <- ' # Outer Model (Based on Hagger et al., 2007) ATT =~ att1 + att2 + att3 + att4 + att5 SN =~ sn1 + sn2 PBC =~ pbc1 + pbc2 + pbc3 INT =~ int1 + int2 + int3 BEH =~ b1 + b2 # Inner Model (Based on Steinmetz et al., 2011) BEH ~ INT + PBC BEH ~ INT:INT ' est_lms <- modsem(tpb_nonlinear, data = TPB, cov.syntax = tpb_linear, method = \"lms\") #> Warning: It is recommended that you have at least 48 nodes for interaction #> effects between endogenous variables in the lms approach 'nodes = 24' summary(est_lms) #> Estimating null model #> EM: Iteration = 1, LogLik = -28467.33, Change = -28467.332 #> EM: Iteration = 2, LogLik = -28124.48, Change = 342.852 #> EM: Iteration = 3, LogLik = -27825.10, Change = 299.377 #> EM: Iteration = 4, LogLik = -27581.12, Change = 243.980 #> EM: Iteration = 5, LogLik = -27370.69, Change = 210.431 #> EM: Iteration = 6, LogLik = -27175.43, Change = 195.264 #> EM: Iteration = 7, LogLik = -27000.48, Change = 174.946 #> EM: Iteration = 8, LogLik = -26848.56, Change = 151.919 #> EM: Iteration = 9, LogLik = -26711.51, Change = 137.051 #> EM: Iteration = 10, LogLik = -26592.54, Change = 118.968 #> EM: Iteration = 11, LogLik = -26504.04, Change = 88.504 #> EM: Iteration = 12, LogLik = -26466.85, Change = 37.190 #> EM: Iteration = 13, LogLik = -26452.38, Change = 14.465 #> EM: Iteration = 14, LogLik = -26439.05, Change = 13.331 #> EM: Iteration = 15, LogLik = -26430.51, Change = 8.541 #> EM: Iteration = 16, LogLik = -26422.81, Change = 7.698 #> EM: Iteration = 17, LogLik = -26403.89, Change = 18.924 #> EM: Iteration = 18, LogLik = -26402.25, Change = 1.642 #> EM: Iteration = 19, LogLik = -26401.21, Change = 1.037 #> EM: Iteration = 20, LogLik = -26400.26, Change = 0.955 #> EM: Iteration = 21, LogLik = -26399.30, Change = 0.960 #> EM: Iteration = 22, LogLik = -26398.64, Change = 0.658 #> EM: Iteration = 23, LogLik = -26398.02, Change = 0.615 #> EM: Iteration = 24, LogLik = -26397.74, Change = 0.278 #> EM: Iteration = 25, LogLik = -26397.33, Change = 0.420 #> EM: Iteration = 26, LogLik = -26397.20, Change = 0.128 #> EM: Iteration = 27, LogLik = -26396.77, Change = 0.425 #> EM: Iteration = 28, LogLik = -26396.48, Change = 0.292 #> EM: Iteration = 29, LogLik = -26396.34, Change = 0.145 #> EM: Iteration = 30, LogLik = -26396.31, Change = 0.022 #> EM: Iteration = 31, LogLik = -26395.95, Change = 0.365 #> EM: Iteration = 32, LogLik = -26395.73, Change = 0.215 #> EM: Iteration = 33, LogLik = -26395.68, Change = 0.055 #> EM: Iteration = 34, LogLik = -26395.37, Change = 0.304 #> EM: Iteration = 35, LogLik = -26395.30, Change = 0.079 #> EM: Iteration = 36, LogLik = -26395.10, Change = 0.198 #> EM: Iteration = 37, LogLik = -26395.04, Change = 0.057 #> EM: Iteration = 38, LogLik = -26394.99, Change = 0.054 #> EM: Iteration = 39, LogLik = -26394.96, Change = 0.028 #> EM: Iteration = 40, LogLik = -26394.93, Change = 0.031 #> EM: Iteration = 41, LogLik = -26394.91, Change = 0.019 #> EM: Iteration = 42, LogLik = -26394.89, Change = 0.023 #> EM: Iteration = 43, LogLik = -26394.87, Change = 0.015 #> EM: Iteration = 44, LogLik = -26394.85, Change = 0.019 #> EM: Iteration = 45, LogLik = -26394.84, Change = 0.013 #> EM: Iteration = 46, LogLik = -26394.82, Change = 0.018 #> EM: Iteration = 47, LogLik = -26394.81, Change = 0.012 #> EM: Iteration = 48, LogLik = -26394.79, Change = 0.018 #> EM: Iteration = 49, LogLik = -26394.78, Change = 0.013 #> EM: Iteration = 50, LogLik = -26394.76, Change = 0.020 #> EM: Iteration = 51, LogLik = -26394.74, Change = 0.015 #> EM: Iteration = 52, LogLik = -26394.72, Change = 0.028 #> EM: Iteration = 53, LogLik = -26394.69, Change = 0.022 #> EM: Iteration = 54, LogLik = -26394.63, Change = 0.062 #> EM: Iteration = 55, LogLik = -26394.58, Change = 0.057 #> EM: Iteration = 56, LogLik = -26394.29, Change = 0.284 #> EM: Iteration = 57, LogLik = -26394.04, Change = 0.248 #> EM: Iteration = 58, LogLik = -26393.97, Change = 0.075 #> EM: Iteration = 59, LogLik = -26393.73, Change = 0.240 #> EM: Iteration = 60, LogLik = -26393.72, Change = 0.011 #> EM: Iteration = 61, LogLik = -26393.71, Change = 0.013 #> EM: Iteration = 62, LogLik = -26393.70, Change = 0.005 #> EM: Iteration = 63, LogLik = -26393.69, Change = 0.008 #> EM: Iteration = 64, LogLik = -26393.69, Change = 0.003 #> EM: Iteration = 65, LogLik = -26393.68, Change = 0.007 #> EM: Iteration = 66, LogLik = -26393.68, Change = 0.003 #> EM: Iteration = 67, LogLik = -26393.67, Change = 0.007 #> EM: Iteration = 68, LogLik = -26393.67, Change = 0.003 #> EM: Iteration = 69, LogLik = -26393.66, Change = 0.006 #> EM: Iteration = 70, LogLik = -26393.66, Change = 0.002 #> EM: Iteration = 71, LogLik = -26393.66, Change = 0.006 #> EM: Iteration = 72, LogLik = -26393.65, Change = 0.002 #> EM: Iteration = 73, LogLik = -26393.65, Change = 0.005 #> EM: Iteration = 74, LogLik = -26393.65, Change = 0.002 #> EM: Iteration = 75, LogLik = -26393.64, Change = 0.005 #> EM: Iteration = 76, LogLik = -26393.64, Change = 0.002 #> EM: Iteration = 77, LogLik = -26393.63, Change = 0.004 #> EM: Iteration = 78, LogLik = -26393.63, Change = 0.002 #> EM: Iteration = 79, LogLik = -26393.63, Change = 0.004 #> EM: Iteration = 80, LogLik = -26393.63, Change = 0.002 #> EM: Iteration = 81, LogLik = -26393.62, Change = 0.004 #> EM: Iteration = 82, LogLik = -26393.62, Change = 0.002 #> EM: Iteration = 83, LogLik = -26393.62, Change = 0.004 #> EM: Iteration = 84, LogLik = -26393.61, Change = 0.002 #> EM: Iteration = 85, LogLik = -26393.61, Change = 0.003 #> EM: Iteration = 86, LogLik = -26393.61, Change = 0.002 #> EM: Iteration = 87, LogLik = -26393.60, Change = 0.003 #> EM: Iteration = 88, LogLik = -26393.60, Change = 0.003 #> EM: Iteration = 89, LogLik = -26393.60, Change = 0.003 #> EM: Iteration = 90, LogLik = -26393.60, Change = 0.003 #> EM: Iteration = 91, LogLik = -26393.59, Change = 0.003 #> EM: Iteration = 92, LogLik = -26393.59, Change = 0.003 #> EM: Iteration = 93, LogLik = -26393.59, Change = 0.003 #> EM: Iteration = 94, LogLik = -26393.59, Change = 0.003 #> EM: Iteration = 95, LogLik = -26393.58, Change = 0.003 #> EM: Iteration = 96, LogLik = -26393.58, Change = 0.003 #> EM: Iteration = 97, LogLik = -26393.58, Change = 0.002 #> EM: Iteration = 98, LogLik = -26393.58, Change = 0.003 #> EM: Iteration = 99, LogLik = -26393.57, Change = 0.002 #> EM: Iteration = 100, LogLik = -26393.57, Change = 0.003 #> EM: Iteration = 101, LogLik = -26393.57, Change = 0.002 #> EM: Iteration = 102, LogLik = -26393.57, Change = 0.003 #> EM: Iteration = 103, LogLik = -26393.56, Change = 0.002 #> EM: Iteration = 104, LogLik = -26393.56, Change = 0.003 #> EM: Iteration = 105, LogLik = -26393.56, Change = 0.002 #> EM: Iteration = 106, LogLik = -26393.56, Change = 0.003 #> EM: Iteration = 107, LogLik = -26393.55, Change = 0.002 #> EM: Iteration = 108, LogLik = -26393.55, Change = 0.003 #> EM: Iteration = 109, LogLik = -26393.55, Change = 0.002 #> EM: Iteration = 110, LogLik = -26393.55, Change = 0.003 #> EM: Iteration = 111, LogLik = -26393.54, Change = 0.002 #> EM: Iteration = 112, LogLik = -26393.54, Change = 0.003 #> EM: Iteration = 113, LogLik = -26393.54, Change = 0.002 #> EM: Iteration = 114, LogLik = -26393.54, Change = 0.003 #> EM: Iteration = 115, LogLik = -26393.53, Change = 0.002 #> EM: Iteration = 116, LogLik = -26393.53, Change = 0.003 #> EM: Iteration = 117, LogLik = -26393.53, Change = 0.002 #> EM: Iteration = 118, LogLik = -26393.53, Change = 0.003 #> EM: Iteration = 119, LogLik = -26393.53, Change = 0.001 #> EM: Iteration = 120, LogLik = -26393.52, Change = 0.003 #> EM: Iteration = 121, LogLik = -26393.52, Change = 0.001 #> EM: Iteration = 122, LogLik = -26393.52, Change = 0.003 #> EM: Iteration = 123, LogLik = -26393.52, Change = 0.001 #> EM: Iteration = 124, LogLik = -26393.51, Change = 0.003 #> EM: Iteration = 125, LogLik = -26393.51, Change = 0.001 #> EM: Iteration = 126, LogLik = -26393.51, Change = 0.003 #> EM: Iteration = 127, LogLik = -26393.51, Change = 0.001 #> EM: Iteration = 128, LogLik = -26393.51, Change = 0.003 #> EM: Iteration = 129, LogLik = -26393.50, Change = 0.001 #> EM: Iteration = 130, LogLik = -26393.50, Change = 0.003 #> EM: Iteration = 131, LogLik = -26393.50, Change = 0.001 #> EM: Iteration = 132, LogLik = -26393.50, Change = 0.003 #> EM: Iteration = 133, LogLik = -26393.50, Change = 0.001 #> EM: Iteration = 134, LogLik = -26393.49, Change = 0.003 #> EM: Iteration = 135, LogLik = -26393.49, Change = 0.001 #> EM: Iteration = 136, LogLik = -26393.49, Change = 0.003 #> EM: Iteration = 137, LogLik = -26393.49, Change = 0.001 #> EM: Iteration = 138, LogLik = -26393.48, Change = 0.003 #> EM: Iteration = 139, LogLik = -26393.48, Change = 0.001 #> EM: Iteration = 140, LogLik = -26393.48, Change = 0.003 #> EM: Iteration = 141, LogLik = -26393.48, Change = 0.001 #> EM: Iteration = 142, LogLik = -26393.48, Change = 0.003 #> EM: Iteration = 143, LogLik = -26393.48, Change = 0.001 #> EM: Iteration = 144, LogLik = -26393.47, Change = 0.003 #> EM: Iteration = 145, LogLik = -26393.47, Change = 0.001 #> EM: Iteration = 146, LogLik = -26393.47, Change = 0.003 #> EM: Iteration = 147, LogLik = -26393.47, Change = 0.000 #> EM: Iteration = 148, LogLik = -26393.46, Change = 0.003 #> EM: Iteration = 149, LogLik = -26393.46, Change = 0.000 #> EM: Iteration = 150, LogLik = -26393.46, Change = 0.003 #> EM: Iteration = 151, LogLik = -26393.46, Change = 0.000 #> EM: Iteration = 152, LogLik = -26393.46, Change = 0.004 #> EM: Iteration = 153, LogLik = -26393.46, Change = 0.000 #> EM: Iteration = 154, LogLik = -26393.45, Change = 0.004 #> EM: Iteration = 155, LogLik = -26393.45, Change = 0.000 #> EM: Iteration = 156, LogLik = -26393.45, Change = 0.004 #> EM: Iteration = 157, LogLik = -26393.45, Change = 0.000 #> #> modsem (version 1.0.3): #> Estimator LMS #> Optimization method EM-NLMINB #> Number of observations 2000 #> Number of iterations 136 #> Loglikelihood -23781.36 #> Akaike (AIC) 47670.72 #> Bayesian (BIC) 47973.16 #> #> Numerical Integration: #> Points of integration (per dim) 24 #> Dimensions 1 #> Total points of integration 24 #> #> Fit Measures for H0: #> Loglikelihood -26393 #> Akaike (AIC) 52892.89 #> Bayesian (BIC) 53189.74 #> Chi-square 66.72 #> Degrees of Freedom (Chi-square) 82 #> P-value (Chi-square) 0.889 #> RMSEA 0.000 #> #> Comparative fit to H0 (no interaction effect) #> Loglikelihood change 2612.09 #> Difference test (D) 5224.18 #> Degrees of freedom (D) 1 #> P-value (D) 0.000 #> #> R-Squared: #> BEH 0.235 #> INT 0.365 #> R-Squared Null-Model (H0): #> BEH 0.210 #> INT 0.367 #> R-Squared Change: #> BEH 0.025 #> INT -0.002 #> #> Parameter Estimates: #> Coefficients unstandardized #> Information expected #> Standard errors standard #> #> Latent Variables: #> Estimate Std.Error z.value P(>|z|) #> INT =~ #> int1 1.000 #> int2 0.915 0.016 58.24 0.000 #> int3 0.807 0.015 54.49 0.000 #> ATT =~ #> att1 1.000 #> att2 0.876 0.012 71.51 0.000 #> att3 0.787 0.012 66.55 0.000 #> att4 0.693 0.011 60.50 0.000 #> att5 0.885 0.012 71.68 0.000 #> SN =~ #> sn1 1.000 #> sn2 0.893 0.017 52.59 0.000 #> PBC =~ #> pbc1 1.000 #> pbc2 0.912 0.013 69.14 0.000 #> pbc3 0.801 0.012 66.52 0.000 #> BEH =~ #> b1 1.000 #> b2 0.959 0.033 29.32 0.000 #> #> Regressions: #> Estimate Std.Error z.value P(>|z|) #> BEH ~ #> INT 0.196 0.026 7.60 0.000 #> PBC 0.238 0.022 10.62 0.000 #> INT:INT 0.129 0.018 7.29 0.000 #> INT ~ #> PBC 0.218 0.029 7.51 0.000 #> ATT 0.210 0.025 8.28 0.000 #> SN 0.172 0.028 6.22 0.000 #> #> Intercepts: #> Estimate Std.Error z.value P(>|z|) #> int1 1.005 0.020 49.42 0.000 #> int2 1.004 0.019 53.24 0.000 #> int3 0.998 0.017 57.46 0.000 #> att1 1.007 0.024 42.32 0.000 #> att2 1.001 0.021 47.17 0.000 #> att3 1.011 0.019 51.87 0.000 #> att4 0.994 0.018 55.74 0.000 #> att5 0.986 0.021 46.02 0.000 #> sn1 1.000 0.024 42.10 0.000 #> sn2 1.005 0.021 47.14 0.000 #> pbc1 0.991 0.023 43.04 0.000 #> pbc2 0.979 0.021 45.56 0.000 #> pbc3 0.986 0.019 51.24 0.000 #> b1 0.995 0.024 42.27 0.000 #> b2 1.014 0.022 45.68 0.000 #> BEH 0.000 #> INT 0.000 #> ATT 0.000 #> SN 0.000 #> PBC 0.000 #> #> Covariances: #> Estimate Std.Error z.value P(>|z|) #> PBC ~~ #> ATT 0.668 0.079 8.48 0.000 #> SN 0.675 0.054 12.48 0.000 #> ATT ~~ #> SN 0.625 0.029 21.63 0.000 #> #> Variances: #> Estimate Std.Error z.value P(>|z|) #> int1 0.161 0.009 18.28 0.000 #> int2 0.161 0.008 20.89 0.000 #> int3 0.170 0.007 23.51 0.000 #> att1 0.167 0.007 23.23 0.000 #> att2 0.150 0.006 24.81 0.000 #> att3 0.160 0.006 26.51 0.000 #> att4 0.163 0.006 27.46 0.000 #> att5 0.159 0.006 24.85 0.000 #> sn1 0.181 0.015 12.48 0.000 #> sn2 0.155 0.012 13.27 0.000 #> pbc1 0.145 0.008 18.27 0.000 #> pbc2 0.160 0.007 21.74 0.000 #> pbc3 0.154 0.007 23.69 0.000 #> b1 0.185 0.020 9.23 0.000 #> b2 0.136 0.018 7.52 0.000 #> BEH 0.475 0.024 19.71 0.000 #> PBC 0.960 0.037 26.13 0.000 #> ATT 1.000 0.058 17.32 0.000 #> SN 0.968 0.086 11.29 0.000 #> INT 0.481 0.019 24.97 0.000 est_qml <- modsem(tpb_nonlinear, data = TPB, cov.syntax = tpb_linear, method = \"qml\") summary(est_qml) #> Estimating null model #> Starting M-step #> #> modsem (version 1.0.3): #> Estimator QML #> Optimization method NLMINB #> Number of observations 2000 #> Number of iterations 76 #> Loglikelihood -26360.52 #> Akaike (AIC) 52829.04 #> Bayesian (BIC) 53131.49 #> #> Fit Measures for H0: #> Loglikelihood -26393 #> Akaike (AIC) 52892.45 #> Bayesian (BIC) 53189.29 #> Chi-square 66.27 #> Degrees of Freedom (Chi-square) 82 #> P-value (Chi-square) 0.897 #> RMSEA 0.000 #> #> Comparative fit to H0 (no interaction effect) #> Loglikelihood change 32.70 #> Difference test (D) 65.41 #> Degrees of freedom (D) 1 #> P-value (D) 0.000 #> #> R-Squared: #> BEH 0.239 #> INT 0.370 #> R-Squared Null-Model (H0): #> BEH 0.210 #> INT 0.367 #> R-Squared Change: #> BEH 0.029 #> INT 0.003 #> #> Parameter Estimates: #> Coefficients unstandardized #> Information observed #> Standard errors standard #> #> Latent Variables: #> Estimate Std.Error z.value P(>|z|) #> INT =~ #> int1 1.000 #> int2 0.914 0.015 59.04 0.000 #> int3 0.807 0.015 55.65 0.000 #> ATT =~ #> att1 1.000 #> att2 0.878 0.012 71.56 0.000 #> att3 0.789 0.012 66.37 0.000 #> att4 0.695 0.011 61.00 0.000 #> att5 0.887 0.013 70.85 0.000 #> SN =~ #> sn1 1.000 #> sn2 0.888 0.017 52.62 0.000 #> PBC =~ #> pbc1 1.000 #> pbc2 0.913 0.013 69.38 0.000 #> pbc3 0.801 0.012 66.08 0.000 #> BEH =~ #> b1 1.000 #> b2 0.960 0.032 29.91 0.000 #> #> Regressions: #> Estimate Std.Error z.value P(>|z|) #> BEH ~ #> INT 0.197 0.025 7.76 0.000 #> PBC 0.239 0.023 10.59 0.000 #> INT:INT 0.128 0.016 7.88 0.000 #> INT ~ #> PBC 0.222 0.030 7.51 0.000 #> ATT 0.213 0.026 8.17 0.000 #> SN 0.175 0.028 6.33 0.000 #> #> Intercepts: #> Estimate Std.Error z.value P(>|z|) #> int1 1.014 0.022 46.96 0.000 #> int2 1.012 0.020 50.41 0.000 #> int3 1.005 0.018 54.80 0.000 #> att1 1.014 0.024 42.01 0.000 #> att2 1.007 0.021 46.97 0.000 #> att3 1.016 0.020 51.45 0.000 #> att4 0.999 0.018 55.65 0.000 #> att5 0.992 0.022 45.67 0.000 #> sn1 1.006 0.024 41.66 0.000 #> sn2 1.010 0.022 46.71 0.000 #> pbc1 0.998 0.024 42.41 0.000 #> pbc2 0.985 0.022 44.93 0.000 #> pbc3 0.991 0.020 50.45 0.000 #> b1 0.999 0.023 42.64 0.000 #> b2 1.017 0.022 46.25 0.000 #> BEH 0.000 #> INT 0.000 #> ATT 0.000 #> SN 0.000 #> PBC 0.000 #> #> Covariances: #> Estimate Std.Error z.value P(>|z|) #> PBC ~~ #> ATT 0.678 0.029 23.45 0.000 #> SN 0.678 0.029 23.08 0.000 #> ATT ~~ #> SN 0.629 0.029 21.70 0.000 #> #> Variances: #> Estimate Std.Error z.value P(>|z|) #> int1 0.158 0.009 18.22 0.000 #> int2 0.160 0.008 20.38 0.000 #> int3 0.168 0.007 23.63 0.000 #> att1 0.167 0.007 23.53 0.000 #> att2 0.150 0.006 24.71 0.000 #> att3 0.160 0.006 26.38 0.000 #> att4 0.162 0.006 27.64 0.000 #> att5 0.159 0.006 24.93 0.000 #> sn1 0.178 0.015 12.09 0.000 #> sn2 0.157 0.012 13.26 0.000 #> pbc1 0.145 0.008 18.44 0.000 #> pbc2 0.160 0.007 21.42 0.000 #> pbc3 0.154 0.006 23.80 0.000 #> b1 0.185 0.020 9.42 0.000 #> b2 0.135 0.018 7.60 0.000 #> BEH 0.475 0.024 19.74 0.000 #> PBC 0.962 0.036 27.04 0.000 #> ATT 0.998 0.037 26.93 0.000 #> SN 0.988 0.039 25.23 0.000 #> INT 0.488 0.020 24.59 0.000"},{"path":"/articles/lms_qml.html","id":"the-latent-moderated-structural-equations-lms-and-the-quasi-maximum-likelihood-qml-approach","dir":"Articles","previous_headings":"","what":"The Latent Moderated Structural Equations (LMS) and the Quasi Maximum Likelihood (QML) approach","title":"LMS and QML approaches","text":"LMS- QML approach works models, interaction effects endogenous can bit tricky estimate (see vignette. approaches (particularily LMS approach) quite computationally intensive, thus partly implemented C++ (using Rcpp RcppArmadillo). Additionally starting parameters estimated using double centering approach (means observed variables) used generate good starting parameters faster convergence. want see progress estimation process can use ´verbose = TRUE´.","code":""},{"path":"/articles/lms_qml.html","id":"a-simple-example","dir":"Articles","previous_headings":"The Latent Moderated Structural Equations (LMS) and the Quasi Maximum Likelihood (QML) approach","what":"A Simple Example","title":"LMS and QML approaches","text":"can see example LMS approach simple model. default summary function calculates fit measures compared null model (.e., model without interaction term). can see example using QML approach.","code":"library(modsem) m1 <- ' # Outer Model X =~ x1 X =~ x2 + x3 Z =~ z1 + z2 + z3 Y =~ y1 + y2 + y3 # Inner model Y ~ X + Z Y ~ X:Z ' lms1 <- modsem(m1, oneInt, method = \"lms\") summary(lms1, standardized = TRUE) # standardized estimates #> Estimating null model #> EM: Iteration = 1, LogLik = -17831.87, Change = -17831.875 #> EM: Iteration = 2, LogLik = -17831.87, Change = 0.000 #> #> modsem (version 1.0.3): #> Estimator LMS #> Optimization method EM-NLMINB #> Number of observations 2000 #> Number of iterations 92 #> Loglikelihood -14687.85 #> Akaike (AIC) 29437.71 #> Bayesian (BIC) 29611.34 #> #> Numerical Integration: #> Points of integration (per dim) 24 #> Dimensions 1 #> Total points of integration 24 #> #> Fit Measures for H0: #> Loglikelihood -17832 #> Akaike (AIC) 35723.75 #> Bayesian (BIC) 35891.78 #> Chi-square 17.52 #> Degrees of Freedom (Chi-square) 24 #> P-value (Chi-square) 0.826 #> RMSEA 0.000 #> #> Comparative fit to H0 (no interaction effect) #> Loglikelihood change 3144.02 #> Difference test (D) 6288.04 #> Degrees of freedom (D) 1 #> P-value (D) 0.000 #> #> R-Squared: #> Y 0.596 #> R-Squared Null-Model (H0): #> Y 0.395 #> R-Squared Change: #> Y 0.201 #> #> Parameter Estimates: #> Coefficients standardized #> Information expected #> Standard errors standard #> #> Latent Variables: #> Estimate Std.Error z.value P(>|z|) #> X =~ #> x1 0.926 #> x2 0.891 0.014 64.39 0.000 #> x3 0.912 0.013 67.69 0.000 #> Z =~ #> z1 0.927 #> z2 0.898 0.014 64.59 0.000 #> z3 0.913 0.013 67.87 0.000 #> Y =~ #> y1 0.969 #> y2 0.954 0.009 105.92 0.000 #> y3 0.961 0.009 111.95 0.000 #> #> Regressions: #> Estimate Std.Error z.value P(>|z|) #> Y ~ #> X 0.427 0.020 21.79 0.000 #> Z 0.370 0.018 20.16 0.000 #> X:Z 0.454 0.017 26.28 0.000 #> #> Covariances: #> Estimate Std.Error z.value P(>|z|) #> X ~~ #> Z 0.199 0.024 8.43 0.000 #> #> Variances: #> Estimate Std.Error z.value P(>|z|) #> x1 0.142 0.007 19.27 0.000 #> x2 0.206 0.009 23.86 0.000 #> x3 0.169 0.008 21.31 0.000 #> z1 0.141 0.008 18.34 0.000 #> z2 0.193 0.009 22.39 0.000 #> z3 0.167 0.008 20.52 0.000 #> y1 0.061 0.003 17.93 0.000 #> y2 0.090 0.004 22.72 0.000 #> y3 0.077 0.004 20.69 0.000 #> X 1.000 0.016 61.06 0.000 #> Z 1.000 0.018 55.21 0.000 #> Y 0.404 0.015 26.54 0.000 qml1 <- modsem(m1, oneInt, method = \"qml\") summary(qml1) #> Estimating null model #> Starting M-step #> #> modsem (version 1.0.3): #> Estimator QML #> Optimization method NLMINB #> Number of observations 2000 #> Number of iterations 111 #> Loglikelihood -17496.22 #> Akaike (AIC) 35054.43 #> Bayesian (BIC) 35228.06 #> #> Fit Measures for H0: #> Loglikelihood -17832 #> Akaike (AIC) 35723.75 #> Bayesian (BIC) 35891.78 #> Chi-square 17.52 #> Degrees of Freedom (Chi-square) 24 #> P-value (Chi-square) 0.826 #> RMSEA 0.000 #> #> Comparative fit to H0 (no interaction effect) #> Loglikelihood change 335.66 #> Difference test (D) 671.32 #> Degrees of freedom (D) 1 #> P-value (D) 0.000 #> #> R-Squared: #> Y 0.607 #> R-Squared Null-Model (H0): #> Y 0.395 #> R-Squared Change: #> Y 0.211 #> #> Parameter Estimates: #> Coefficients unstandardized #> Information observed #> Standard errors standard #> #> Latent Variables: #> Estimate Std.Error z.value P(>|z|) #> X =~ #> x1 1.000 #> x2 0.803 0.013 63.96 0.000 #> x3 0.914 0.013 67.80 0.000 #> Z =~ #> z1 1.000 #> z2 0.810 0.012 65.12 0.000 #> z3 0.881 0.013 67.62 0.000 #> Y =~ #> y1 1.000 #> y2 0.798 0.007 107.57 0.000 #> y3 0.899 0.008 112.55 0.000 #> #> Regressions: #> Estimate Std.Error z.value P(>|z|) #> Y ~ #> X 0.674 0.032 20.94 0.000 #> Z 0.566 0.030 18.96 0.000 #> X:Z 0.712 0.028 25.45 0.000 #> #> Intercepts: #> Estimate Std.Error z.value P(>|z|) #> x1 1.023 0.024 42.89 0.000 #> x2 1.215 0.020 60.99 0.000 #> x3 0.919 0.022 41.48 0.000 #> z1 1.012 0.024 41.57 0.000 #> z2 1.206 0.020 59.27 0.000 #> z3 0.916 0.022 42.06 0.000 #> y1 1.038 0.033 31.45 0.000 #> y2 1.221 0.027 45.49 0.000 #> y3 0.955 0.030 31.86 0.000 #> Y 0.000 #> X 0.000 #> Z 0.000 #> #> Covariances: #> Estimate Std.Error z.value P(>|z|) #> X ~~ #> Z 0.200 0.024 8.24 0.000 #> #> Variances: #> Estimate Std.Error z.value P(>|z|) #> x1 0.158 0.009 18.14 0.000 #> x2 0.162 0.007 23.19 0.000 #> x3 0.165 0.008 20.82 0.000 #> z1 0.166 0.009 18.34 0.000 #> z2 0.159 0.007 22.62 0.000 #> z3 0.158 0.008 20.71 0.000 #> y1 0.159 0.009 17.98 0.000 #> y2 0.154 0.007 22.67 0.000 #> y3 0.164 0.008 20.71 0.000 #> X 0.983 0.036 26.99 0.000 #> Z 1.019 0.038 26.95 0.000 #> Y 0.943 0.038 24.87 0.000"},{"path":"/articles/lms_qml.html","id":"a-more-complicated-example","dir":"Articles","previous_headings":"The Latent Moderated Structural Equations (LMS) and the Quasi Maximum Likelihood (QML) approach","what":"A more complicated example","title":"LMS and QML approaches","text":"can see example complicated example using model theory planned behaviour (TPB), two endogenous variables, interaction endogenous exogenous variable. estimating complicated models LMS-approach, recommended increase number nodes used numerical integration. default number nodes set 16, can increased using nodes argument. argument effect QML approach. interaction effect endogenous exogenous variable, recommended use least 32 nodes LMS-approach. can also get robust standard errors setting robust.se = TRUE modsem() function. Note: want lms-approach give similar results possible mplus, increase number nodes (e.g., nodes = 100).","code":"# ATT = Attitude, # PBC = Perceived Behavioural Control # INT = Intention # SN = Subjective Norms # BEH = Behaviour tpb <- ' # Outer Model (Based on Hagger et al., 2007) ATT =~ att1 + att2 + att3 + att4 + att5 SN =~ sn1 + sn2 PBC =~ pbc1 + pbc2 + pbc3 INT =~ int1 + int2 + int3 BEH =~ b1 + b2 # Inner Model (Based on Steinmetz et al., 2011) INT ~ ATT + SN + PBC BEH ~ INT + PBC BEH ~ INT:PBC ' lms2 <- modsem(tpb, TPB, method = \"lms\", nodes = 32) summary(lms2) #> Estimating null model #> EM: Iteration = 1, LogLik = -26393.22, Change = -26393.223 #> EM: Iteration = 2, LogLik = -26393.22, Change = 0.000 #> #> modsem (version 1.0.3): #> Estimator LMS #> Optimization method EM-NLMINB #> Number of observations 2000 #> Number of iterations 70 #> Loglikelihood -23439.02 #> Akaike (AIC) 46986.04 #> Bayesian (BIC) 47288.49 #> #> Numerical Integration: #> Points of integration (per dim) 32 #> Dimensions 1 #> Total points of integration 32 #> #> Fit Measures for H0: #> Loglikelihood -26393 #> Akaike (AIC) 52892.45 #> Bayesian (BIC) 53189.29 #> Chi-square 66.27 #> Degrees of Freedom (Chi-square) 82 #> P-value (Chi-square) 0.897 #> RMSEA 0.000 #> #> Comparative fit to H0 (no interaction effect) #> Loglikelihood change 2954.20 #> Difference test (D) 5908.41 #> Degrees of freedom (D) 1 #> P-value (D) 0.000 #> #> R-Squared: #> INT 0.364 #> BEH 0.259 #> R-Squared Null-Model (H0): #> INT 0.367 #> BEH 0.210 #> R-Squared Change: #> INT -0.003 #> BEH 0.049 #> #> Parameter Estimates: #> Coefficients unstandardized #> Information expected #> Standard errors standard #> #> Latent Variables: #> Estimate Std.Error z.value P(>|z|) #> PBC =~ #> pbc1 1.000 #> pbc2 0.914 0.013 68.52 0.000 #> pbc3 0.802 0.012 65.02 0.000 #> ATT =~ #> att1 1.000 #> att2 0.878 0.012 70.81 0.000 #> att3 0.789 0.012 65.77 0.000 #> att4 0.695 0.011 61.09 0.000 #> att5 0.887 0.013 70.26 0.000 #> SN =~ #> sn1 1.000 #> sn2 0.889 0.017 52.00 0.000 #> INT =~ #> int1 1.000 #> int2 0.913 0.016 58.38 0.000 #> int3 0.807 0.015 55.37 0.000 #> BEH =~ #> b1 1.000 #> b2 0.959 0.033 29.28 0.000 #> #> Regressions: #> Estimate Std.Error z.value P(>|z|) #> INT ~ #> PBC 0.218 0.030 7.36 0.000 #> ATT 0.214 0.026 8.19 0.000 #> SN 0.176 0.027 6.43 0.000 #> BEH ~ #> PBC 0.233 0.022 10.35 0.000 #> INT 0.188 0.025 7.62 0.000 #> PBC:INT 0.205 0.019 10.90 0.000 #> #> Intercepts: #> Estimate Std.Error z.value P(>|z|) #> pbc1 0.990 0.022 45.57 0.000 #> pbc2 0.978 0.020 48.28 0.000 #> pbc3 0.985 0.018 53.86 0.000 #> att1 1.009 0.023 43.19 0.000 #> att2 1.002 0.021 48.19 0.000 #> att3 1.012 0.019 53.21 0.000 #> att4 0.995 0.017 56.95 0.000 #> att5 0.988 0.021 46.75 0.000 #> sn1 1.001 0.023 42.73 0.000 #> sn2 1.006 0.021 48.06 0.000 #> int1 1.010 0.021 47.81 0.000 #> int2 1.009 0.020 51.14 0.000 #> int3 1.002 0.018 56.02 0.000 #> b1 0.999 0.021 47.31 0.000 #> b2 1.017 0.020 51.50 0.000 #> INT 0.000 #> BEH 0.000 #> PBC 0.000 #> ATT 0.000 #> SN 0.000 #> #> Covariances: #> Estimate Std.Error z.value P(>|z|) #> PBC ~~ #> ATT 0.668 0.021 31.78 0.000 #> SN 0.668 0.022 30.52 0.000 #> ATT ~~ #> SN 0.623 0.019 32.90 0.000 #> #> Variances: #> Estimate Std.Error z.value P(>|z|) #> pbc1 0.148 0.008 18.81 0.000 #> pbc2 0.159 0.007 21.62 0.000 #> pbc3 0.155 0.007 23.64 0.000 #> att1 0.167 0.007 23.64 0.000 #> att2 0.150 0.006 24.73 0.000 #> att3 0.159 0.006 26.68 0.000 #> att4 0.162 0.006 27.71 0.000 #> att5 0.159 0.006 25.11 0.000 #> sn1 0.178 0.015 11.97 0.000 #> sn2 0.156 0.012 13.20 0.000 #> int1 0.157 0.009 18.25 0.000 #> int2 0.160 0.008 20.48 0.000 #> int3 0.168 0.007 24.27 0.000 #> b1 0.185 0.020 9.46 0.000 #> b2 0.136 0.018 7.71 0.000 #> PBC 0.947 0.017 55.23 0.000 #> ATT 0.992 0.014 69.87 0.000 #> SN 0.981 0.015 64.37 0.000 #> INT 0.491 0.020 24.97 0.000 #> BEH 0.456 0.023 19.46 0.000 qml2 <- modsem(tpb, TPB, method = \"qml\") summary(qml2, standardized = TRUE) # standardized estimates #> Estimating null model #> Starting M-step #> #> modsem (version 1.0.3): #> Estimator QML #> Optimization method NLMINB #> Number of observations 2000 #> Number of iterations 73 #> Loglikelihood -26326.25 #> Akaike (AIC) 52760.5 #> Bayesian (BIC) 53062.95 #> #> Fit Measures for H0: #> Loglikelihood -26393 #> Akaike (AIC) 52892.45 #> Bayesian (BIC) 53189.29 #> Chi-square 66.27 #> Degrees of Freedom (Chi-square) 82 #> P-value (Chi-square) 0.897 #> RMSEA 0.000 #> #> Comparative fit to H0 (no interaction effect) #> Loglikelihood change 66.97 #> Difference test (D) 133.95 #> Degrees of freedom (D) 1 #> P-value (D) 0.000 #> #> R-Squared: #> INT 0.366 #> BEH 0.263 #> R-Squared Null-Model (H0): #> INT 0.367 #> BEH 0.210 #> R-Squared Change: #> INT 0.000 #> BEH 0.053 #> #> Parameter Estimates: #> Coefficients standardized #> Information observed #> Standard errors standard #> #> Latent Variables: #> Estimate Std.Error z.value P(>|z|) #> PBC =~ #> pbc1 0.933 #> pbc2 0.913 0.013 69.47 0.000 #> pbc3 0.894 0.014 66.10 0.000 #> ATT =~ #> att1 0.925 #> att2 0.915 0.013 71.56 0.000 #> att3 0.892 0.013 66.38 0.000 #> att4 0.865 0.014 61.00 0.000 #> att5 0.912 0.013 70.85 0.000 #> SN =~ #> sn1 0.921 #> sn2 0.913 0.017 52.61 0.000 #> INT =~ #> int1 0.912 #> int2 0.895 0.015 59.05 0.000 #> int3 0.866 0.016 55.73 0.000 #> BEH =~ #> b1 0.877 #> b2 0.900 0.028 31.71 0.000 #> #> Regressions: #> Estimate Std.Error z.value P(>|z|) #> INT ~ #> PBC 0.243 0.033 7.35 0.000 #> ATT 0.242 0.030 8.16 0.000 #> SN 0.199 0.031 6.37 0.000 #> BEH ~ #> PBC 0.289 0.028 10.37 0.000 #> INT 0.212 0.028 7.69 0.000 #> PBC:INT 0.227 0.020 11.32 0.000 #> #> Covariances: #> Estimate Std.Error z.value P(>|z|) #> PBC ~~ #> ATT 0.692 0.030 23.45 0.000 #> SN 0.695 0.030 23.08 0.000 #> ATT ~~ #> SN 0.634 0.029 21.70 0.000 #> #> Variances: #> Estimate Std.Error z.value P(>|z|) #> pbc1 0.130 0.007 18.39 0.000 #> pbc2 0.166 0.008 21.43 0.000 #> pbc3 0.201 0.008 23.89 0.000 #> att1 0.144 0.006 23.53 0.000 #> att2 0.164 0.007 24.71 0.000 #> att3 0.204 0.008 26.38 0.000 #> att4 0.252 0.009 27.65 0.000 #> att5 0.168 0.007 24.93 0.000 #> sn1 0.153 0.013 12.09 0.000 #> sn2 0.167 0.013 13.26 0.000 #> int1 0.168 0.009 18.11 0.000 #> int2 0.199 0.010 20.41 0.000 #> int3 0.249 0.011 23.55 0.000 #> b1 0.231 0.023 10.12 0.000 #> b2 0.191 0.024 8.10 0.000 #> PBC 1.000 0.037 27.07 0.000 #> ATT 1.000 0.037 26.93 0.000 #> SN 1.000 0.040 25.22 0.000 #> INT 0.634 0.026 24.64 0.000 #> BEH 0.737 0.037 20.17 0.000"},{"path":"/articles/modsem.html","id":"the-basic-syntax","dir":"Articles","previous_headings":"","what":"The Basic Syntax","title":"modsem","text":"modsem basically introduces new feature lavaan-syntax – semicolon operator (“:”). semicolon operator works way lm()-function. order specify interaction effect two variables, join Var1:Var2, Models can either estimated using one product indicator approaches (“ca”, “rca”, “dblcent”, “pind”) using latent moderated structural equations approach (“lms”), quasi maximum likelihood approach (“qml”). product indicator approaches estimated via lavaan, whilst lms qml approaches estimated via modsem .","code":""},{"path":"/articles/modsem.html","id":"a-simple-example","dir":"Articles","previous_headings":"The Basic Syntax","what":"A Simple Example","title":"modsem","text":"can see simple example specify interaction effect two latent variables lavaan. default model estimated using “dblcent” method. want use another method, method can changed using method argument.","code":"m1 <- ' # Outer Model X =~ x1 + x2 +x3 Y =~ y1 + y2 + y3 Z =~ z1 + z2 + z3 # Inner model Y ~ X + Z + X:Z ' est1 <- modsem(m1, oneInt) summary(est1) #> modsem: #> Method = dblcent #> lavaan 0.6-18 ended normally after 159 iterations #> #> Estimator ML #> Optimization method NLMINB #> Number of model parameters 60 #> #> Number of observations 2000 #> #> Model Test User Model: #> #> Test statistic 122.924 #> Degrees of freedom 111 #> P-value (Chi-square) 0.207 #> #> Parameter Estimates: #> #> Standard errors Standard #> Information Expected #> Information saturated (h1) model Structured #> #> Latent Variables: #> Estimate Std.Err z-value P(>|z|) #> X =~ #> x1 1.000 #> x2 0.804 0.013 63.612 0.000 #> x3 0.916 0.014 67.144 0.000 #> Y =~ #> y1 1.000 #> y2 0.798 0.007 107.428 0.000 #> y3 0.899 0.008 112.453 0.000 #> Z =~ #> z1 1.000 #> z2 0.812 0.013 64.763 0.000 #> z3 0.882 0.013 67.014 0.000 #> XZ =~ #> x1z1 1.000 #> x2z1 0.805 0.013 60.636 0.000 #> x3z1 0.877 0.014 62.680 0.000 #> x1z2 0.793 0.013 59.343 0.000 #> x2z2 0.646 0.015 43.672 0.000 #> x3z2 0.706 0.016 44.292 0.000 #> x1z3 0.887 0.014 63.700 0.000 #> x2z3 0.716 0.016 45.645 0.000 #> x3z3 0.781 0.017 45.339 0.000 #> #> Regressions: #> Estimate Std.Err z-value P(>|z|) #> Y ~ #> X 0.675 0.027 25.379 0.000 #> Z 0.561 0.026 21.606 0.000 #> XZ 0.702 0.027 26.360 0.000 #> #> Covariances: #> Estimate Std.Err z-value P(>|z|) #> .x1z1 ~~ #> .x2z2 0.000 #> .x2z3 0.000 #> .x3z2 0.000 #> .x3z3 0.000 #> .x2z1 ~~ #> .x1z2 0.000 #> .x1z2 ~~ #> .x2z3 0.000 #> .x3z1 ~~ #> .x1z2 0.000 #> .x1z2 ~~ #> .x3z3 0.000 #> .x2z1 ~~ #> .x1z3 0.000 #> .x2z2 ~~ #> .x1z3 0.000 #> .x3z1 ~~ #> .x1z3 0.000 #> .x3z2 ~~ #> .x1z3 0.000 #> .x2z1 ~~ #> .x3z2 0.000 #> .x3z3 0.000 #> .x3z1 ~~ #> .x2z2 0.000 #> .x2z2 ~~ #> .x3z3 0.000 #> .x3z1 ~~ #> .x2z3 0.000 #> .x3z2 ~~ #> .x2z3 0.000 #> .x1z1 ~~ #> .x1z2 0.115 0.008 14.802 0.000 #> .x1z3 0.114 0.008 13.947 0.000 #> .x2z1 0.125 0.008 16.095 0.000 #> .x3z1 0.140 0.009 16.135 0.000 #> .x1z2 ~~ #> .x1z3 0.103 0.007 14.675 0.000 #> .x2z2 0.128 0.006 20.850 0.000 #> .x3z2 0.146 0.007 21.243 0.000 #> .x1z3 ~~ #> .x2z3 0.116 0.007 17.818 0.000 #> .x3z3 0.135 0.007 18.335 0.000 #> .x2z1 ~~ #> .x2z2 0.135 0.006 20.905 0.000 #> .x2z3 0.145 0.007 21.145 0.000 #> .x3z1 0.114 0.007 16.058 0.000 #> .x2z2 ~~ #> .x2z3 0.117 0.006 20.419 0.000 #> .x3z2 0.116 0.006 20.586 0.000 #> .x2z3 ~~ #> .x3z3 0.109 0.006 18.059 0.000 #> .x3z1 ~~ #> .x3z2 0.138 0.007 19.331 0.000 #> .x3z3 0.158 0.008 20.269 0.000 #> .x3z2 ~~ #> .x3z3 0.131 0.007 19.958 0.000 #> X ~~ #> Z 0.201 0.024 8.271 0.000 #> XZ 0.016 0.025 0.628 0.530 #> Z ~~ #> XZ 0.062 0.025 2.449 0.014 #> #> Variances: #> Estimate Std.Err z-value P(>|z|) #> .x1 0.160 0.009 17.871 0.000 #> .x2 0.162 0.007 22.969 0.000 #> .x3 0.163 0.008 20.161 0.000 #> .y1 0.159 0.009 17.896 0.000 #> .y2 0.154 0.007 22.640 0.000 #> .y3 0.164 0.008 20.698 0.000 #> .z1 0.168 0.009 18.143 0.000 #> .z2 0.158 0.007 22.264 0.000 #> .z3 0.158 0.008 20.389 0.000 #> .x1z1 0.311 0.014 22.227 0.000 #> .x2z1 0.292 0.011 27.287 0.000 #> .x3z1 0.327 0.012 26.275 0.000 #> .x1z2 0.290 0.011 26.910 0.000 #> .x2z2 0.239 0.008 29.770 0.000 #> .x3z2 0.270 0.009 29.117 0.000 #> .x1z3 0.272 0.012 23.586 0.000 #> .x2z3 0.245 0.009 27.979 0.000 #> .x3z3 0.297 0.011 28.154 0.000 #> X 0.981 0.036 26.895 0.000 #> .Y 0.990 0.038 25.926 0.000 #> Z 1.016 0.038 26.856 0.000 #> XZ 1.045 0.044 24.004 0.000 est1 <- modsem(m1, oneInt, method = \"lms\") summary(est1) #> Estimating null model #> EM: Iteration = 1, LogLik = -17831.87, Change = -17831.875 #> EM: Iteration = 2, LogLik = -17831.87, Change = 0.000 #> #> modsem (version 1.0.3): #> Estimator LMS #> Optimization method EM-NLMINB #> Number of observations 2000 #> Number of iterations 92 #> Loglikelihood -14687.85 #> Akaike (AIC) 29437.71 #> Bayesian (BIC) 29611.34 #> #> Numerical Integration: #> Points of integration (per dim) 24 #> Dimensions 1 #> Total points of integration 24 #> #> Fit Measures for H0: #> Loglikelihood -17832 #> Akaike (AIC) 35723.75 #> Bayesian (BIC) 35891.78 #> Chi-square 17.52 #> Degrees of Freedom (Chi-square) 24 #> P-value (Chi-square) 0.826 #> RMSEA 0.000 #> #> Comparative fit to H0 (no interaction effect) #> Loglikelihood change 3144.02 #> Difference test (D) 6288.04 #> Degrees of freedom (D) 1 #> P-value (D) 0.000 #> #> R-Squared: #> Y 0.596 #> R-Squared Null-Model (H0): #> Y 0.395 #> R-Squared Change: #> Y 0.201 #> #> Parameter Estimates: #> Coefficients unstandardized #> Information expected #> Standard errors standard #> #> Latent Variables: #> Estimate Std.Error z.value P(>|z|) #> X =~ #> x1 1.000 #> x2 0.804 0.012 64.39 0.000 #> x3 0.915 0.014 67.69 0.000 #> Z =~ #> z1 1.000 #> z2 0.810 0.013 64.59 0.000 #> z3 0.881 0.013 67.87 0.000 #> Y =~ #> y1 1.000 #> y2 0.799 0.008 105.92 0.000 #> y3 0.899 0.008 111.95 0.000 #> #> Regressions: #> Estimate Std.Error z.value P(>|z|) #> Y ~ #> X 0.676 0.031 21.79 0.000 #> Z 0.572 0.028 20.16 0.000 #> X:Z 0.712 0.027 26.28 0.000 #> #> Intercepts: #> Estimate Std.Error z.value P(>|z|) #> x1 1.025 0.019 52.75 0.000 #> x2 1.218 0.017 73.47 0.000 #> x3 0.922 0.018 50.64 0.000 #> z1 1.016 0.024 41.94 0.000 #> z2 1.209 0.020 59.65 0.000 #> z3 0.920 0.022 42.33 0.000 #> y1 1.046 0.031 33.47 0.000 #> y2 1.227 0.025 48.20 0.000 #> y3 0.962 0.028 33.81 0.000 #> Y 0.000 #> X 0.000 #> Z 0.000 #> #> Covariances: #> Estimate Std.Error z.value P(>|z|) #> X ~~ #> Z 0.198 0.023 8.43 0.000 #> #> Variances: #> Estimate Std.Error z.value P(>|z|) #> x1 0.160 0.008 19.27 0.000 #> x2 0.163 0.007 23.86 0.000 #> x3 0.165 0.008 21.31 0.000 #> z1 0.166 0.009 18.34 0.000 #> z2 0.160 0.007 22.39 0.000 #> z3 0.158 0.008 20.52 0.000 #> y1 0.160 0.009 17.93 0.000 #> y2 0.154 0.007 22.72 0.000 #> y3 0.163 0.008 20.69 0.000 #> X 0.972 0.016 61.06 0.000 #> Z 1.017 0.018 55.21 0.000 #> Y 0.984 0.037 26.54 0.000"},{"path":"/articles/modsem.html","id":"interactions-between-two-observed-variables","dir":"Articles","previous_headings":"The Basic Syntax","what":"Interactions Between two Observed Variables","title":"modsem","text":"modsem allow estimate interactions latent variables, also interactions observed variables. first run regression observed variables, interaction x1 z2, run equivalent model using modsem(). Regression Using modsem() general, interactions observed variables recommended use method = “pind”. Interaction effects observed variables supported LMS- QML-approach. certain circumstances, can define latent variabale single indicator estimate interaction effect two observed variables, LMS QML approach, generally recommended.","code":"reg1 <- lm(y1 ~ x1*z1, oneInt) summary(reg1) #> #> Call: #> lm(formula = y1 ~ x1 * z1, data = oneInt) #> #> Residuals: #> Min 1Q Median 3Q Max #> -3.7155 -0.8087 -0.0367 0.8078 4.6531 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 0.51422 0.04618 11.135 <2e-16 *** #> x1 0.05477 0.03387 1.617 0.1060 #> z1 -0.06575 0.03461 -1.900 0.0576 . #> x1:z1 0.54361 0.02272 23.926 <2e-16 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 1.184 on 1996 degrees of freedom #> Multiple R-squared: 0.4714, Adjusted R-squared: 0.4706 #> F-statistic: 593.3 on 3 and 1996 DF, p-value: < 2.2e-16 # Here we use \"pind\" as the method (see chapter 3) est2 <- modsem('y1 ~ x1 + z1 + x1:z1', data = oneInt, method = \"pind\") summary(est2) #> modsem: #> Method = pind #> lavaan 0.6-18 ended normally after 1 iteration #> #> Estimator ML #> Optimization method NLMINB #> Number of model parameters 4 #> #> Number of observations 2000 #> #> Model Test User Model: #> #> Test statistic 0.000 #> Degrees of freedom 0 #> #> Parameter Estimates: #> #> Standard errors Standard #> Information Expected #> Information saturated (h1) model Structured #> #> Regressions: #> Estimate Std.Err z-value P(>|z|) #> y1 ~ #> x1 0.055 0.034 1.619 0.105 #> z1 -0.066 0.035 -1.902 0.057 #> x1z1 0.544 0.023 23.950 0.000 #> #> Variances: #> Estimate Std.Err z-value P(>|z|) #> .y1 1.399 0.044 31.623 0.000"},{"path":"/articles/modsem.html","id":"interactions-between-latent-and-observed-variables","dir":"Articles","previous_headings":"The Basic Syntax","what":"Interactions between Latent and Observed Variables","title":"modsem","text":"modsem also allows estimate interaction effects latent observed variables. , just join latent observed variable colon, e.g., ‘latent:observer’. interactions observed variables, generally recommended use method = “pind” estimating effect observed x latent","code":"m3 <- ' # Outer Model X =~ x1 + x2 +x3 Y =~ y1 + y2 + y3 # Inner model Y ~ X + z1 + X:z1 ' est3 <- modsem(m3, oneInt, method = \"pind\") summary(est3) #> modsem: #> Method = pind #> lavaan 0.6-18 ended normally after 45 iterations #> #> Estimator ML #> Optimization method NLMINB #> Number of model parameters 22 #> #> Number of observations 2000 #> #> Model Test User Model: #> #> Test statistic 4468.171 #> Degrees of freedom 32 #> P-value (Chi-square) 0.000 #> #> Parameter Estimates: #> #> Standard errors Standard #> Information Expected #> Information saturated (h1) model Structured #> #> Latent Variables: #> Estimate Std.Err z-value P(>|z|) #> X =~ #> x1 1.000 #> x2 0.803 0.013 63.697 0.000 #> x3 0.915 0.014 67.548 0.000 #> Y =~ #> y1 1.000 #> y2 0.798 0.007 115.375 0.000 #> y3 0.899 0.007 120.783 0.000 #> Xz1 =~ #> x1z1 1.000 #> x2z1 0.947 0.010 96.034 0.000 #> x3z1 0.902 0.009 99.512 0.000 #> #> Regressions: #> Estimate Std.Err z-value P(>|z|) #> Y ~ #> X 0.021 0.034 0.614 0.540 #> z1 -0.185 0.023 -8.096 0.000 #> Xz1 0.646 0.017 37.126 0.000 #> #> Covariances: #> Estimate Std.Err z-value P(>|z|) #> X ~~ #> Xz1 1.243 0.055 22.523 0.000 #> #> Variances: #> Estimate Std.Err z-value P(>|z|) #> .x1 0.158 0.009 17.976 0.000 #> .x2 0.164 0.007 23.216 0.000 #> .x3 0.162 0.008 20.325 0.000 #> .y1 0.158 0.009 17.819 0.000 #> .y2 0.154 0.007 22.651 0.000 #> .y3 0.164 0.008 20.744 0.000 #> .x1z1 0.315 0.017 18.328 0.000 #> .x2z1 0.428 0.019 22.853 0.000 #> .x3z1 0.337 0.016 21.430 0.000 #> X 0.982 0.036 26.947 0.000 #> .Y 1.112 0.040 27.710 0.000 #> Xz1 3.965 0.136 29.217 0.000"},{"path":"/articles/modsem.html","id":"quadratic-effects","dir":"Articles","previous_headings":"The Basic Syntax","what":"Quadratic Effects","title":"modsem","text":"essence, quadratic effects just special case interaction effects. Thus modsem can also used estimate quadratic effects.","code":"m4 <- ' # Outer Model X =~ x1 + x2 + x3 Y =~ y1 + y2 + y3 Z =~ z1 + z2 + z3 # Inner model Y ~ X + Z + Z:X + X:X ' est4 <- modsem(m4, oneInt, \"qml\") summary(est4) #> Estimating null model #> Starting M-step #> #> modsem (version 1.0.3): #> Estimator QML #> Optimization method NLMINB #> Number of observations 2000 #> Number of iterations 123 #> Loglikelihood -17496.2 #> Akaike (AIC) 35056.4 #> Bayesian (BIC) 35235.63 #> #> Fit Measures for H0: #> Loglikelihood -17832 #> Akaike (AIC) 35723.75 #> Bayesian (BIC) 35891.78 #> Chi-square 17.52 #> Degrees of Freedom (Chi-square) 24 #> P-value (Chi-square) 0.826 #> RMSEA 0.000 #> #> Comparative fit to H0 (no interaction effect) #> Loglikelihood change 335.68 #> Difference test (D) 671.35 #> Degrees of freedom (D) 2 #> P-value (D) 0.000 #> #> R-Squared: #> Y 0.607 #> R-Squared Null-Model (H0): #> Y 0.395 #> R-Squared Change: #> Y 0.212 #> #> Parameter Estimates: #> Coefficients unstandardized #> Information observed #> Standard errors standard #> #> Latent Variables: #> Estimate Std.Error z.value P(>|z|) #> X =~ #> x1 1.000 #> x2 0.803 0.013 63.961 0.000 #> x3 0.914 0.013 67.797 0.000 #> Z =~ #> z1 1.000 #> z2 0.810 0.012 65.124 0.000 #> z3 0.881 0.013 67.621 0.000 #> Y =~ #> y1 1.000 #> y2 0.798 0.007 107.567 0.000 #> y3 0.899 0.008 112.542 0.000 #> #> Regressions: #> Estimate Std.Error z.value P(>|z|) #> Y ~ #> X 0.674 0.032 20.888 0.000 #> Z 0.566 0.030 18.948 0.000 #> X:X -0.005 0.023 -0.207 0.836 #> X:Z 0.713 0.029 24.554 0.000 #> #> Intercepts: #> Estimate Std.Error z.value P(>|z|) #> x1 1.023 0.024 42.894 0.000 #> x2 1.216 0.020 60.996 0.000 #> x3 0.919 0.022 41.484 0.000 #> z1 1.012 0.024 41.576 0.000 #> z2 1.206 0.020 59.271 0.000 #> z3 0.916 0.022 42.063 0.000 #> y1 1.042 0.038 27.684 0.000 #> y2 1.224 0.030 40.159 0.000 #> y3 0.958 0.034 28.101 0.000 #> Y 0.000 #> X 0.000 #> Z 0.000 #> #> Covariances: #> Estimate Std.Error z.value P(>|z|) #> X ~~ #> Z 0.200 0.024 8.239 0.000 #> #> Variances: #> Estimate Std.Error z.value P(>|z|) #> x1 0.158 0.009 18.145 0.000 #> x2 0.162 0.007 23.188 0.000 #> x3 0.165 0.008 20.821 0.000 #> z1 0.166 0.009 18.341 0.000 #> z2 0.159 0.007 22.622 0.000 #> z3 0.158 0.008 20.714 0.000 #> y1 0.159 0.009 17.975 0.000 #> y2 0.154 0.007 22.670 0.000 #> y3 0.164 0.008 20.711 0.000 #> X 0.983 0.036 26.994 0.000 #> Z 1.019 0.038 26.951 0.000 #> Y 0.943 0.038 24.820 0.000"},{"path":"/articles/modsem.html","id":"more-complicated-examples","dir":"Articles","previous_headings":"The Basic Syntax","what":"More Complicated Examples","title":"modsem","text":"can see complicated example using model theory planned behaviour. example included two quadratic- one interaction effect, using included dataset jordan. dataset subset PISA 2006 dataset. Note: approaches work well, might quite slow depending number interaction effects (particularly LMS- constrained approach).","code":"tpb <- ' # Outer Model (Based on Hagger et al., 2007) ATT =~ att1 + att2 + att3 + att4 + att5 SN =~ sn1 + sn2 PBC =~ pbc1 + pbc2 + pbc3 INT =~ int1 + int2 + int3 BEH =~ b1 + b2 # Inner Model (Based on Steinmetz et al., 2011) INT ~ ATT + SN + PBC BEH ~ INT + PBC + INT:PBC ' # the double centering apporach est_tpb <- modsem(tpb, TPB) # using the lms approach est_tpb_lms <- modsem(tpb, TPB, method = \"lms\") #> Warning: It is recommended that you have at least 32 nodes for interaction #> effects between exogenous and endogenous variables in the lms approach 'nodes = #> 24' summary(est_tpb_lms) #> Estimating null model #> EM: Iteration = 1, LogLik = -26393.22, Change = -26393.223 #> EM: Iteration = 2, LogLik = -26393.22, Change = 0.000 #> #> modsem (version 1.0.3): #> Estimator LMS #> Optimization method EM-NLMINB #> Number of observations 2000 #> Number of iterations 103 #> Loglikelihood -23463.37 #> Akaike (AIC) 47034.74 #> Bayesian (BIC) 47337.19 #> #> Numerical Integration: #> Points of integration (per dim) 24 #> Dimensions 1 #> Total points of integration 24 #> #> Fit Measures for H0: #> Loglikelihood -26393 #> Akaike (AIC) 52892.45 #> Bayesian (BIC) 53189.29 #> Chi-square 66.27 #> Degrees of Freedom (Chi-square) 82 #> P-value (Chi-square) 0.897 #> RMSEA 0.000 #> #> Comparative fit to H0 (no interaction effect) #> Loglikelihood change 2929.85 #> Difference test (D) 5859.70 #> Degrees of freedom (D) 1 #> P-value (D) 0.000 #> #> R-Squared: #> INT 0.361 #> BEH 0.248 #> R-Squared Null-Model (H0): #> INT 0.367 #> BEH 0.210 #> R-Squared Change: #> INT -0.006 #> BEH 0.038 #> #> Parameter Estimates: #> Coefficients unstandardized #> Information expected #> Standard errors standard #> #> Latent Variables: #> Estimate Std.Error z.value P(>|z|) #> PBC =~ #> pbc1 1.000 #> pbc2 0.911 0.014 67.47 0.000 #> pbc3 0.802 0.012 65.29 0.000 #> ATT =~ #> att1 1.000 #> att2 0.877 0.012 71.30 0.000 #> att3 0.789 0.012 65.67 0.000 #> att4 0.695 0.011 60.83 0.000 #> att5 0.887 0.013 70.47 0.000 #> SN =~ #> sn1 1.000 #> sn2 0.889 0.017 51.65 0.000 #> INT =~ #> int1 1.000 #> int2 0.913 0.016 58.82 0.000 #> int3 0.807 0.015 55.32 0.000 #> BEH =~ #> b1 1.000 #> b2 0.961 0.033 29.34 0.000 #> #> Regressions: #> Estimate Std.Error z.value P(>|z|) #> INT ~ #> PBC 0.217 0.030 7.30 0.000 #> ATT 0.213 0.026 8.29 0.000 #> SN 0.177 0.028 6.35 0.000 #> BEH ~ #> PBC 0.228 0.022 10.16 0.000 #> INT 0.182 0.025 7.38 0.000 #> PBC:INT 0.204 0.019 10.79 0.000 #> #> Intercepts: #> Estimate Std.Error z.value P(>|z|) #> pbc1 0.959 0.018 52.11 0.000 #> pbc2 0.950 0.017 54.90 0.000 #> pbc3 0.960 0.016 61.08 0.000 #> att1 0.987 0.022 45.68 0.000 #> att2 0.983 0.019 51.10 0.000 #> att3 0.995 0.018 56.12 0.000 #> att4 0.980 0.016 60.13 0.000 #> att5 0.969 0.019 49.85 0.000 #> sn1 0.979 0.022 44.67 0.000 #> sn2 0.987 0.020 50.00 0.000 #> int1 0.995 0.020 48.93 0.000 #> int2 0.995 0.019 52.40 0.000 #> int3 0.990 0.017 56.69 0.000 #> b1 0.989 0.021 47.79 0.000 #> b2 1.008 0.019 51.98 0.000 #> INT 0.000 #> BEH 0.000 #> PBC 0.000 #> ATT 0.000 #> SN 0.000 #> #> Covariances: #> Estimate Std.Error z.value P(>|z|) #> PBC ~~ #> ATT 0.658 0.020 32.58 0.000 #> SN 0.657 0.021 31.11 0.000 #> ATT ~~ #> SN 0.616 0.019 32.97 0.000 #> #> Variances: #> Estimate Std.Error z.value P(>|z|) #> pbc1 0.147 0.008 19.28 0.000 #> pbc2 0.164 0.007 22.15 0.000 #> pbc3 0.154 0.006 24.09 0.000 #> att1 0.167 0.007 23.37 0.000 #> att2 0.150 0.006 24.30 0.000 #> att3 0.159 0.006 26.67 0.000 #> att4 0.163 0.006 27.65 0.000 #> att5 0.159 0.006 24.77 0.000 #> sn1 0.178 0.015 12.09 0.000 #> sn2 0.156 0.012 12.97 0.000 #> int1 0.157 0.009 18.06 0.000 #> int2 0.160 0.008 20.12 0.000 #> int3 0.168 0.007 23.32 0.000 #> b1 0.186 0.020 9.51 0.000 #> b2 0.135 0.018 7.62 0.000 #> PBC 0.933 0.015 60.78 0.000 #> ATT 0.985 0.014 70.25 0.000 #> SN 0.974 0.015 63.87 0.000 #> INT 0.491 0.020 24.34 0.000 #> BEH 0.456 0.023 19.60 0.000 m2 <- ' ENJ =~ enjoy1 + enjoy2 + enjoy3 + enjoy4 + enjoy5 CAREER =~ career1 + career2 + career3 + career4 SC =~ academic1 + academic2 + academic3 + academic4 + academic5 + academic6 CAREER ~ ENJ + SC + ENJ:ENJ + SC:SC + ENJ:SC ' est_jordan <- modsem(m2, data = jordan) est_jordan_qml <- modsem(m2, data = jordan, method = \"qml\") summary(est_jordan_qml) #> Estimating null model #> Starting M-step #> #> modsem (version 1.0.3): #> Estimator QML #> Optimization method NLMINB #> Number of observations 6038 #> Number of iterations 101 #> Loglikelihood -110520.22 #> Akaike (AIC) 221142.45 #> Bayesian (BIC) 221484.44 #> #> Fit Measures for H0: #> Loglikelihood -110521 #> Akaike (AIC) 221138.58 #> Bayesian (BIC) 221460.46 #> Chi-square 1016.34 #> Degrees of Freedom (Chi-square) 87 #> P-value (Chi-square) 0.000 #> RMSEA 0.005 #> #> Comparative fit to H0 (no interaction effect) #> Loglikelihood change 1.07 #> Difference test (D) 2.13 #> Degrees of freedom (D) 3 #> P-value (D) 0.546 #> #> R-Squared: #> CAREER 0.512 #> R-Squared Null-Model (H0): #> CAREER 0.510 #> R-Squared Change: #> CAREER 0.002 #> #> Parameter Estimates: #> Coefficients unstandardized #> Information observed #> Standard errors standard #> #> Latent Variables: #> Estimate Std.Error z.value P(>|z|) #> ENJ =~ #> enjoy1 1.000 #> enjoy2 1.002 0.020 50.587 0.000 #> enjoy3 0.894 0.020 43.669 0.000 #> enjoy4 0.999 0.021 48.227 0.000 #> enjoy5 1.047 0.021 50.400 0.000 #> SC =~ #> academic1 1.000 #> academic2 1.104 0.028 38.946 0.000 #> academic3 1.235 0.030 41.720 0.000 #> academic4 1.254 0.030 41.828 0.000 #> academic5 1.113 0.029 38.647 0.000 #> academic6 1.198 0.030 40.356 0.000 #> CAREER =~ #> career1 1.000 #> career2 1.040 0.016 65.180 0.000 #> career3 0.952 0.016 57.838 0.000 #> career4 0.818 0.017 48.358 0.000 #> #> Regressions: #> Estimate Std.Error z.value P(>|z|) #> CAREER ~ #> ENJ 0.523 0.020 26.286 0.000 #> SC 0.467 0.023 19.884 0.000 #> ENJ:ENJ 0.026 0.022 1.206 0.228 #> ENJ:SC -0.039 0.046 -0.851 0.395 #> SC:SC -0.002 0.035 -0.058 0.953 #> #> Intercepts: #> Estimate Std.Error z.value P(>|z|) #> enjoy1 0.000 0.013 -0.008 0.994 #> enjoy2 0.000 0.015 0.010 0.992 #> enjoy3 0.000 0.013 -0.023 0.982 #> enjoy4 0.000 0.014 0.008 0.993 #> enjoy5 0.000 0.014 0.025 0.980 #> academic1 0.000 0.016 -0.009 0.993 #> academic2 0.000 0.014 -0.009 0.993 #> academic3 0.000 0.015 -0.028 0.978 #> academic4 0.000 0.016 -0.015 0.988 #> academic5 -0.001 0.014 -0.044 0.965 #> academic6 0.001 0.015 0.048 0.962 #> career1 -0.004 0.017 -0.204 0.838 #> career2 -0.004 0.018 -0.248 0.804 #> career3 -0.004 0.017 -0.214 0.830 #> career4 -0.004 0.016 -0.232 0.816 #> CAREER 0.000 #> ENJ 0.000 #> SC 0.000 #> #> Covariances: #> Estimate Std.Error z.value P(>|z|) #> ENJ ~~ #> SC 0.218 0.009 25.477 0.000 #> #> Variances: #> Estimate Std.Error z.value P(>|z|) #> enjoy1 0.487 0.011 44.335 0.000 #> enjoy2 0.488 0.011 44.406 0.000 #> enjoy3 0.596 0.012 48.233 0.000 #> enjoy4 0.488 0.011 44.561 0.000 #> enjoy5 0.442 0.010 42.470 0.000 #> academic1 0.645 0.013 49.813 0.000 #> academic2 0.566 0.012 47.864 0.000 #> academic3 0.473 0.011 44.319 0.000 #> academic4 0.455 0.010 43.579 0.000 #> academic5 0.565 0.012 47.695 0.000 #> academic6 0.502 0.011 45.434 0.000 #> career1 0.373 0.009 40.392 0.000 #> career2 0.328 0.009 37.019 0.000 #> career3 0.436 0.010 43.272 0.000 #> career4 0.576 0.012 48.372 0.000 #> ENJ 0.500 0.017 29.547 0.000 #> SC 0.338 0.015 23.195 0.000 #> CAREER 0.302 0.010 29.711 0.000"},{"path":"/articles/observed_lms_qml.html","id":"the-latent-moderated-structural-equations-lms-and-the-quasi-maximum-likelihood-qml-approach","dir":"Articles","previous_headings":"","what":"The Latent Moderated Structural Equations (LMS) and the Quasi Maximum Likelihood (QML) approach","title":"observed variables in the LMS- and QML approach","text":"contrast approaches, LMS QML approaches designed handle latent variables . Thus observed variables easily used, approaches. One way getting around specifying observed variable latent variable single indicator. modsem() default constrain factor loading 1, residual variance indicator 0. , difference latent variable indicator, (assuming exogenous variable) zero-mean. work LMS- QML approach cases, except two exceptions.","code":""},{"path":"/articles/observed_lms_qml.html","id":"the-lms-approach","dir":"Articles","previous_headings":"The Latent Moderated Structural Equations (LMS) and the Quasi Maximum Likelihood (QML) approach","what":"The LMS approach","title":"observed variables in the LMS- and QML approach","text":"LMS approach can use mentioned approach almost cases, except case wish use observed variable moderating variable. LMS approach, usually select one variable interaction term moderator. interaction effect estimated via numerical integration, n quadrature nodes moderating variable. process however, requires moderating variable error-term, distribution moderating variable modelled X∼N(Az,ε)X \\sim N(Az, \\varepsilon), AzAz expected value XX quadrature point k, ε\\varepsilon error term. error-term zero, probability observing given value XX computable. instances first variable interaction term, chosen moderator. example, interaction term \"X:Z\", \"X\" usually chosen moderator. Thus one variables latent, put latent variable first interaction term. observed, specify measurement error (e.g., “x1 ~~ 0.1 * x1”) indicator first variable interaction term.","code":"library(modsem) # interaction effect between a latent and an observed variable m1 <- ' # Outer Model X =~ x1 # X is observed Z =~ z1 + z2 # Z is latent Y =~ y1 # Y is observed # Inner model Y ~ X + Z Y ~ Z:X ' lms1 <- modsem(m1, oneInt, method = \"lms\") # interaction effect between two observed variables m2 <- ' # Outer Model X =~ x1 # X is observed Z =~ z1 # Z is observed Y =~ y1 # Y is observed x1 ~~ 0.1 * x1 # specify a variance for the measurement error # Inner model Y ~ X + Z Y ~ X:Z ' lms2 <- modsem(m1, oneInt, method = \"lms\") summary(lms2) #> Estimating null model #> EM: Iteration = 1, LogLik = -10816.13, Change = -10816.126 #> EM: Iteration = 2, LogLik = -10816.13, Change = 0.000 #> #> modsem (version 1.0.3): #> Estimator LMS #> Optimization method EM-NLMINB #> Number of observations 2000 #> Number of iterations 58 #> Loglikelihood -8087.2 #> Akaike (AIC) 16202.4 #> Bayesian (BIC) 16280.81 #> #> Numerical Integration: #> Points of integration (per dim) 24 #> Dimensions 1 #> Total points of integration 24 #> #> Fit Measures for H0: #> Loglikelihood -10816 #> Akaike (AIC) 21658.25 #> Bayesian (BIC) 21731.06 #> Chi-square 0.01 #> Degrees of Freedom (Chi-square) 1 #> P-value (Chi-square) 0.917 #> RMSEA 0.000 #> #> Comparative fit to H0 (no interaction effect) #> Loglikelihood change 2728.93 #> Difference test (D) 5457.85 #> Degrees of freedom (D) 1 #> P-value (D) 0.000 #> #> R-Squared: #> Y 0.510 #> R-Squared Null-Model (H0): #> Y 0.343 #> R-Squared Change: #> Y 0.167 #> #> Parameter Estimates: #> Coefficients unstandardized #> Information expected #> Standard errors standard #> #> Latent Variables: #> Estimate Std.Error z.value P(>|z|) #> Z =~ #> z1 1.000 #> z2 0.811 0.018 45.25 0.000 #> X =~ #> x1 1.000 #> Y =~ #> y1 1.000 #> #> Regressions: #> Estimate Std.Error z.value P(>|z|) #> Y ~ #> Z 0.587 0.032 18.10 0.000 #> X 0.574 0.029 19.96 0.000 #> Z:X 0.627 0.026 23.76 0.000 #> #> Intercepts: #> Estimate Std.Error z.value P(>|z|) #> z1 1.009 0.024 42.22 0.000 #> z2 1.203 0.020 60.50 0.000 #> x1 1.023 0.024 43.13 0.000 #> y1 1.046 0.033 31.33 0.000 #> Y 0.000 #> Z 0.000 #> X 0.000 #> #> Covariances: #> Estimate Std.Error z.value P(>|z|) #> Z ~~ #> X 0.211 0.025 8.56 0.000 #> #> Variances: #> Estimate Std.Error z.value P(>|z|) #> z1 0.170 0.018 9.23 0.000 #> z2 0.160 0.013 12.64 0.000 #> x1 0.000 #> y1 0.000 #> Z 1.010 0.020 50.04 0.000 #> X 1.141 0.016 69.82 0.000 #> Y 1.284 0.043 29.70 0.000"},{"path":"/articles/observed_lms_qml.html","id":"the-qml-approach","dir":"Articles","previous_headings":"The Latent Moderated Structural Equations (LMS) and the Quasi Maximum Likelihood (QML) approach","what":"The QML approach","title":"observed variables in the LMS- and QML approach","text":"estimation QML approach different LMS approach, issue LMS approach. Thus don’t specify measurement error moderating variables.","code":"m3 <- ' # Outer Model X =~ x1 # X is observed Z =~ z1 # Z is observed Y =~ y1 # Y is observed # Inner model Y ~ X + Z Y ~ X:Z ' qml3 <- modsem(m3, oneInt, method = \"qml\") summary(qml3) #> Estimating null model #> Starting M-step #> #> modsem (version 1.0.3): #> Estimator QML #> Optimization method NLMINB #> Number of observations 2000 #> Number of iterations 11 #> Loglikelihood -9117.07 #> Akaike (AIC) 18254.13 #> Bayesian (BIC) 18310.14 #> #> Fit Measures for H0: #> Loglikelihood -9369 #> Akaike (AIC) 18756.46 #> Bayesian (BIC) 18806.87 #> Chi-square 0.00 #> Degrees of Freedom (Chi-square) 0 #> P-value (Chi-square) 0.000 #> RMSEA 0.000 #> #> Comparative fit to H0 (no interaction effect) #> Loglikelihood change 252.17 #> Difference test (D) 504.33 #> Degrees of freedom (D) 1 #> P-value (D) 0.000 #> #> R-Squared: #> Y 0.470 #> R-Squared Null-Model (H0): #> Y 0.320 #> R-Squared Change: #> Y 0.150 #> #> Parameter Estimates: #> Coefficients unstandardized #> Information observed #> Standard errors standard #> #> Latent Variables: #> Estimate Std.Error z.value P(>|z|) #> X =~ #> x1 1.000 #> Z =~ #> z1 1.000 #> Y =~ #> y1 1.000 #> #> Regressions: #> Estimate Std.Error z.value P(>|z|) #> Y ~ #> X 0.605 0.028 21.26 0.000 #> Z 0.490 0.028 17.55 0.000 #> X:Z 0.544 0.023 23.95 0.000 #> #> Intercepts: #> Estimate Std.Error z.value P(>|z|) #> x1 1.023 0.024 42.83 0.000 #> z1 1.011 0.024 41.56 0.000 #> y1 1.066 0.034 31.64 0.000 #> Y 0.000 #> X 0.000 #> Z 0.000 #> #> Covariances: #> Estimate Std.Error z.value P(>|z|) #> X ~~ #> Z 0.210 0.026 7.95 0.000 #> #> Variances: #> Estimate Std.Error z.value P(>|z|) #> x1 0.000 #> z1 0.000 #> y1 0.000 #> X 1.141 0.036 31.62 0.000 #> Z 1.184 0.037 31.62 0.000 #> Y 1.399 0.044 31.62 0.000"},{"path":"/articles/plot_interactions.html","id":"plotting-interaction-effects","dir":"Articles","previous_headings":"","what":"Plotting interaction effects","title":"plotting interaction effects","text":"Interaction effects can plotted using included plot_interaction function. function takes fitted model object names two variables interacting. function plot interaction effect two variables. x-variable plotted x-axis y-variable plotted y-axis. z-variable decides points z effect x y plotted. function also plot 95% confidence interval interaction effect. can see simple example using double centering approach. can see different example using LMS approach, theory planned behavior model.","code":"m1 <- \" # Outer Model X =~ x1 X =~ x2 + x3 Z =~ z1 + z2 + z3 Y =~ y1 + y2 + y3 # Inner model Y ~ X + Z + X:Z \" est1 <- modsem(m1, data = oneInt) plot_interaction(\"X\", \"Z\", \"Y\", \"X:Z\", -3:3, c(-0.2, 0), est1) tpb <- \" # Outer Model (Based on Hagger et al., 2007) ATT =~ att1 + att2 + att3 + att4 + att5 SN =~ sn1 + sn2 PBC =~ pbc1 + pbc2 + pbc3 INT =~ int1 + int2 + int3 BEH =~ b1 + b2 # Inner Model (Based on Steinmetz et al., 2011) INT ~ ATT + SN + PBC BEH ~ INT + PBC BEH ~ PBC:INT \" est2 <- modsem(tpb, TPB, method = \"lms\") #> Warning: It is recommended that you have at least 32 nodes for interaction #> effects between exogenous and endogenous variables in the lms approach 'nodes = #> 24' plot_interaction(x = \"INT\", z = \"PBC\", y = \"BEH\", xz = \"PBC:INT\", vals_z = c(-0.5, 0.5), model = est2)"},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Kjell Solem Slupphaug. Author, maintainer.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Solem Slupphaug K (2024). modsem: Latent Interaction (Moderation) Analysis Structural Equation Models (SEM). R package version 1.0.3, https://github.com/Kss2k/modsem.","code":"@Manual{, title = {modsem: Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM)}, author = {Kjell {Solem Slupphaug}}, year = {2024}, note = {R package version 1.0.3}, url = {https://github.com/Kss2k/modsem}, }"},{"path":"/index.html","id":"modsem-","dir":"","previous_headings":"","what":"Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM)","title":"Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM)","text":"package allows perform interactions latent variables (.e., moderation) CB-SEM. See https://kss2k.github.io/intro_modsem/ tutorial.","code":""},{"path":"/index.html","id":"to-install","dir":"","previous_headings":"","what":"To Install","title":"Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM)","text":"","code":"# From CRAN install.packages(\"modsem\") # Latest version from Github install.packages(\"devtools\") devtools::install_github(\"kss2k/modsem\", build_vignettes = TRUE)"},{"path":"/index.html","id":"methodsapproaches","dir":"","previous_headings":"","what":"Methods/Approaches","title":"Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM)","text":"number approaches estimating interaction effects SEM. modsem(), method = \"method\" argument allows choose use. Note constraints can become quite complicated complex models, particularly interaction including enodgenous variables. method can therefore quite slow. \"uca\" = unconstrained approach (Marsh, 2004) \"rca\" = residual centering approach (Little et al., 2006) default \"pind\" = basic product indicator approach (recommended) \"lms\" = Latent Moderated Structural equations (LMS) approach, see vignette \"qml\" = Quasi Maximum Likelihood (QML) approach, see vignette estimates model Mplus, installed","code":""},{"path":[]},{"path":"/index.html","id":"one-interaction","dir":"","previous_headings":"","what":"One interaction","title":"Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM)","text":"","code":"library(modsem) m1 <- ' # Outer Model X =~ x1 + x2 +x3 Y =~ y1 + y2 + y3 Z =~ z1 + z2 + z3 # Inner model Y ~ X + Z + X:Z ' # Double centering approach est1_dca <- modsem(m1, oneInt) summary(est1_dca) # Constrained approach est1_ca <- modsem(m1, oneInt, method = \"ca\") summary(est1_ca) # QML approach est1_qml <- modsem(m1, oneInt, method = \"qml\") summary(est1_qml, standardized = TRUE) # LMS approach est1_lms <- modsem(m1, oneInt, method = \"lms\") summary(est1_lms)"},{"path":"/index.html","id":"theory-of-planned-behavior","dir":"","previous_headings":"","what":"Theory Of Planned Behavior","title":"Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM)","text":"","code":"tpb <- \" # Outer Model (Based on Hagger et al., 2007) ATT =~ att1 + att2 + att3 + att4 + att5 SN =~ sn1 + sn2 PBC =~ pbc1 + pbc2 + pbc3 INT =~ int1 + int2 + int3 BEH =~ b1 + b2 # Inner Model (Based on Steinmetz et al., 2011) # Causal Relationsships INT ~ ATT + SN + PBC BEH ~ INT + PBC BEH ~ PBC:INT \" # double centering approach est_tpb_dca <- modsem(tpb, data = TPB, method = \"dblcent\") summary(est_tpb_dca) # Constrained approach using Wrigths path tracing rules for generating # the appropriate constraints est_tpb_ca <- modsem(tpb, data = TPB, method = \"ca\") summary(est_tpb_ca) # LMS approach est_tpb_lms <- modsem(tpb, data = TPB, method = \"lms\") summary(est_tpb_lms, standardized = TRUE) # QML approach est_tpb_qml <- modsem(tpb, data = TPB, method = \"qml\") summary(est_tpb_qml, standardized = TRUE)"},{"path":"/index.html","id":"interactions-between-two-observed-variables","dir":"","previous_headings":"","what":"Interactions between two observed variables","title":"Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM)","text":"","code":"est2 <- modsem('y1 ~ x1 + z1 + x1:z1', data = oneInt, method = \"pind\") summary(est2) ## Interaction between an obsereved and a latent variable m3 <- ' # Outer Model X =~ x1 + x2 +x3 Y =~ y1 + y2 + y3 # Inner model Y ~ X + z1 + X:z1 ' est3 <- modsem(m3, oneInt, method = \"pind\") summary(est3)"},{"path":"/reference/TPB.html","id":null,"dir":"Reference","previous_headings":"","what":"TPB — TPB","title":"TPB — TPB","text":"simulated dataset based Theory Planned Behaviour","code":""},{"path":"/reference/TPB.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TPB — TPB","text":"","code":"tpb <- ' # Outer Model (Based on Hagger et al., 2007) ATT =~ att1 + att2 + att3 + att4 + att5 SN =~ sn1 + sn2 PBC =~ pbc1 + pbc2 + pbc3 INT =~ int1 + int2 + int3 BEH =~ b1 + b2 # Inner Model (Based on Steinmetz et al., 2011) INT ~ ATT + SN + PBC BEH ~ INT + PBC + INT:PBC ' est <- modsem(tpb, data = TPB)"},{"path":"/reference/TPB_UK.html","id":null,"dir":"Reference","previous_headings":"","what":"TPB_UK — TPB_UK","title":"TPB_UK — TPB_UK","text":"dataset based Theory Planned Behaviour UK sample. 4 variables high communality selected latent variable (ATT, SN, PBC, INT, BEH), two time points (t1 t2).","code":""},{"path":"/reference/TPB_UK.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"TPB_UK — TPB_UK","text":"Gathered replciation study original Hagger et al. (2023). Obtained https://doi.org/10.23668/psycharchives.12187","code":""},{"path":"/reference/TPB_UK.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TPB_UK — TPB_UK","text":"","code":"tpb_uk <- ' # Outer Model (Based on Hagger et al., 2007) ATT =~ att3 + att2 + att1 + att4 SN =~ sn4 + sn2 + sn3 + sn1 PBC =~ pbc2 + pbc1 + pbc3 + pbc4 INT =~ int2 + int1 + int3 + int4 BEH =~ beh3 + beh2 + beh1 + beh4 # Inner Model (Based on Steinmetz et al., 2011) # Causal Relationsships INT ~ ATT + SN + PBC BEH ~ INT + PBC BEH ~ INT:PBC ' est <- modsem(tpb_uk, data = TPB_UK)"},{"path":"/reference/coef_modsem_da.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper for coef — coef_modsem_da","title":"Wrapper for coef — coef_modsem_da","text":"wrapper coef, used modsem::coef_modsem_da, since coef namespace modsem, stats","code":""},{"path":"/reference/coef_modsem_da.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper for coef — coef_modsem_da","text":"","code":"coef_modsem_da(object, ...)"},{"path":"/reference/coef_modsem_da.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper for coef — coef_modsem_da","text":"object fittet model inspect ... additional arguments","code":""},{"path":"/reference/compare_fit.html","id":null,"dir":"Reference","previous_headings":"","what":"compare model fit for qml and lms models — compare_fit","title":"compare model fit for qml and lms models — compare_fit","text":"Compare fit two models using likelihood ratio test. `estH0` representing null hypothesis model, `estH1` alternative hypothesis model. Importantly, function assumes `estH0` free parameters (.e., degrees freedom) `estH1`. alternative hypothesis model","code":""},{"path":"/reference/compare_fit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"compare model fit for qml and lms models — compare_fit","text":"","code":"compare_fit(estH0, estH1)"},{"path":"/reference/compare_fit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"compare model fit for qml and lms models — compare_fit","text":"estH0 object class `modsem_da` representing null hypothesis model estH1 object class `modsem_da` representing ","code":""},{"path":"/reference/compare_fit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"compare model fit for qml and lms models — compare_fit","text":"","code":"if (FALSE) { # \\dontrun{ H0 <- \" # Outer Model X =~ x1 + x2 + x3 Y =~ y1 + y2 + y3 Z =~ z1 + z2 + z3 # Inner model Y ~ X + Z \" estH0 <- modsem(m1, oneInt, \"lms\") H1 <- \" # Outer Model X =~ x1 + x2 + x3 Y =~ y1 + y2 + y3 Z =~ z1 + z2 + z3 # Inner model Y ~ X + Z + X:Z \" estH1 <- modsem(m1, oneInt, \"lms\") compare_fit(estH0, estH1) } # }"},{"path":"/reference/default_settings_da.html","id":null,"dir":"Reference","previous_headings":"","what":"default arguments fro LMS and QML approach — default_settings_da","title":"default arguments fro LMS and QML approach — default_settings_da","text":"function returns default settings LMS QML approach.","code":""},{"path":"/reference/default_settings_da.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"default arguments fro LMS and QML approach — default_settings_da","text":"","code":"default_settings_da(method = c(\"lms\", \"qml\"))"},{"path":"/reference/default_settings_da.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"default arguments fro LMS and QML approach — default_settings_da","text":"method method get settings ","code":""},{"path":"/reference/default_settings_da.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"default arguments fro LMS and QML approach — default_settings_da","text":"list","code":""},{"path":"/reference/default_settings_da.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"default arguments fro LMS and QML approach — default_settings_da","text":"","code":"library(modsem) default_settings_da() #> $lms #> $lms$verbose #> [1] FALSE #> #> $lms$optimize #> [1] TRUE #> #> $lms$nodes #> [1] 24 #> #> $lms$convergence #> [1] 1e-04 #> #> $lms$optimizer #> [1] \"nlminb\" #> #> $lms$center.data #> [1] FALSE #> #> $lms$standardize.data #> [1] FALSE #> #> $lms$standardize.out #> [1] FALSE #> #> $lms$standardize #> [1] FALSE #> #> $lms$mean.observed #> [1] TRUE #> #> $lms$double #> [1] FALSE #> #> $lms$calc.se #> [1] TRUE #> #> $lms$FIM #> [1] \"expected\" #> #> $lms$OFIM.hessian #> [1] FALSE #> #> $lms$EFIM.S #> [1] 30000 #> #> $lms$EFIM.parametric #> [1] TRUE #> #> $lms$robust.se #> [1] FALSE #> #> $lms$max.iter #> [1] 500 #> #> $lms$max.step #> [1] 1 #> #> $lms$fix.estep #> [1] TRUE #> #> $lms$epsilon #> [1] 1e-04 #> #> $lms$quad.range #> [1] Inf #> #> $lms$n.threads #> NULL #> #> #> $qml #> $qml$verbose #> [1] FALSE #> #> $qml$optimize #> [1] TRUE #> #> $qml$nodes #> [1] 0 #> #> $qml$convergence #> [1] 1e-06 #> #> $qml$optimizer #> [1] \"nlminb\" #> #> $qml$center.data #> [1] FALSE #> #> $qml$standardize #> [1] FALSE #> #> $qml$standardize.data #> [1] FALSE #> #> $qml$standardize.out #> [1] FALSE #> #> $qml$mean.observed #> [1] TRUE #> #> $qml$double #> [1] FALSE #> #> $qml$calc.se #> [1] TRUE #> #> $qml$FIM #> [1] \"observed\" #> #> $qml$OFIM.hessian #> [1] TRUE #> #> $qml$EFIM.S #> [1] 30000 #> #> $qml$EFIM.parametric #> [1] TRUE #> #> $qml$robust.se #> [1] FALSE #> #> $qml$max.iter #> [1] 500 #> #> $qml$max.step #> NULL #> #> $qml$fix.estep #> NULL #> #> $qml$epsilon #> [1] 1e-08 #> #> $qml$quad.range #> [1] Inf #> #> $qml$n.threads #> NULL #> #>"},{"path":"/reference/default_settings_pi.html","id":null,"dir":"Reference","previous_headings":"","what":"default arguments for product indicator approaches — default_settings_pi","title":"default arguments for product indicator approaches — default_settings_pi","text":"function returns default settings product indicator approaches","code":""},{"path":"/reference/default_settings_pi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"default arguments for product indicator approaches — default_settings_pi","text":"","code":"default_settings_pi(method = c(\"rca\", \"uca\", \"pind\", \"dblcent\", \"ca\"))"},{"path":"/reference/default_settings_pi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"default arguments for product indicator approaches — default_settings_pi","text":"method method get settings ","code":""},{"path":"/reference/default_settings_pi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"default arguments for product indicator approaches — default_settings_pi","text":"list","code":""},{"path":"/reference/default_settings_pi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"default arguments for product indicator approaches — default_settings_pi","text":"","code":"library(modsem) default_settings_pi() #> $rca #> $rca$center.before #> [1] FALSE #> #> $rca$center.after #> [1] FALSE #> #> $rca$residuals.prods #> [1] TRUE #> #> $rca$residual.cov.syntax #> [1] TRUE #> #> $rca$constrained.prod.mean #> [1] FALSE #> #> $rca$constrained.loadings #> [1] FALSE #> #> $rca$constrained.var #> [1] FALSE #> #> $rca$constrained.res.cov.method #> [1] \"simple\" #> #> $rca$match #> [1] FALSE #> #> #> $uca #> $uca$center.before #> [1] TRUE #> #> $uca$center.after #> [1] FALSE #> #> $uca$residuals.prods #> [1] FALSE #> #> $uca$residual.cov.syntax #> [1] TRUE #> #> $uca$constrained.prod.mean #> [1] TRUE #> #> $uca$constrained.loadings #> [1] FALSE #> #> $uca$constrained.var #> [1] FALSE #> #> $uca$constrained.res.cov.method #> [1] \"simple\" #> #> $uca$match #> [1] FALSE #> #> #> $pind #> $pind$center.before #> [1] FALSE #> #> $pind$center.after #> [1] FALSE #> #> $pind$residuals.prods #> [1] FALSE #> #> $pind$residual.cov.syntax #> [1] FALSE #> #> $pind$constrained.prod.mean #> [1] FALSE #> #> $pind$constrained.loadings #> [1] FALSE #> #> $pind$constrained.var #> [1] FALSE #> #> $pind$constrained.res.cov.method #> [1] \"simple\" #> #> $pind$match #> [1] FALSE #> #> #> $dblcent #> $dblcent$center.before #> [1] TRUE #> #> $dblcent$center.after #> [1] TRUE #> #> $dblcent$residuals.prods #> [1] FALSE #> #> $dblcent$residual.cov.syntax #> [1] TRUE #> #> $dblcent$constrained.prod.mean #> [1] FALSE #> #> $dblcent$constrained.loadings #> [1] FALSE #> #> $dblcent$constrained.var #> [1] FALSE #> #> $dblcent$constrained.res.cov.method #> [1] \"simple\" #> #> $dblcent$match #> [1] FALSE #> #> #> $ca #> $ca$center.before #> [1] TRUE #> #> $ca$center.after #> [1] FALSE #> #> $ca$residuals.prods #> [1] FALSE #> #> $ca$residual.cov.syntax #> [1] TRUE #> #> $ca$constrained.prod.mean #> [1] TRUE #> #> $ca$constrained.loadings #> [1] TRUE #> #> $ca$constrained.var #> [1] TRUE #> #> $ca$constrained.res.cov.method #> [1] \"ca\" #> #> $ca$match #> [1] TRUE #> #>"},{"path":"/reference/extract_lavaan.html","id":null,"dir":"Reference","previous_headings":"","what":"extract lavaan object from modsem object estimated using product indicators — extract_lavaan","title":"extract lavaan object from modsem object estimated using product indicators — extract_lavaan","text":"extract lavaan object modsem object estimated using product indicators","code":""},{"path":"/reference/extract_lavaan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"extract lavaan object from modsem object estimated using product indicators — extract_lavaan","text":"","code":"extract_lavaan(object)"},{"path":"/reference/extract_lavaan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"extract lavaan object from modsem object estimated using product indicators — extract_lavaan","text":"object modsem object","code":""},{"path":"/reference/extract_lavaan.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"extract lavaan object from modsem object estimated using product indicators — extract_lavaan","text":"lavaan object","code":""},{"path":"/reference/extract_lavaan.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"extract lavaan object from modsem object estimated using product indicators — extract_lavaan","text":"","code":"library(modsem) m1 <- ' # Outer Model X =~ x1 + x2 + x3 Y =~ y1 + y2 + y3 Z =~ z1 + z2 + z3 # Inner model Y ~ X + Z + X:Z ' est <- modsem_pi(m1, oneInt) lav_est <- extract_lavaan(est)"},{"path":"/reference/fit_modsem_da.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit measures for QML and LMS models — fit_modsem_da","title":"Fit measures for QML and LMS models — fit_modsem_da","text":"Calculates chi-sq test p-value, well RMSEA LMS QML models. Note Chi-Square based fit measures calculated baseline model, .e., model without interaction effect","code":""},{"path":"/reference/fit_modsem_da.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit measures for QML and LMS models — fit_modsem_da","text":"","code":"fit_modsem_da(model, chisq = TRUE)"},{"path":"/reference/fit_modsem_da.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit measures for QML and LMS models — fit_modsem_da","text":"model fitted model. Thereafter, can use 'compare_fit()' assess comparative fit models. interaction effect makes model better, e.g., RMSEA good baseline model, interaction model likely good RMSEA well. chisq Chi-Square based fit-measures calculated?","code":""},{"path":"/reference/get_pi_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Get data with product indicators for different approaches — get_pi_data","title":"Get data with product indicators for different approaches — get_pi_data","text":"get_pi_syntax function creating lavaan syntax used estimating latent interaction models using one product indiactors lavaan.","code":""},{"path":"/reference/get_pi_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get data with product indicators for different approaches — get_pi_data","text":"","code":"get_pi_data(model.syntax, data, method = \"dblcent\", match = FALSE, ...)"},{"path":"/reference/get_pi_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get data with product indicators for different approaches — get_pi_data","text":"model.syntax lavaan syntax data data create product indicators method method use: \"rca\" = residual centering approach, \"uca\" = unconstrained approach, \"dblcent\" = double centering approach, \"pind\" = prod ind approach, constraints centering, \"custom\" = use parameters specified function call match product indicators made using match strategy ... arguments passed functions (e.g., modsem_pi)","code":""},{"path":"/reference/get_pi_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get data with product indicators for different approaches — get_pi_data","text":"data.frame","code":""},{"path":"/reference/get_pi_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get data with product indicators for different approaches — get_pi_data","text":"","code":"library(modsem) library(lavaan) #> This is lavaan 0.6-18 #> lavaan is FREE software! Please report any bugs. m1 <- ' # Outer Model X =~ x1 + x2 +x3 Y =~ y1 + y2 + y3 Z =~ z1 + z2 + z3 # Inner model Y ~ X + Z + X:Z ' syntax <- get_pi_syntax(m1) data <- get_pi_data(m1, oneInt) est <- sem(syntax, data)"},{"path":"/reference/get_pi_syntax.html","id":null,"dir":"Reference","previous_headings":"","what":"Get lavaan syntax for product indicator approaches — get_pi_syntax","title":"Get lavaan syntax for product indicator approaches — get_pi_syntax","text":"get_pi_syntax function creating lavaan syntax used estimating latent interaction models using one product indiactors lavaan.","code":""},{"path":"/reference/get_pi_syntax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get lavaan syntax for product indicator approaches — get_pi_syntax","text":"","code":"get_pi_syntax(model.syntax, method = \"dblcent\", match = FALSE, ...)"},{"path":"/reference/get_pi_syntax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get lavaan syntax for product indicator approaches — get_pi_syntax","text":"model.syntax lavaan syntax method method use: \"rca\" = residual centering approach, \"uca\" = unconstrained approach, \"dblcent\" = double centering approach, \"pind\" = prod ind approach, constraints centering, \"custom\" = use parameters specified function call match product indicators made using match strategy ... arguments passed functions (e.g., modsem_pi)","code":""},{"path":"/reference/get_pi_syntax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get lavaan syntax for product indicator approaches — get_pi_syntax","text":"character vector","code":""},{"path":"/reference/get_pi_syntax.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get lavaan syntax for product indicator approaches — get_pi_syntax","text":"","code":"library(modsem) library(lavaan) m1 <- ' # Outer Model X =~ x1 + x2 +x3 Y =~ y1 + y2 + y3 Z =~ z1 + z2 + z3 # Inner model Y ~ X + Z + X:Z ' syntax <- get_pi_syntax(m1) data <- get_pi_data(m1, oneInt) est <- sem(syntax, data)"},{"path":"/reference/jordan.html","id":null,"dir":"Reference","previous_headings":"","what":"Jordan subset of PISA 2006 data — jordan","title":"Jordan subset of PISA 2006 data — jordan","text":"data stem large-scale assessment study PISA 2006 (Organisation Economic Co-Operation Development, 2009) competencies 15-year-old students reading, mathematics, science assessed using nationally representative samples 3-year cycles. eacademicample, data student background questionnaire Jordan sample PISA 2006 used. data students complete responses 15 items (N = 6,038) considered.","code":""},{"path":"/reference/jordan.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Jordan subset of PISA 2006 data — jordan","text":"data frame fifteen variables 6,038 observations: enjoy1 indicator enjoyment science, item ST16Q01: generally fun learning topics. enjoy2 indicator enjoyment science, item ST16Q02: like reading . enjoy3 indicator enjoyment science, item ST16Q03: happy problems. enjoy4 indicator enjoyment science, item ST16Q04: enjoy acquiring new knowledge . enjoy5 indicator enjoyment science, item ST16Q05: interested learning . academic1 indicator academic self-concept science, item ST37Q01: can easily understand new ideas . academic2 indicator academic self-concept science, item ST37Q02: Learning advanced topics easy . academic3 indicator academic self-concept science, item ST37Q03: can usually give good answers topics. academic4 indicator academic self-concept science, item ST37Q04: learn topics quickly. academic5 indicator academic self-concept science, item ST37Q05: topics easy . academic6 indicator academic self-concept science, item ST37Q06: taught , can understand concepts well. career1 indicator career aspirations science, item ST29Q01: like work career involving . career2 indicator career aspirations science, item ST29Q02: like study . career3 indicator career aspirations science, item ST29Q03: like spend life advanced . career4 indicator career aspirations science, item ST29Q04: like work projects adult.","code":""},{"path":"/reference/jordan.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Jordan subset of PISA 2006 data — jordan","text":"version dataset, well description gathered documentation 'nlsem' package (https://cran.r-project.org/package=nlsem), difference names variables changed Originally dataset gathered Organisation Economic Co-Operation Development (2009). Pisa 2006: Science competencies tomorrow's world (Tech. Rep.). Paris, France. Obtained : https://www.oecd.org/pisa/pisaproducts/database-pisa2006.htm","code":""},{"path":"/reference/jordan.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Jordan subset of PISA 2006 data — jordan","text":"","code":"if (FALSE) { # \\dontrun{ m1 <- ' ENJ =~ enjoy1 + enjoy2 + enjoy3 + enjoy4 + enjoy5 CAREER =~ career1 + career2 + career3 + career4 SC =~ academic1 + academic2 + academic3 + academic4 + academic5 + academic6 CAREER ~ ENJ + SC + ENJ:ENJ + SC:SC + ENJ:SC ' est <- modsem(m1, data = jordan) } # }"},{"path":"/reference/modsem-package.html","id":null,"dir":"Reference","previous_headings":"","what":"modsem: Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM) — modsem-package","title":"modsem: Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM) — modsem-package","text":"Estimation interaction (.e., moderation) effects latent variables structural equation models (SEM). supported methods : constrained approach (Algina & Moulder, 2001). unconstrained approach (Marsh et al., 2004). residual centering approach (Little et al., 2006). double centering approach (Lin et al., 2010). latent moderated structural equations (LMS) approach (Klein & Moosbrugger, 2000). quasi-maximum likelihood (QML) approach (Klein & Muthén, 2007) (temporarily unavailable) constrained- unconstrained, residual- double centering- approaches estimated via 'lavaan' (Rosseel, 2012), whilst LMS- QML- approaches estimated via modsem self. Alternatively model can estimated via 'Mplus' (Muthén & Muthén, 1998-2017). References: Algina, J., & Moulder, B. C. (2001). doi:10.1207/S15328007SEM0801_3 . \"note estimating Jöreskog-Yang model latent variable interaction using 'LISREL' 8.3.\" Klein, ., & Moosbrugger, H. (2000). doi:10.1007/BF02296338 . \"Maximum likelihood estimation latent interaction effects LMS method.\" Klein, . G., & Muthén, B. O. (2007). doi:10.1080/00273170701710205 . \"Quasi-maximum likelihood estimation structural equation models multiple interaction quadratic effects.\" Lin, G. C., Wen, Z., Marsh, H. W., & Lin, H. S. (2010). doi:10.1080/10705511.2010.488999 . \"Structural equation models latent interactions: Clarification orthogonalizing double-mean-centering strategies.\" Little, T. D., Bovaird, J. ., & Widaman, K. F. (2006). doi:10.1207/s15328007sem1304_1 . \"merits orthogonalizing powered product terms: Implications modeling interactions among latent variables.\" Marsh, H. W., Wen, Z., & Hau, K. T. (2004). doi:10.1037/1082-989X.9.3.275 . \"Structural equation models latent interactions: evaluation alternative estimation strategies indicator construction.\" Muthén, L.K. Muthén, B.O. (1998-2017). \"'Mplus' User’s Guide. Eighth Edition.\" https://www.statmodel.com/. Rosseel Y (2012). doi:10.18637/jss.v048.i02 . \"'lavaan': R Package Structural Equation Modeling.\"","code":""},{"path":[]},{"path":"/reference/modsem-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"modsem: Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM) — modsem-package","text":"Maintainer: Kjell Solem Slupphaug slupphaugkjell@gmail.com (ORCID)","code":""},{"path":"/reference/modsem.html","id":null,"dir":"Reference","previous_headings":"","what":"Interaction between latent variables — modsem","title":"Interaction between latent variables — modsem","text":"modsem function estimating interaction effects latent variables, structural equation models (SEM's). Methods estimating interaction effects SEM's can basically split two frameworks: 1. Product Indicator based approaches (\"dblcent\", \"rca\", \"uca\", \"ca\", \"pind\"), 2. Distributionally based approaches (\"lms\", \"qml\"). product indicator based approaces, modsem() essentially just fancy wrapper lavaan::sem() generates necessary syntax, variables estimation models latent product indicators. distributionally based approaches implemented seperately, estimated using lavaan::sem(), rather using custom functions (largely) written C++ performance reasons. greater control, advised use one sub-functions (modsem_pi, modsem_da, modsem_mplus) directly, passing additional arguments via modsem() can lead unexpected behavior.","code":""},{"path":"/reference/modsem.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interaction between latent variables — modsem","text":"","code":"modsem(model.syntax = NULL, data = NULL, method = \"dblcent\", ...)"},{"path":"/reference/modsem.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interaction between latent variables — modsem","text":"model.syntax lavaan syntax data dataframe method method use: \"rca\" = residual centering approach (passed lavaan), \"uca\" = unconstrained approach (passed lavaan), \"dblcent\" = double centering approach (passed lavaan), \"pind\" = prod ind approach, constraints centering (passed lavaan), \"lms\" = laten model structural equations (passed lavaan). \"qml\" = quasi maximum likelihood estimation laten model structural equations (passed lavaan). \"custom\" = use parameters specified function call (passed lavaan) ... arguments passed functions depending method (see modsem_pi, modsem_da, modsem_mplus)","code":""},{"path":"/reference/modsem.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interaction between latent variables — modsem","text":"modsem object","code":""},{"path":"/reference/modsem.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Interaction between latent variables — modsem","text":"","code":"library(modsem) # For more examples check README and/or GitHub. # One interaction m1 <- ' # Outer Model X =~ x1 + x2 +x3 Y =~ y1 + y2 + y3 Z =~ z1 + z2 + z3 # Inner model Y ~ X + Z + X:Z ' # Double centering approach est1 <- modsem(m1, oneInt) summary(est1) #> modsem: #> Method = dblcent #> lavaan 0.6-18 ended normally after 159 iterations #> #> Estimator ML #> Optimization method NLMINB #> Number of model parameters 60 #> #> Number of observations 2000 #> #> Model Test User Model: #> #> Test statistic 122.924 #> Degrees of freedom 111 #> P-value (Chi-square) 0.207 #> #> Parameter Estimates: #> #> Standard errors Standard #> Information Expected #> Information saturated (h1) model Structured #> #> Latent Variables: #> Estimate Std.Err z-value P(>|z|) #> X =~ #> x1 1.000 #> x2 0.804 0.013 63.612 0.000 #> x3 0.916 0.014 67.144 0.000 #> Y =~ #> y1 1.000 #> y2 0.798 0.007 107.428 0.000 #> y3 0.899 0.008 112.453 0.000 #> Z =~ #> z1 1.000 #> z2 0.812 0.013 64.763 0.000 #> z3 0.882 0.013 67.014 0.000 #> XZ =~ #> x1z1 1.000 #> x2z1 0.805 0.013 60.636 0.000 #> x3z1 0.877 0.014 62.680 0.000 #> x1z2 0.793 0.013 59.343 0.000 #> x2z2 0.646 0.015 43.672 0.000 #> x3z2 0.706 0.016 44.292 0.000 #> x1z3 0.887 0.014 63.700 0.000 #> x2z3 0.716 0.016 45.645 0.000 #> x3z3 0.781 0.017 45.339 0.000 #> #> Regressions: #> Estimate Std.Err z-value P(>|z|) #> Y ~ #> X 0.675 0.027 25.379 0.000 #> Z 0.561 0.026 21.606 0.000 #> XZ 0.702 0.027 26.360 0.000 #> #> Covariances: #> Estimate Std.Err z-value P(>|z|) #> .x1z1 ~~ #> .x2z2 0.000 #> .x2z3 0.000 #> .x3z2 0.000 #> .x3z3 0.000 #> .x2z1 ~~ #> .x1z2 0.000 #> .x1z2 ~~ #> .x2z3 0.000 #> .x3z1 ~~ #> .x1z2 0.000 #> .x1z2 ~~ #> .x3z3 0.000 #> .x2z1 ~~ #> .x1z3 0.000 #> .x2z2 ~~ #> .x1z3 0.000 #> .x3z1 ~~ #> .x1z3 0.000 #> .x3z2 ~~ #> .x1z3 0.000 #> .x2z1 ~~ #> .x3z2 0.000 #> .x3z3 0.000 #> .x3z1 ~~ #> .x2z2 0.000 #> .x2z2 ~~ #> .x3z3 0.000 #> .x3z1 ~~ #> .x2z3 0.000 #> .x3z2 ~~ #> .x2z3 0.000 #> .x1z1 ~~ #> .x1z2 0.115 0.008 14.802 0.000 #> .x1z3 0.114 0.008 13.947 0.000 #> .x2z1 0.125 0.008 16.095 0.000 #> .x3z1 0.140 0.009 16.135 0.000 #> .x1z2 ~~ #> .x1z3 0.103 0.007 14.675 0.000 #> .x2z2 0.128 0.006 20.850 0.000 #> .x3z2 0.146 0.007 21.243 0.000 #> .x1z3 ~~ #> .x2z3 0.116 0.007 17.818 0.000 #> .x3z3 0.135 0.007 18.335 0.000 #> .x2z1 ~~ #> .x2z2 0.135 0.006 20.905 0.000 #> .x2z3 0.145 0.007 21.145 0.000 #> .x3z1 0.114 0.007 16.058 0.000 #> .x2z2 ~~ #> .x2z3 0.117 0.006 20.419 0.000 #> .x3z2 0.116 0.006 20.586 0.000 #> .x2z3 ~~ #> .x3z3 0.109 0.006 18.059 0.000 #> .x3z1 ~~ #> .x3z2 0.138 0.007 19.331 0.000 #> .x3z3 0.158 0.008 20.269 0.000 #> .x3z2 ~~ #> .x3z3 0.131 0.007 19.958 0.000 #> X ~~ #> Z 0.201 0.024 8.271 0.000 #> XZ 0.016 0.025 0.628 0.530 #> Z ~~ #> XZ 0.062 0.025 2.449 0.014 #> #> Variances: #> Estimate Std.Err z-value P(>|z|) #> .x1 0.160 0.009 17.871 0.000 #> .x2 0.162 0.007 22.969 0.000 #> .x3 0.163 0.008 20.161 0.000 #> .y1 0.159 0.009 17.896 0.000 #> .y2 0.154 0.007 22.640 0.000 #> .y3 0.164 0.008 20.698 0.000 #> .z1 0.168 0.009 18.143 0.000 #> .z2 0.158 0.007 22.264 0.000 #> .z3 0.158 0.008 20.389 0.000 #> .x1z1 0.311 0.014 22.227 0.000 #> .x2z1 0.292 0.011 27.287 0.000 #> .x3z1 0.327 0.012 26.275 0.000 #> .x1z2 0.290 0.011 26.910 0.000 #> .x2z2 0.239 0.008 29.770 0.000 #> .x3z2 0.270 0.009 29.117 0.000 #> .x1z3 0.272 0.012 23.586 0.000 #> .x2z3 0.245 0.009 27.979 0.000 #> .x3z3 0.297 0.011 28.154 0.000 #> X 0.981 0.036 26.895 0.000 #> .Y 0.990 0.038 25.926 0.000 #> Z 1.016 0.038 26.856 0.000 #> XZ 1.045 0.044 24.004 0.000 #> if (FALSE) { # \\dontrun{ # The Constrained Approach est1_ca <- modsem(m1, oneInt, method = \"ca\") summary(est1_ca) # LMS approach est1_lms <- modsem(m1, oneInt, method = \"lms\") summary(est1_lms) # QML approach est1_qml <- modsem(m1, oneInt, method = \"qml\") summary(est1_qml) } # } # Theory Of Planned Behavior tpb <- ' # Outer Model (Based on Hagger et al., 2007) ATT =~ att1 + att2 + att3 + att4 + att5 SN =~ sn1 + sn2 PBC =~ pbc1 + pbc2 + pbc3 INT =~ int1 + int2 + int3 BEH =~ b1 + b2 # Inner Model (Based on Steinmetz et al., 2011) INT ~ ATT + SN + PBC BEH ~ INT + PBC BEH ~ INT:PBC ' # double centering approach est_tpb <- modsem(tpb, data = TPB) summary(est_tpb) #> modsem: #> Method = dblcent #> lavaan 0.6-18 ended normally after 171 iterations #> #> Estimator ML #> Optimization method NLMINB #> Number of model parameters 78 #> #> Number of observations 2000 #> #> Model Test User Model: #> #> Test statistic 207.615 #> Degrees of freedom 222 #> P-value (Chi-square) 0.747 #> #> Parameter Estimates: #> #> Standard errors Standard #> Information Expected #> Information saturated (h1) model Structured #> #> Latent Variables: #> Estimate Std.Err z-value P(>|z|) #> ATT =~ #> att1 1.000 #> att2 0.878 0.012 71.509 0.000 #> att3 0.789 0.012 66.368 0.000 #> att4 0.695 0.011 61.017 0.000 #> att5 0.887 0.013 70.884 0.000 #> SN =~ #> sn1 1.000 #> sn2 0.889 0.017 52.553 0.000 #> PBC =~ #> pbc1 1.000 #> pbc2 0.912 0.013 69.500 0.000 #> pbc3 0.801 0.012 65.830 0.000 #> INT =~ #> int1 1.000 #> int2 0.914 0.016 58.982 0.000 #> int3 0.808 0.015 55.547 0.000 #> BEH =~ #> b1 1.000 #> b2 0.960 0.030 31.561 0.000 #> INTPBC =~ #> int1pbc1 1.000 #> int2pbc1 0.931 0.015 63.809 0.000 #> int3pbc1 0.774 0.013 60.107 0.000 #> int1pbc2 0.893 0.013 68.173 0.000 #> int2pbc2 0.826 0.017 48.845 0.000 #> int3pbc2 0.690 0.015 45.300 0.000 #> int1pbc3 0.799 0.012 67.008 0.000 #> int2pbc3 0.738 0.015 47.809 0.000 #> int3pbc3 0.622 0.014 45.465 0.000 #> #> Regressions: #> Estimate Std.Err z-value P(>|z|) #> INT ~ #> ATT 0.213 0.026 8.170 0.000 #> SN 0.177 0.028 6.416 0.000 #> PBC 0.217 0.030 7.340 0.000 #> BEH ~ #> INT 0.191 0.024 7.817 0.000 #> PBC 0.230 0.022 10.507 0.000 #> INTPBC 0.204 0.018 11.425 0.000 #> #> Covariances: #> Estimate Std.Err z-value P(>|z|) #> .int1pbc1 ~~ #> .int2pbc2 0.000 #> .int2pbc3 0.000 #> .int3pbc2 0.000 #> .int3pbc3 0.000 #> .int2pbc1 ~~ #> .int1pbc2 0.000 #> .int1pbc2 ~~ #> .int2pbc3 0.000 #> .int3pbc1 ~~ #> .int1pbc2 0.000 #> .int1pbc2 ~~ #> .int3pbc3 0.000 #> .int2pbc1 ~~ #> .int1pbc3 0.000 #> .int2pbc2 ~~ #> .int1pbc3 0.000 #> .int3pbc1 ~~ #> .int1pbc3 0.000 #> .int3pbc2 ~~ #> .int1pbc3 0.000 #> .int2pbc1 ~~ #> .int3pbc2 0.000 #> .int3pbc3 0.000 #> .int3pbc1 ~~ #> .int2pbc2 0.000 #> .int2pbc2 ~~ #> .int3pbc3 0.000 #> .int3pbc1 ~~ #> .int2pbc3 0.000 #> .int3pbc2 ~~ #> .int2pbc3 0.000 #> .int1pbc1 ~~ #> .int1pbc2 0.126 0.009 14.768 0.000 #> .int1pbc3 0.102 0.007 13.794 0.000 #> .int2pbc1 0.104 0.007 14.608 0.000 #> .int3pbc1 0.091 0.006 14.109 0.000 #> .int1pbc2 ~~ #> .int1pbc3 0.095 0.007 13.852 0.000 #> .int2pbc2 0.128 0.007 19.320 0.000 #> .int3pbc2 0.119 0.006 19.402 0.000 #> .int1pbc3 ~~ #> .int2pbc3 0.110 0.006 19.911 0.000 #> .int3pbc3 0.097 0.005 19.415 0.000 #> .int2pbc1 ~~ #> .int2pbc2 0.152 0.008 18.665 0.000 #> .int2pbc3 0.138 0.007 18.779 0.000 #> .int3pbc1 0.082 0.006 13.951 0.000 #> .int2pbc2 ~~ #> .int2pbc3 0.121 0.007 18.361 0.000 #> .int3pbc2 0.104 0.005 19.047 0.000 #> .int2pbc3 ~~ #> .int3pbc3 0.087 0.005 19.180 0.000 #> .int3pbc1 ~~ #> .int3pbc2 0.139 0.007 21.210 0.000 #> .int3pbc3 0.123 0.006 21.059 0.000 #> .int3pbc2 ~~ #> .int3pbc3 0.114 0.005 21.021 0.000 #> ATT ~~ #> SN 0.629 0.029 21.977 0.000 #> PBC 0.678 0.029 23.721 0.000 #> INTPBC 0.086 0.024 3.519 0.000 #> SN ~~ #> PBC 0.678 0.029 23.338 0.000 #> INTPBC 0.055 0.025 2.230 0.026 #> PBC ~~ #> INTPBC 0.087 0.024 3.609 0.000 #> #> Variances: #> Estimate Std.Err z-value P(>|z|) #> .att1 0.167 0.007 23.528 0.000 #> .att2 0.150 0.006 24.693 0.000 #> .att3 0.160 0.006 26.378 0.000 #> .att4 0.163 0.006 27.649 0.000 #> .att5 0.159 0.006 24.930 0.000 #> .sn1 0.178 0.015 12.110 0.000 #> .sn2 0.156 0.012 13.221 0.000 #> .pbc1 0.145 0.008 18.440 0.000 #> .pbc2 0.160 0.007 21.547 0.000 #> .pbc3 0.154 0.007 23.716 0.000 #> .int1 0.158 0.009 18.152 0.000 #> .int2 0.160 0.008 20.345 0.000 #> .int3 0.167 0.007 23.414 0.000 #> .b1 0.186 0.018 10.058 0.000 #> .b2 0.135 0.017 8.080 0.000 #> .int1pbc1 0.266 0.013 20.971 0.000 #> .int2pbc1 0.292 0.012 24.421 0.000 #> .int3pbc1 0.251 0.010 26.305 0.000 #> .int1pbc2 0.290 0.012 24.929 0.000 #> .int2pbc2 0.269 0.010 26.701 0.000 #> .int3pbc2 0.253 0.009 29.445 0.000 #> .int1pbc3 0.223 0.009 24.431 0.000 #> .int2pbc3 0.234 0.008 27.633 0.000 #> .int3pbc3 0.203 0.007 29.288 0.000 #> ATT 0.998 0.037 27.138 0.000 #> SN 0.987 0.039 25.394 0.000 #> PBC 0.962 0.035 27.260 0.000 #> .INT 0.490 0.020 24.638 0.000 #> .BEH 0.455 0.023 20.068 0.000 #> INTPBC 1.020 0.041 24.612 0.000 #> if (FALSE) { # \\dontrun{ # The Constrained Approach est_tpb_ca <- modsem(tpb, data = TPB, method = \"ca\") summary(est_tpb_ca) # LMS approach est_tpb_lms <- modsem(tpb, data = TPB, method = \"lms\") summary(est_tpb_lms) # QML approach est_tpb_qml <- modsem(tpb, data = TPB, method = \"qml\") summary(est_tpb_qml) } # }"},{"path":"/reference/modsem_da.html","id":null,"dir":"Reference","previous_headings":"","what":"Interaction between latent variables using lms and qml approaches — modsem_da","title":"Interaction between latent variables using lms and qml approaches — modsem_da","text":"modsem_da function estimating interaction effects latent variables, structural equation models (SEMs), using distributional analytic (DA) approaches. Methods estimating interaction effects SEM's can basically split two frameworks: 1. Product Indicator based approaches (\"dblcent\", \"rca\", \"uca\", \"ca\", \"pind\"), 2. Distributionally based approaches (\"lms\", \"qml\"). modsem_da() handles latter, can estimate models using qml lms necessary syntax, variables estimation models latent product indicators. NOTE: run 'default_settings_da()' see default arguments.","code":""},{"path":"/reference/modsem_da.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interaction between latent variables using lms and qml approaches — modsem_da","text":"","code":"modsem_da( model.syntax = NULL, data = NULL, method = \"lms\", verbose = NULL, optimize = NULL, nodes = NULL, convergence = NULL, optimizer = NULL, center.data = NULL, standardize.data = NULL, standardize.out = NULL, standardize = NULL, mean.observed = NULL, cov.syntax = NULL, double = NULL, calc.se = NULL, FIM = NULL, EFIM.S = NULL, OFIM.hessian = NULL, EFIM.parametric = NULL, robust.se = NULL, max.iter = NULL, max.step = NULL, fix.estep = NULL, start = NULL, epsilon = NULL, quad.range = NULL, n.threads = NULL, ... )"},{"path":"/reference/modsem_da.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interaction between latent variables using lms and qml approaches — modsem_da","text":"model.syntax lavaan syntax data dataframe method method use: \"lms\" = laten model structural equations (passed lavaan). \"qml\" = quasi maximum likelihood estimation laten model structural equations (passed lavaan). verbose estimation progress shown optimize starting parameters optimized nodes number quadrature nodes (points integration) used lms, increased number gives better estimates slower computation. many needed, depends complexity model simple models somwhere 16-24 enough, complex higher numbers may needed. models interaction effects endogenous exogenous variable number nodes least 32, practically (e.g., ordinal/skewed data) 32 recommended. cases, data non-normal might better use qml approach instead. large numbers nodes, might want change 'quad.range' argument. convergence convergence criterion. Lower values give better estimates slower computation. optimizer optimizer use, can either \"nlminb\" \"L-BFGS-B\". LMS, \"nlminb\" recommended. QML, \"L-BFGS-B\" may faster large number iterations, slower iterations. center.data data centered fitting model standardize.data data scaled fitting model, overridden standardize standardize set TRUE. NOTE: recommended estimate model normally standardize output using `standardized_estimates()`. standardize.output standardized (note alter relationsships parameter constraints, since parameters scaled unevenly, even label). alter estimation model, output. NOTE: recommended estimate model normally standardize output using `standardized_estimates()`. standardize standardize data fitting model, remove mean structure observed variables, standardize output. Note standardize.data mean.observed, standardize.overridden standardize standardize set TRUE. NOTE: recommended estimate model normally standardize output using `standardized_estimates()`. mean.observed mean structure observed variables estimated, overridden standardize standardize set TRUE. NOTE: recommended unless know . cov.syntax model syntax implied covariance matrix (see 'vignette(\"interaction_two_etas\", \"modsem\")') double try double number dimensions integrations used LMS, extremely slow, similar mplus. calc.se standard errros computed, NOTE: 'FALSE' information matrix computed either FIM fisher information matrix calculated using observed expected. must either \"observed\" \"expected\" EFIM.S expected fisher information matrix computed, EFIM.S selects sample size generated data OFIM.hessian observed fisher information computed using hessian? FALSE, computed using gradient EFIM.parametric data calculating expected fisher information matrix simulated parametrically (simulated based assumptions- implied parameters model), non-parametrically (stochastically sampled). believe normality assumptions violated, 'EFIM.parametric = FALSE' might better option. robust.se robust standard errors computed? Meant used QML, can unreliable LMS-approach. max.iter max numebr iterations max.step max steps M-step EM algorithm (LMS) fix.estep TRUE, E-step fixed prior probabilities set best prior probabilities, loglikelihood decreasing 30 iterations. start starting parameters epsilon finite difference numerical derivatives quad.range range z-scores perform numerical integration LMS using Gaussian-Hermite Quadratures. default Inf, f(t) integrated -Inf Inf, likely inefficient pointless large number nodes. Nodes outside +/- quad.range ignored. n.threads number cores use parallel processing, NULL, use <= 2 threads, integer specified, use number threads (e.g., `n.threads = 4`, use 4 threads) = \"default\" use default number threads (2). = \"max\" use available threads, \"min\" use 1 thread. ... additional arguments passed estimation function","code":""},{"path":"/reference/modsem_da.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interaction between latent variables using lms and qml approaches — modsem_da","text":"modsem_da object","code":""},{"path":"/reference/modsem_da.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Interaction between latent variables using lms and qml approaches — modsem_da","text":"","code":"library(modsem) # For more examples check README and/or GitHub. # One interaction m1 <- \" # Outer Model X =~ x1 + x2 +x3 Y =~ y1 + y2 + y3 Z =~ z1 + z2 + z3 # Inner model Y ~ X + Z + X:Z \" if (FALSE) { # \\dontrun{ # QML Approach est1 <- modsem_da(m1, oneInt, method = \"qml\") summary(est1) # Theory Of Planned Behavior tpb <- \" # Outer Model (Based on Hagger et al., 2007) ATT =~ att1 + att2 + att3 + att4 + att5 SN =~ sn1 + sn2 PBC =~ pbc1 + pbc2 + pbc3 INT =~ int1 + int2 + int3 BEH =~ b1 + b2 # Inner Model (Based on Steinmetz et al., 2011) # Covariances ATT ~~ SN + PBC PBC ~~ SN # Causal Relationsships INT ~ ATT + SN + PBC BEH ~ INT + PBC BEH ~ INT:PBC \" # lms approach estTpb <- modsem_da(tpb, data = TPB, method = lms) summary(estTpb) } # }"},{"path":"/reference/modsem_inspect.html","id":null,"dir":"Reference","previous_headings":"","what":"Inspect model information — modsem_inspect","title":"Inspect model information — modsem_inspect","text":"function used inspect fittet object. similar `lavInspect()` argument '' decides inspect","code":""},{"path":"/reference/modsem_inspect.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inspect model information — modsem_inspect","text":"","code":"modsem_inspect(object, what = NULL, ...)"},{"path":"/reference/modsem_inspect.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inspect model information — modsem_inspect","text":"object fittet model inspect inspect ... Additional arguments passed functions","code":""},{"path":"/reference/modsem_inspect.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Inspect model information — modsem_inspect","text":"`modsem_da`, `modsem_lavaan` `modsem_lavaan`, just wrapper `lavInspect()` `modsem_da` “ can either \"\", \"matrices\", \"optim\", just name extract.","code":""},{"path":"/reference/modsem_mplus.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimation latent interactions through mplus — modsem_mplus","title":"Estimation latent interactions through mplus — modsem_mplus","text":"Estimation latent interactions mplus","code":""},{"path":"/reference/modsem_mplus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimation latent interactions through mplus — modsem_mplus","text":"","code":"modsem_mplus( model.syntax, data, estimator = \"ml\", type = \"random\", algorithm = \"integration\", process = \"8\", ... )"},{"path":"/reference/modsem_mplus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimation latent interactions through mplus — modsem_mplus","text":"model.syntax lavaan/modsem syntax data dataset estimator estimator argument passed mplus type type argument passed mplus algorithm algorithm argument passed mplus process process argument passed mplus ... arguments passed functions","code":""},{"path":"/reference/modsem_mplus.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimation latent interactions through mplus — modsem_mplus","text":"modsem_mplus object","code":""},{"path":"/reference/modsem_mplus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimation latent interactions through mplus — modsem_mplus","text":"","code":"# Theory Of Planned Behavior tpb <- ' # Outer Model (Based on Hagger et al., 2007) ATT =~ att1 + att2 + att3 + att4 + att5 SN =~ sn1 + sn2 PBC =~ pbc1 + pbc2 + pbc3 INT =~ int1 + int2 + int3 BEH =~ b1 + b2 # Inner Model (Based on Steinmetz et al., 2011) # Covariances ATT ~~ SN + PBC PBC ~~ SN # Causal Relationsships INT ~ ATT + SN + PBC BEH ~ INT + PBC BEH ~ INT:PBC ' if (FALSE) { # \\dontrun{ estTpbMplus <- modsem_mplus(tpb, data = TPB) summary(estTpbLMS) } # }"},{"path":"/reference/modsem_pi.html","id":null,"dir":"Reference","previous_headings":"","what":"Interaction between latent variables using product indicators — modsem_pi","title":"Interaction between latent variables using product indicators — modsem_pi","text":"modsem_pi function estimating interaction effects latent variables, structural equation models (SEMs), using product indicators. Methods estimating interaction effects SEM's can basically split two frameworks: 1. Product Indicator based approaches (\"dblcent\", \"rca\", \"uca\", \"ca\", \"pind\"), 2. Distributionally based approaches (\"lms\", \"qml\"). modsem_pi() essentially just fancy wrapper lavaan::sem() generates necessary syntax, variables estimation models latent product indicators. use `default_settings_pi()` get default settings different methods.","code":""},{"path":"/reference/modsem_pi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interaction between latent variables using product indicators — modsem_pi","text":"","code":"modsem_pi( model.syntax = NULL, data = NULL, method = \"dblcent\", match = NULL, standardize.data = FALSE, center.data = FALSE, first.loading.fixed = TRUE, center.before = NULL, center.after = NULL, residuals.prods = NULL, residual.cov.syntax = NULL, constrained.prod.mean = NULL, constrained.loadings = NULL, constrained.var = NULL, constrained.res.cov.method = NULL, auto.scale = \"none\", auto.center = \"none\", estimator = \"ML\", group = NULL, run = TRUE, suppress.warnings.lavaan = FALSE, ... )"},{"path":"/reference/modsem_pi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interaction between latent variables using product indicators — modsem_pi","text":"model.syntax lavaan syntax data dataframe method method use: \"rca\" = residual centering approach (passed lavaan), \"uca\" = unconstrained approach (passed lavaan), \"dblcent\" = double centering approach (passed lavaan), \"pind\" = prod ind approach, constraints centering (passed lavaan), \"custom\" = use parameters specified function call (passed lavaan) match product indicators created using match-strategy standardize.data data scaled fitting model center.data data centered fitting model first.loading.fixed Sould first factorloading latent prod fixed one? center.inds prods centered computing prods (overwritten method, method != NULL) center.ind prods centered computed? residuals.prods ind prods centered using residuals (overwritten method, method != NULL) residual.cov.syntax syntax residual covariances produced (overwritten method, method != NULL) constrained.prod.mean syntax prod mean produced (overwritten method, method != NULL) constrained.loadings syntax constrained loadings produced (overwritten method, method != NULL) constrained.var syntax constrained variances produced (overwritten method, method != NULL) constrained.res.cov.method method constraining residual covariances auto.scale methods scaled automatically (usually useful) auto.center methods centered automatically (usually useful) estimator estimator use lavaan group group variable multigroup analysis run model run via lavaan, FALSE modified syntax data returned suppress.warnings.lavaan warnings lavaan supressed? ... arguments passed functions, e.g,. lavaan","code":""},{"path":"/reference/modsem_pi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interaction between latent variables using product indicators — modsem_pi","text":"modsem object","code":""},{"path":"/reference/modsem_pi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Interaction between latent variables using product indicators — modsem_pi","text":"","code":"library(modsem) # For more examples check README and/or GitHub. # One interaction m1 <- ' # Outer Model X =~ x1 + x2 +x3 Y =~ y1 + y2 + y3 Z =~ z1 + z2 + z3 # Inner model Y ~ X + Z + X:Z ' # Double centering approach est1 <- modsem_pi(m1, oneInt) summary(est1) #> modsem: #> Method = dblcent #> lavaan 0.6-18 ended normally after 159 iterations #> #> Estimator ML #> Optimization method NLMINB #> Number of model parameters 60 #> #> Number of observations 2000 #> #> Model Test User Model: #> #> Test statistic 122.924 #> Degrees of freedom 111 #> P-value (Chi-square) 0.207 #> #> Parameter Estimates: #> #> Standard errors Standard #> Information Expected #> Information saturated (h1) model Structured #> #> Latent Variables: #> Estimate Std.Err z-value P(>|z|) #> X =~ #> x1 1.000 #> x2 0.804 0.013 63.612 0.000 #> x3 0.916 0.014 67.144 0.000 #> Y =~ #> y1 1.000 #> y2 0.798 0.007 107.428 0.000 #> y3 0.899 0.008 112.453 0.000 #> Z =~ #> z1 1.000 #> z2 0.812 0.013 64.763 0.000 #> z3 0.882 0.013 67.014 0.000 #> XZ =~ #> x1z1 1.000 #> x2z1 0.805 0.013 60.636 0.000 #> x3z1 0.877 0.014 62.680 0.000 #> x1z2 0.793 0.013 59.343 0.000 #> x2z2 0.646 0.015 43.672 0.000 #> x3z2 0.706 0.016 44.292 0.000 #> x1z3 0.887 0.014 63.700 0.000 #> x2z3 0.716 0.016 45.645 0.000 #> x3z3 0.781 0.017 45.339 0.000 #> #> Regressions: #> Estimate Std.Err z-value P(>|z|) #> Y ~ #> X 0.675 0.027 25.379 0.000 #> Z 0.561 0.026 21.606 0.000 #> XZ 0.702 0.027 26.360 0.000 #> #> Covariances: #> Estimate Std.Err z-value P(>|z|) #> .x1z1 ~~ #> .x2z2 0.000 #> .x2z3 0.000 #> .x3z2 0.000 #> .x3z3 0.000 #> .x2z1 ~~ #> .x1z2 0.000 #> .x1z2 ~~ #> .x2z3 0.000 #> .x3z1 ~~ #> .x1z2 0.000 #> .x1z2 ~~ #> .x3z3 0.000 #> .x2z1 ~~ #> .x1z3 0.000 #> .x2z2 ~~ #> .x1z3 0.000 #> .x3z1 ~~ #> .x1z3 0.000 #> .x3z2 ~~ #> .x1z3 0.000 #> .x2z1 ~~ #> .x3z2 0.000 #> .x3z3 0.000 #> .x3z1 ~~ #> .x2z2 0.000 #> .x2z2 ~~ #> .x3z3 0.000 #> .x3z1 ~~ #> .x2z3 0.000 #> .x3z2 ~~ #> .x2z3 0.000 #> .x1z1 ~~ #> .x1z2 0.115 0.008 14.802 0.000 #> .x1z3 0.114 0.008 13.947 0.000 #> .x2z1 0.125 0.008 16.095 0.000 #> .x3z1 0.140 0.009 16.135 0.000 #> .x1z2 ~~ #> .x1z3 0.103 0.007 14.675 0.000 #> .x2z2 0.128 0.006 20.850 0.000 #> .x3z2 0.146 0.007 21.243 0.000 #> .x1z3 ~~ #> .x2z3 0.116 0.007 17.818 0.000 #> .x3z3 0.135 0.007 18.335 0.000 #> .x2z1 ~~ #> .x2z2 0.135 0.006 20.905 0.000 #> .x2z3 0.145 0.007 21.145 0.000 #> .x3z1 0.114 0.007 16.058 0.000 #> .x2z2 ~~ #> .x2z3 0.117 0.006 20.419 0.000 #> .x3z2 0.116 0.006 20.586 0.000 #> .x2z3 ~~ #> .x3z3 0.109 0.006 18.059 0.000 #> .x3z1 ~~ #> .x3z2 0.138 0.007 19.331 0.000 #> .x3z3 0.158 0.008 20.269 0.000 #> .x3z2 ~~ #> .x3z3 0.131 0.007 19.958 0.000 #> X ~~ #> Z 0.201 0.024 8.271 0.000 #> XZ 0.016 0.025 0.628 0.530 #> Z ~~ #> XZ 0.062 0.025 2.449 0.014 #> #> Variances: #> Estimate Std.Err z-value P(>|z|) #> .x1 0.160 0.009 17.871 0.000 #> .x2 0.162 0.007 22.969 0.000 #> .x3 0.163 0.008 20.161 0.000 #> .y1 0.159 0.009 17.896 0.000 #> .y2 0.154 0.007 22.640 0.000 #> .y3 0.164 0.008 20.698 0.000 #> .z1 0.168 0.009 18.143 0.000 #> .z2 0.158 0.007 22.264 0.000 #> .z3 0.158 0.008 20.389 0.000 #> .x1z1 0.311 0.014 22.227 0.000 #> .x2z1 0.292 0.011 27.287 0.000 #> .x3z1 0.327 0.012 26.275 0.000 #> .x1z2 0.290 0.011 26.910 0.000 #> .x2z2 0.239 0.008 29.770 0.000 #> .x3z2 0.270 0.009 29.117 0.000 #> .x1z3 0.272 0.012 23.586 0.000 #> .x2z3 0.245 0.009 27.979 0.000 #> .x3z3 0.297 0.011 28.154 0.000 #> X 0.981 0.036 26.895 0.000 #> .Y 0.990 0.038 25.926 0.000 #> Z 1.016 0.038 26.856 0.000 #> XZ 1.045 0.044 24.004 0.000 #> if (FALSE) { # \\dontrun{ # The Constrained Approach est1Constrained <- modsem_pi(m1, oneInt, method = \"ca\") summary(est1Constrained) } # } # Theory Of Planned Behavior tpb <- ' # Outer Model (Based on Hagger et al., 2007) ATT =~ att1 + att2 + att3 + att4 + att5 SN =~ sn1 + sn2 PBC =~ pbc1 + pbc2 + pbc3 INT =~ int1 + int2 + int3 BEH =~ b1 + b2 # Inner Model (Based on Steinmetz et al., 2011) # Covariances ATT ~~ SN + PBC PBC ~~ SN # Causal Relationsships INT ~ ATT + SN + PBC BEH ~ INT + PBC BEH ~ INT:PBC ' # double centering approach estTpb <- modsem_pi(tpb, data = TPB) summary(estTpb) #> modsem: #> Method = dblcent #> lavaan 0.6-18 ended normally after 169 iterations #> #> Estimator ML #> Optimization method NLMINB #> Number of model parameters 78 #> #> Number of observations 2000 #> #> Model Test User Model: #> #> Test statistic 207.615 #> Degrees of freedom 222 #> P-value (Chi-square) 0.747 #> #> Parameter Estimates: #> #> Standard errors Standard #> Information Expected #> Information saturated (h1) model Structured #> #> Latent Variables: #> Estimate Std.Err z-value P(>|z|) #> ATT =~ #> att1 1.000 #> att2 0.878 0.012 71.509 0.000 #> att3 0.789 0.012 66.368 0.000 #> att4 0.695 0.011 61.017 0.000 #> att5 0.887 0.013 70.884 0.000 #> SN =~ #> sn1 1.000 #> sn2 0.889 0.017 52.553 0.000 #> PBC =~ #> pbc1 1.000 #> pbc2 0.912 0.013 69.500 0.000 #> pbc3 0.801 0.012 65.830 0.000 #> INT =~ #> int1 1.000 #> int2 0.914 0.016 58.982 0.000 #> int3 0.808 0.015 55.547 0.000 #> BEH =~ #> b1 1.000 #> b2 0.960 0.030 31.561 0.000 #> INTPBC =~ #> int1pbc1 1.000 #> int2pbc1 0.931 0.015 63.809 0.000 #> int3pbc1 0.774 0.013 60.107 0.000 #> int1pbc2 0.893 0.013 68.172 0.000 #> int2pbc2 0.826 0.017 48.845 0.000 #> int3pbc2 0.690 0.015 45.300 0.000 #> int1pbc3 0.799 0.012 67.008 0.000 #> int2pbc3 0.738 0.015 47.809 0.000 #> int3pbc3 0.622 0.014 45.465 0.000 #> #> Regressions: #> Estimate Std.Err z-value P(>|z|) #> INT ~ #> ATT 0.213 0.026 8.170 0.000 #> SN 0.177 0.028 6.416 0.000 #> PBC 0.217 0.030 7.340 0.000 #> BEH ~ #> INT 0.191 0.024 7.817 0.000 #> PBC 0.230 0.022 10.507 0.000 #> INTPBC 0.204 0.018 11.425 0.000 #> #> Covariances: #> Estimate Std.Err z-value P(>|z|) #> ATT ~~ #> SN 0.629 0.029 21.977 0.000 #> PBC 0.678 0.029 23.721 0.000 #> SN ~~ #> PBC 0.678 0.029 23.338 0.000 #> .int1pbc1 ~~ #> .int2pbc2 0.000 #> .int2pbc3 0.000 #> .int3pbc2 0.000 #> .int3pbc3 0.000 #> .int2pbc1 ~~ #> .int1pbc2 0.000 #> .int1pbc2 ~~ #> .int2pbc3 0.000 #> .int3pbc1 ~~ #> .int1pbc2 0.000 #> .int1pbc2 ~~ #> .int3pbc3 0.000 #> .int2pbc1 ~~ #> .int1pbc3 0.000 #> .int2pbc2 ~~ #> .int1pbc3 0.000 #> .int3pbc1 ~~ #> .int1pbc3 0.000 #> .int3pbc2 ~~ #> .int1pbc3 0.000 #> .int2pbc1 ~~ #> .int3pbc2 0.000 #> .int3pbc3 0.000 #> .int3pbc1 ~~ #> .int2pbc2 0.000 #> .int2pbc2 ~~ #> .int3pbc3 0.000 #> .int3pbc1 ~~ #> .int2pbc3 0.000 #> .int3pbc2 ~~ #> .int2pbc3 0.000 #> .int1pbc1 ~~ #> .int1pbc2 0.126 0.009 14.768 0.000 #> .int1pbc3 0.102 0.007 13.794 0.000 #> .int2pbc1 0.104 0.007 14.608 0.000 #> .int3pbc1 0.091 0.006 14.109 0.000 #> .int1pbc2 ~~ #> .int1pbc3 0.095 0.007 13.852 0.000 #> .int2pbc2 0.128 0.007 19.320 0.000 #> .int3pbc2 0.119 0.006 19.402 0.000 #> .int1pbc3 ~~ #> .int2pbc3 0.110 0.006 19.911 0.000 #> .int3pbc3 0.097 0.005 19.415 0.000 #> .int2pbc1 ~~ #> .int2pbc2 0.152 0.008 18.665 0.000 #> .int2pbc3 0.138 0.007 18.779 0.000 #> .int3pbc1 0.082 0.006 13.951 0.000 #> .int2pbc2 ~~ #> .int2pbc3 0.121 0.007 18.361 0.000 #> .int3pbc2 0.104 0.005 19.047 0.000 #> .int2pbc3 ~~ #> .int3pbc3 0.087 0.005 19.180 0.000 #> .int3pbc1 ~~ #> .int3pbc2 0.139 0.007 21.210 0.000 #> .int3pbc3 0.123 0.006 21.059 0.000 #> .int3pbc2 ~~ #> .int3pbc3 0.114 0.005 21.021 0.000 #> ATT ~~ #> INTPBC 0.086 0.024 3.519 0.000 #> SN ~~ #> INTPBC 0.055 0.025 2.230 0.026 #> PBC ~~ #> INTPBC 0.087 0.024 3.609 0.000 #> #> Variances: #> Estimate Std.Err z-value P(>|z|) #> .att1 0.167 0.007 23.528 0.000 #> .att2 0.150 0.006 24.693 0.000 #> .att3 0.160 0.006 26.378 0.000 #> .att4 0.163 0.006 27.649 0.000 #> .att5 0.159 0.006 24.930 0.000 #> .sn1 0.178 0.015 12.110 0.000 #> .sn2 0.156 0.012 13.221 0.000 #> .pbc1 0.145 0.008 18.440 0.000 #> .pbc2 0.160 0.007 21.547 0.000 #> .pbc3 0.154 0.007 23.716 0.000 #> .int1 0.158 0.009 18.152 0.000 #> .int2 0.160 0.008 20.345 0.000 #> .int3 0.167 0.007 23.414 0.000 #> .b1 0.186 0.018 10.058 0.000 #> .b2 0.135 0.017 8.080 0.000 #> .int1pbc1 0.266 0.013 20.971 0.000 #> .int2pbc1 0.292 0.012 24.421 0.000 #> .int3pbc1 0.251 0.010 26.305 0.000 #> .int1pbc2 0.290 0.012 24.929 0.000 #> .int2pbc2 0.269 0.010 26.701 0.000 #> .int3pbc2 0.253 0.009 29.445 0.000 #> .int1pbc3 0.223 0.009 24.431 0.000 #> .int2pbc3 0.234 0.008 27.633 0.000 #> .int3pbc3 0.203 0.007 29.288 0.000 #> ATT 0.998 0.037 27.138 0.000 #> SN 0.987 0.039 25.394 0.000 #> PBC 0.962 0.035 27.260 0.000 #> .INT 0.490 0.020 24.638 0.000 #> .BEH 0.455 0.023 20.068 0.000 #> INTPBC 1.020 0.041 24.612 0.000 #> if (FALSE) { # \\dontrun{ # The Constrained Approach estTpbConstrained <- modsem_pi(tpb, data = TPB, method = \"ca\") summary(estTpbConstrained) } # }"},{"path":"/reference/modsemify.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate parameter table for lavaan syntax — modsemify","title":"Generate parameter table for lavaan syntax — modsemify","text":"Generate parameter table lavaan syntax","code":""},{"path":"/reference/modsemify.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate parameter table for lavaan syntax — modsemify","text":"","code":"modsemify(syntax)"},{"path":"/reference/modsemify.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate parameter table for lavaan syntax — modsemify","text":"syntax model syntax","code":""},{"path":"/reference/modsemify.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate parameter table for lavaan syntax — modsemify","text":"data.frame columns lhs, op, rhs, mod","code":""},{"path":"/reference/modsemify.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate parameter table for lavaan syntax — modsemify","text":"","code":"library(modsem) m1 <- ' # Outer Model X =~ x1 + x2 +x3 Y =~ y1 + y2 + y3 Z =~ z1 + z2 + z3 # Inner model Y ~ X + Z + X:Z ' modsemify(m1) #> lhs op rhs mod #> 1 X =~ x1 #> 2 X =~ x2 #> 3 X =~ x3 #> 4 Y =~ y1 #> 5 Y =~ y2 #> 6 Y =~ y3 #> 7 Z =~ z1 #> 8 Z =~ z2 #> 9 Z =~ z3 #> 10 Y ~ X #> 11 Y ~ Z #> 12 Y ~ X:Z"},{"path":"/reference/multiplyIndicatorsCpp.html","id":null,"dir":"Reference","previous_headings":"","what":"Multiply indicators — multiplyIndicatorsCpp","title":"Multiply indicators — multiplyIndicatorsCpp","text":"Multiply indicators","code":""},{"path":"/reference/multiplyIndicatorsCpp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multiply indicators — multiplyIndicatorsCpp","text":"","code":"multiplyIndicatorsCpp(df)"},{"path":"/reference/multiplyIndicatorsCpp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multiply indicators — multiplyIndicatorsCpp","text":"df data DataFrame","code":""},{"path":"/reference/multiplyIndicatorsCpp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multiply indicators — multiplyIndicatorsCpp","text":"NumericVector","code":""},{"path":"/reference/oneInt.html","id":null,"dir":"Reference","previous_headings":"","what":"oneInt — oneInt","title":"oneInt — oneInt","text":"simulated dataset one interaction effect","code":""},{"path":"/reference/parameter_estimates.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract parameterEstimates from an estimated model — parameter_estimates","title":"Extract parameterEstimates from an estimated model — parameter_estimates","text":"Extract parameterEstimates estimated model","code":""},{"path":"/reference/parameter_estimates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract parameterEstimates from an estimated model — parameter_estimates","text":"","code":"parameter_estimates(object, ...)"},{"path":"/reference/parameter_estimates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract parameterEstimates from an estimated model — parameter_estimates","text":"object object class `modsem_pi`, `modsem_da`, `modsem_mplus` ... Additional arguments passed functions","code":""},{"path":"/reference/plot_interaction.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Interaction Effects — plot_interaction","title":"Plot Interaction Effects — plot_interaction","text":"Plot Interaction Effects","code":""},{"path":"/reference/plot_interaction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Interaction Effects — plot_interaction","text":"","code":"plot_interaction( x, z, y, xz = NULL, vals_x = seq(-3, 3, 0.001), vals_z, model, alpha_se = 0.15, ... )"},{"path":"/reference/plot_interaction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Interaction Effects — plot_interaction","text":"x name variable x-axis z name moderator variable y name outcome variable xz name interaction term. interaction term specified, created using `x` `z`. vals_x values x variable plot, values smoother std.error-area vals_z values moderator variable plot. seperate regression line (\"y ~ x | z\") plotted value moderator variable model object class `modsem_pi`, `modsem_da`, `modsem_mplus` alpha_se alpha level std.error area ... Additional arguments passed functions","code":""},{"path":"/reference/plot_interaction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Interaction Effects — plot_interaction","text":"ggplot object","code":""},{"path":"/reference/plot_interaction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Interaction Effects — plot_interaction","text":"","code":"library(modsem) if (FALSE) { # \\dontrun{ m1 <- \" # Outer Model X =~ x1 X =~ x2 + x3 Z =~ z1 + z2 + z3 Y =~ y1 + y2 + y3 # Inner model Y ~ X + Z + X:Z \" est1 <- modsem(m1, data = oneInt) plot_interaction(\"X\", \"Z\", \"Y\", \"X:Z\", -3:3, c(-0.2, 0), est1) tpb <- \" # Outer Model (Based on Hagger et al., 2007) ATT =~ att1 + att2 + att3 + att4 + att5 SN =~ sn1 + sn2 PBC =~ pbc1 + pbc2 + pbc3 INT =~ int1 + int2 + int3 BEH =~ b1 + b2 # Inner Model (Based on Steinmetz et al., 2011) # Causal Relationsships INT ~ ATT + SN + PBC BEH ~ INT + PBC # BEH ~ ATT:PBC BEH ~ PBC:INT # BEH ~ PBC:PBC \" est2 <- modsem(tpb, TPB, method = \"lms\") plot_interaction(x = \"INT\", z = \"PBC\", y = \"BEH\", xz = \"PBC:INT\", vals_z = c(-0.5, 0.5), model = est2) } # }"},{"path":"/reference/standardized_estimates.html","id":null,"dir":"Reference","previous_headings":"","what":"Get standardized estimates — standardized_estimates","title":"Get standardized estimates — standardized_estimates","text":"Get standardized estimates","code":""},{"path":"/reference/standardized_estimates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get standardized estimates — standardized_estimates","text":"","code":"standardized_estimates(object, ...)"},{"path":"/reference/standardized_estimates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get standardized estimates — standardized_estimates","text":"object object class `modsem_da`, `modsem_mplus`, parTable class `data.frame` ... Additional arguments passed functions","code":""},{"path":"/reference/standardized_estimates.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get standardized estimates — standardized_estimates","text":"`modsem_da`, `modsem_mplus` objects, interaction term standardized var(xz) = 1. interaction term actual variable model, meaning variance. must therefore calculated parameters model. Assuming normality zero-means variance calculated `var(xz) = var(x) * var(z) + cov(x, z)^2`. Thus setting variance interaction term 1, 'correct' correlation x z zero. means standardized estimates interaction term different using lavaan, since interaction term actual latent variable model, standardized variance 1.","code":""},{"path":"/reference/summary.html","id":null,"dir":"Reference","previous_headings":"","what":"summary for modsem objects — summary.modsem_da","title":"summary for modsem objects — summary.modsem_da","text":"summary modsem objects summary modsem objects summary modsem objects","code":""},{"path":"/reference/summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"summary for modsem objects — summary.modsem_da","text":"","code":"# S3 method for class 'modsem_da' summary( object, H0 = TRUE, verbose = TRUE, r.squared = TRUE, adjusted.stat = FALSE, digits = 3, scientific = FALSE, ci = FALSE, standardized = FALSE, loadings = TRUE, regressions = TRUE, covariances = TRUE, intercepts = TRUE, variances = TRUE, var.interaction = FALSE, ... ) # S3 method for class 'modsem_mplus' summary( object, scientific = FALSE, standardize = FALSE, ci = FALSE, digits = 3, loadings = TRUE, regressions = TRUE, covariances = TRUE, intercepts = TRUE, variances = TRUE, ... ) # S3 method for class 'modsem_pi' summary(object, ...)"},{"path":"/reference/summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"summary for modsem objects — summary.modsem_da","text":"object modsem object summarized H0 null model estimated (used comparison) verbose print progress estimation null model r.squared calculate R-squared adjusted.stat sample size corrected/adjustes AIC BIC reported? digits number digits print scientific print p-values scientific notation ci print confidence intervals standardized print standardized estimates loadings print loadings regressions print regressions covariances print covariances intercepts print intercepts variances print variances var.interaction FALSE (default) variances interaction terms removed (present) ... arguments passed lavaan::summary() standardize standardize estimates","code":""},{"path":"/reference/summary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"summary for modsem objects — summary.modsem_da","text":"","code":"if (FALSE) { # \\dontrun{ m1 <- \" # Outer Model X =~ x1 + x2 + x3 Y =~ y1 + y2 + y3 Z =~ z1 + z2 + z3 # Inner model Y ~ X + Z + X:Z \" est1 <- modsem(m1, oneInt, \"qml\") summary(est1, ci = TRUE, scientific = TRUE) } # }"},{"path":"/reference/trace_path.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate formulas for (co-)variance paths using Wright's path tracing rules — trace_path","title":"Estimate formulas for (co-)variance paths using Wright's path tracing rules — trace_path","text":"function estimates path x y using path tracing rules, note works structural parameters, \"=~\" ignored. unless measurement.model = TRUE. want use measurement model, \"~\" mod column pt.","code":""},{"path":"/reference/trace_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate formulas for (co-)variance paths using Wright's path tracing rules — trace_path","text":"","code":"trace_path( pt, x, y, parenthesis = TRUE, missing.cov = FALSE, measurement.model = FALSE, maxlen = 100, ... )"},{"path":"/reference/trace_path.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate formulas for (co-)variance paths using Wright's path tracing rules — trace_path","text":"pt data frame columns lhs, op, rhs, mod, modsemify(syntax) x source variable y destination variable parenthesis TRUE, output enclosed parenthesis missing.cov TRUE covariances missing model syntax added measurement.model TRUE, function use measurement model maxlen maximum length path aborting ... additional arguments passed trace_path","code":""},{"path":"/reference/trace_path.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate formulas for (co-)variance paths using Wright's path tracing rules — trace_path","text":"string estimated path (simplified possible)","code":""},{"path":"/reference/trace_path.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate formulas for (co-)variance paths using Wright's path tracing rules — trace_path","text":"","code":"library(modsem) m1 <- ' # Outer Model X =~ x1 + x2 +x3 Y =~ y1 + y2 + y3 Z =~ z1 + z2 + z3 # Inner model Y ~ X + Z + X:Z ' pt <- modsemify(m1) trace_path(pt, x = \"Y\", y = \"Y\", missing.cov = TRUE) # variance of Y #> [1] \"(X~~X * Y~X ^ 2 + 2 * X~~Z * Y~X * Y~Z + Y~Z ^ 2 * Z~~Z + Y~~Y)\""},{"path":"/reference/var_interactions.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or modify parTable from an estimated model with estimated variances of interaction terms — var_interactions","title":"Extract or modify parTable from an estimated model with estimated variances of interaction terms — var_interactions","text":"Extract modify parTable estimated model estimated variances interaction terms","code":""},{"path":"/reference/var_interactions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or modify parTable from an estimated model with estimated variances of interaction terms — var_interactions","text":"","code":"var_interactions(object, ...)"},{"path":"/reference/var_interactions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or modify parTable from an estimated model with estimated variances of interaction terms — var_interactions","text":"object object class `modsem_da`, `modsem_mplus`, parTable class `data.frame` ... Additional arguments passed functions","code":""},{"path":"/reference/vcov_modsem_da.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper for vcov — vcov_modsem_da","title":"Wrapper for vcov — vcov_modsem_da","text":"wrapper vcov, used modsem::vcov_modsem_da, since vcov namespace modsem, stats","code":""},{"path":"/reference/vcov_modsem_da.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper for vcov — vcov_modsem_da","text":"","code":"vcov_modsem_da(object, ...)"},{"path":"/reference/vcov_modsem_da.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper for vcov — vcov_modsem_da","text":"object fittet model inspect ... additional arguments","code":""}] diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 0000000..9d1a696 --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,46 @@ + +/404.html +/LICENSE-text.html +/LICENSE.html +/articles/customizing.html +/articles/index.html +/articles/interaction_two_etas.html +/articles/lavaan.html +/articles/lms_qml.html +/articles/methods.html +/articles/modsem.html +/articles/observed_lms_qml.html +/articles/plot_interactions.html +/articles/quadratic.html +/authors.html +/index.html +/reference/TPB.html +/reference/TPB_UK.html +/reference/coef_modsem_da.html +/reference/compare_fit.html +/reference/default_settings_da.html +/reference/default_settings_pi.html +/reference/extract_lavaan.html +/reference/fit_modsem_da.html +/reference/get_pi_data.html +/reference/get_pi_syntax.html +/reference/index.html +/reference/jordan.html +/reference/modsem-package.html +/reference/modsem.html +/reference/modsem_da.html +/reference/modsem_inspect.html +/reference/modsem_mplus.html +/reference/modsem_pi.html +/reference/modsemify.html +/reference/multiplyIndicatorsCpp.html +/reference/oneInt.html +/reference/parameter_estimates.html +/reference/plot_interaction.html +/reference/standardized_estimates.html +/reference/summary.html +/reference/trace_path.html +/reference/var_interactions.html +/reference/vcov_modsem_da.html + + diff --git a/src/LMS.h b/src/LMS.h deleted file mode 100644 index bbe0ca4..0000000 --- a/src/LMS.h +++ /dev/null @@ -1,10 +0,0 @@ -#ifndef LMS_H -#define LMS_H - - -arma::vec muLmsCpp(Rcpp::List model, arma::vec z); -arma::mat sigmaLmsCpp(Rcpp::List model, arma::vec z); -arma::mat zToMatrix(arma::vec z, int numEtas); - - -#endif diff --git a/src/Makevars b/src/Makevars deleted file mode 100644 index 22c7566..0000000 --- a/src/Makevars +++ /dev/null @@ -1,19 +0,0 @@ - -## With R 3.1.0 or later, you can uncomment the following line to tell R to -## enable compilation with C++11 (where available) -## -## Also, OpenMP support in Armadillo prefers C++11 support. However, for wider -## availability of the package we do not yet enforce this here. It is however -## recommended for client packages to set it. -## -## And with R 3.4.0, and RcppArmadillo 0.7.960.*, we turn C++11 on as OpenMP -## support within Armadillo prefers / requires it -## -## R 4.0.0 made C++11 the default, R 4.1.0 switched to C++14, R 4.3.0 to C++17 -## _In general_ we should no longer need to set a standard as any recent R -## installation will do the right thing. Should you need it, uncomment it and -## set the appropriate value, possibly CXX17. -#CXX_STD = CXX11 - -PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS) -PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) diff --git a/src/Makevars.win b/src/Makevars.win deleted file mode 100644 index 22c7566..0000000 --- a/src/Makevars.win +++ /dev/null @@ -1,19 +0,0 @@ - -## With R 3.1.0 or later, you can uncomment the following line to tell R to -## enable compilation with C++11 (where available) -## -## Also, OpenMP support in Armadillo prefers C++11 support. However, for wider -## availability of the package we do not yet enforce this here. It is however -## recommended for client packages to set it. -## -## And with R 3.4.0, and RcppArmadillo 0.7.960.*, we turn C++11 on as OpenMP -## support within Armadillo prefers / requires it -## -## R 4.0.0 made C++11 the default, R 4.1.0 switched to C++14, R 4.3.0 to C++17 -## _In general_ we should no longer need to set a standard as any recent R -## installation will do the right thing. Should you need it, uncomment it and -## set the appropriate value, possibly CXX17. -#CXX_STD = CXX11 - -PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS) -PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) diff --git a/src/QML.h b/src/QML.h deleted file mode 100644 index d25ce43..0000000 --- a/src/QML.h +++ /dev/null @@ -1,12 +0,0 @@ -#ifndef QML_H -#define QML_H - - -arma::mat muQmlCpp(Rcpp::List m, int t); -arma::mat sigmaQmlCpp(Rcpp::List m, int t); -arma::mat varZCpp(arma::mat Omega, arma::mat Sigma1, int numEta); -double varZSubOmega(arma::mat Omega, arma::mat Sigma1); -arma::vec traceOmegaSigma1(const arma::mat OmegaSigma1, const int numEta); - - -#endif diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp deleted file mode 100644 index 7d4ac15..0000000 --- a/src/RcppExports.cpp +++ /dev/null @@ -1,170 +0,0 @@ -// Generated by using Rcpp::compileAttributes() -> do not edit by hand -// Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 - -#include -#include - -using namespace Rcpp; - -#ifdef RCPP_USE_GLOBAL_ROSTREAM -Rcpp::Rostream& Rcpp::Rcout = Rcpp::Rcpp_cout_get(); -Rcpp::Rostream& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get(); -#endif - -// muLmsCpp -arma::vec muLmsCpp(Rcpp::List model, arma::vec z); -RcppExport SEXP _modsem_muLmsCpp(SEXP modelSEXP, SEXP zSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< Rcpp::List >::type model(modelSEXP); - Rcpp::traits::input_parameter< arma::vec >::type z(zSEXP); - rcpp_result_gen = Rcpp::wrap(muLmsCpp(model, z)); - return rcpp_result_gen; -END_RCPP -} -// sigmaLmsCpp -arma::mat sigmaLmsCpp(Rcpp::List model, arma::vec z); -RcppExport SEXP _modsem_sigmaLmsCpp(SEXP modelSEXP, SEXP zSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< Rcpp::List >::type model(modelSEXP); - Rcpp::traits::input_parameter< arma::vec >::type z(zSEXP); - rcpp_result_gen = Rcpp::wrap(sigmaLmsCpp(model, z)); - return rcpp_result_gen; -END_RCPP -} -// muQmlCpp -arma::mat muQmlCpp(Rcpp::List m, int t); -RcppExport SEXP _modsem_muQmlCpp(SEXP mSEXP, SEXP tSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< Rcpp::List >::type m(mSEXP); - Rcpp::traits::input_parameter< int >::type t(tSEXP); - rcpp_result_gen = Rcpp::wrap(muQmlCpp(m, t)); - return rcpp_result_gen; -END_RCPP -} -// sigmaQmlCpp -arma::mat sigmaQmlCpp(Rcpp::List m, int t); -RcppExport SEXP _modsem_sigmaQmlCpp(SEXP mSEXP, SEXP tSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< Rcpp::List >::type m(mSEXP); - Rcpp::traits::input_parameter< int >::type t(tSEXP); - rcpp_result_gen = Rcpp::wrap(sigmaQmlCpp(m, t)); - return rcpp_result_gen; -END_RCPP -} -// calcKronXi -arma::mat calcKronXi(Rcpp::List m, int t); -RcppExport SEXP _modsem_calcKronXi(SEXP mSEXP, SEXP tSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< Rcpp::List >::type m(mSEXP); - Rcpp::traits::input_parameter< int >::type t(tSEXP); - rcpp_result_gen = Rcpp::wrap(calcKronXi(m, t)); - return rcpp_result_gen; -END_RCPP -} -// calcBinvCpp -arma::mat calcBinvCpp(Rcpp::List m, int t); -RcppExport SEXP _modsem_calcBinvCpp(SEXP mSEXP, SEXP tSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< Rcpp::List >::type m(mSEXP); - Rcpp::traits::input_parameter< int >::type t(tSEXP); - rcpp_result_gen = Rcpp::wrap(calcBinvCpp(m, t)); - return rcpp_result_gen; -END_RCPP -} -// dnormCpp -arma::vec dnormCpp(const arma::vec& x, const arma::vec& mu, const arma::vec& sigma); -RcppExport SEXP _modsem_dnormCpp(SEXP xSEXP, SEXP muSEXP, SEXP sigmaSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::vec& >::type x(xSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type mu(muSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type sigma(sigmaSEXP); - rcpp_result_gen = Rcpp::wrap(dnormCpp(x, mu, sigma)); - return rcpp_result_gen; -END_RCPP -} -// varZCpp -arma::mat varZCpp(arma::mat Omega, arma::mat Sigma1, int numEta); -RcppExport SEXP _modsem_varZCpp(SEXP OmegaSEXP, SEXP Sigma1SEXP, SEXP numEtaSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< arma::mat >::type Omega(OmegaSEXP); - Rcpp::traits::input_parameter< arma::mat >::type Sigma1(Sigma1SEXP); - Rcpp::traits::input_parameter< int >::type numEta(numEtaSEXP); - rcpp_result_gen = Rcpp::wrap(varZCpp(Omega, Sigma1, numEta)); - return rcpp_result_gen; -END_RCPP -} -// multiplyIndicatorsCpp -NumericVector multiplyIndicatorsCpp(DataFrame df); -RcppExport SEXP _modsem_multiplyIndicatorsCpp(SEXP dfSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< DataFrame >::type df(dfSEXP); - rcpp_result_gen = Rcpp::wrap(multiplyIndicatorsCpp(df)); - return rcpp_result_gen; -END_RCPP -} -// rep_dmvnorm -arma::vec rep_dmvnorm(const arma::mat& x, const arma::mat& expected, const arma::mat& sigma, const int t); -RcppExport SEXP _modsem_rep_dmvnorm(SEXP xSEXP, SEXP expectedSEXP, SEXP sigmaSEXP, SEXP tSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type x(xSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type expected(expectedSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type sigma(sigmaSEXP); - Rcpp::traits::input_parameter< const int >::type t(tSEXP); - rcpp_result_gen = Rcpp::wrap(rep_dmvnorm(x, expected, sigma, t)); - return rcpp_result_gen; -END_RCPP -} -// dmvnrm_arma_mc -arma::vec dmvnrm_arma_mc(arma::mat const& x, arma::rowvec const& mean, arma::mat const& sigma, bool const logd); -RcppExport SEXP _modsem_dmvnrm_arma_mc(SEXP xSEXP, SEXP meanSEXP, SEXP sigmaSEXP, SEXP logdSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< arma::mat const& >::type x(xSEXP); - Rcpp::traits::input_parameter< arma::rowvec const& >::type mean(meanSEXP); - Rcpp::traits::input_parameter< arma::mat const& >::type sigma(sigmaSEXP); - Rcpp::traits::input_parameter< bool const >::type logd(logdSEXP); - rcpp_result_gen = Rcpp::wrap(dmvnrm_arma_mc(x, mean, sigma, logd)); - return rcpp_result_gen; -END_RCPP -} - -static const R_CallMethodDef CallEntries[] = { - {"_modsem_muLmsCpp", (DL_FUNC) &_modsem_muLmsCpp, 2}, - {"_modsem_sigmaLmsCpp", (DL_FUNC) &_modsem_sigmaLmsCpp, 2}, - {"_modsem_muQmlCpp", (DL_FUNC) &_modsem_muQmlCpp, 2}, - {"_modsem_sigmaQmlCpp", (DL_FUNC) &_modsem_sigmaQmlCpp, 2}, - {"_modsem_calcKronXi", (DL_FUNC) &_modsem_calcKronXi, 2}, - {"_modsem_calcBinvCpp", (DL_FUNC) &_modsem_calcBinvCpp, 2}, - {"_modsem_dnormCpp", (DL_FUNC) &_modsem_dnormCpp, 3}, - {"_modsem_varZCpp", (DL_FUNC) &_modsem_varZCpp, 3}, - {"_modsem_multiplyIndicatorsCpp", (DL_FUNC) &_modsem_multiplyIndicatorsCpp, 1}, - {"_modsem_rep_dmvnorm", (DL_FUNC) &_modsem_rep_dmvnorm, 4}, - {"_modsem_dmvnrm_arma_mc", (DL_FUNC) &_modsem_dmvnrm_arma_mc, 4}, - {NULL, NULL, 0} -}; - -RcppExport void R_init_modsem(DllInfo *dll) { - R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); - R_useDynamicSymbols(dll, FALSE); -} diff --git a/src/equationsLMS.cpp b/src/equationsLMS.cpp deleted file mode 100644 index 3a66b5b..0000000 --- a/src/equationsLMS.cpp +++ /dev/null @@ -1,89 +0,0 @@ -#include -#include "LMS.h" -// [[Rcpp::depends(RcppArmadillo)]] - - -// [[Rcpp::export]] -arma::vec muLmsCpp(Rcpp::List model, arma::vec z) { - Rcpp::List matrices = model["matrices"]; - Rcpp::List info = model["info"]; - Rcpp::List quad = model["quad"]; - int numXis = Rcpp::as(info["numXis"]); - int k = Rcpp::as(quad["k"]); - arma::mat A = Rcpp::as(matrices["A"]); - arma::mat Oxx = Rcpp::as(matrices["omegaXiXi"]); - arma::mat Oex = Rcpp::as(matrices["omegaEtaXi"]); - arma::mat Ie = Rcpp::as(matrices["Ieta"]); - arma::mat lY = Rcpp::as(matrices["lambdaY"]); - arma::mat lX = Rcpp::as(matrices["lambdaX"]); - arma::mat tY = Rcpp::as(matrices["tauY"]); - arma::mat tX = Rcpp::as(matrices["tauX"]); - arma::mat Gx = Rcpp::as(matrices["gammaXi"]); - arma::mat Ge = Rcpp::as(matrices["gammaEta"]); - arma::mat a = Rcpp::as(matrices["alpha"]); - arma::mat beta0 = Rcpp::as(matrices["beta0"]); - - arma::vec zVec; - if (k > 0) zVec = arma::join_cols(z, arma::zeros(numXis - k)); - else zVec = arma::zeros(numXis); - arma::mat kronZ = arma::kron(Ie, beta0 + A * zVec); - - arma::mat Binv; - if (Ie.n_cols == 1) { - Binv = arma::mat(Ie); - } else { - Binv = arma::inv(Ie - Ge - kronZ.t() * Oex); - } - arma::vec muX = tX + lX * (beta0 + A * zVec); - arma::vec muY = tY + - lY * (Binv * (a + - Gx * (beta0 + A * zVec) + - kronZ.t() * Oxx * (beta0 + A * zVec))); - return arma::join_cols(muX, muY); -} - - -// [[Rcpp::export]] -arma::mat sigmaLmsCpp(Rcpp::List model, arma::vec z) { - Rcpp::List matrices = model["matrices"]; - Rcpp::List info = model["info"]; - Rcpp::List quad = model["quad"]; - int numXis = Rcpp::as(info["numXis"]); - int k = Rcpp::as(quad["k"]); - arma::mat A = Rcpp::as(matrices["A"]); - arma::mat Oxx = Rcpp::as(matrices["omegaXiXi"]); - arma::mat Oex = Rcpp::as(matrices["omegaEtaXi"]); - arma::mat Ie = Rcpp::as(matrices["Ieta"]); - arma::mat lY = Rcpp::as(matrices["lambdaY"]); - arma::mat lX = Rcpp::as(matrices["lambdaX"]); - arma::mat tY = Rcpp::as(matrices["tauY"]); - arma::mat tX = Rcpp::as(matrices["tauX"]); - arma::mat Gx = Rcpp::as(matrices["gammaXi"]); - arma::mat Ge = Rcpp::as(matrices["gammaEta"]); - arma::mat a = Rcpp::as(matrices["alpha"]); - arma::mat beta0 = Rcpp::as(matrices["beta0"]); - arma::mat Psi = Rcpp::as(matrices["psi"]); - arma::mat d = Rcpp::as(matrices["thetaDelta"]); - arma::mat e = Rcpp::as(matrices["thetaEpsilon"]); - - arma::vec zVec; - if (k > 0) zVec = arma::join_cols(z, arma::zeros(numXis - k)); - else zVec = arma::zeros(numXis); - arma::mat kronZ = arma::kron(Ie, beta0 + A * zVec); - - arma::mat Binv; - if (Ie.n_cols == 1) { - Binv = arma::mat(Ie); - } else { - Binv = arma::inv(Ie - Ge - kronZ.t() * Oex); - } - - arma::mat Oi = arma::eye(numXis, numXis); - Oi.diag() = arma::join_cols(arma::zeros(k), arma::ones(numXis - k)); - arma::mat Sxx = lX * A * Oi * A.t() * lX.t() + d; - arma::mat Eta = Binv * (Gx * A + kronZ.t() * Oxx * A); - arma::mat Sxy = lX * (A * Oi * Eta.t()) * lY.t(); - arma::mat Syy = lY * Eta * Oi * Eta.t() * lY.t() + - lY * (Binv * Psi * Binv.t()) * lY.t() + e; - return arma::join_cols(arma::join_rows(Sxx, Sxy), arma::join_rows(Sxy.t(), Syy)); -} diff --git a/src/equationsQML.cpp b/src/equationsQML.cpp deleted file mode 100644 index d0027b4..0000000 --- a/src/equationsQML.cpp +++ /dev/null @@ -1,244 +0,0 @@ -#include -#include "QML.h" -// [[Rcpp::depends(RcppArmadillo)]] - - -// [[Rcpp::export]] -arma::mat muQmlCpp(Rcpp::List m, int t) { - int numEta = Rcpp::as(m["numEta"]); - int numXi = Rcpp::as(m["numXi"]); - arma::mat alpha = Rcpp::as(m["alpha"]); - arma::mat beta0 = Rcpp::as(m["beta0"]); - arma::mat gammaXi = Rcpp::as(m["gammaXi"]); - arma::mat omegaXiXi = Rcpp::as(m["omegaXiXi"]); - arma::mat l1 = Rcpp::as(m["L1"]); // L1 refers to L1 cache - arma::mat l2 = Rcpp::as(m["L2"]); // L2 refers to L2 cache - arma::mat x = Rcpp::as(m["x"]); - arma::mat u = Rcpp::as(m["u"]); - arma::mat Ey = arma::mat(t, numEta); - arma::mat Sigma1 = Rcpp::as(m["Sigma1"]); - arma::mat Ie = Rcpp::as(m["Ieta"]); - arma::mat Binv = Rcpp::as(m["Binv"]); - arma::vec trOmegaSigma = traceOmegaSigma1(omegaXiXi * Sigma1, numEta); - arma::mat kronXi = Rcpp::as(m["kronXi"]); - - arma::mat kronXi_t; - - int firstRow, lastRow, firstCol = 0, - lastColKOxx = numXi * numEta - 1, lastColBinv = numEta - 1; - - if (int(Binv.n_rows) > numEta) { - arma::mat Binv_t; - for (int i = 0; i < t; i++) { - firstRow = i * numEta; - lastRow = (i + 1) * numEta - 1; - - kronXi_t = - kronXi.submat(firstRow, firstCol, lastRow, lastColKOxx); - Binv_t = Binv.submat(firstRow, firstCol, lastRow, lastColBinv); - - Ey.row(i) = (Binv_t * (trOmegaSigma + alpha + gammaXi * (beta0 + l1 * x.row(i).t()) + - kronXi_t * omegaXiXi * (beta0 + l1 * x.row(i).t())) + l2 * u.row(i).t()).t(); - } - } else { - for (int i = 0; i < t; i++) { - firstRow = i * numEta; - lastRow = (i + 1) * numEta - 1; - - kronXi_t = - kronXi.submat(firstRow, firstCol, lastRow, lastColKOxx); - - Ey.row(i) = (Binv * (trOmegaSigma + alpha + gammaXi * (beta0 + l1 * x.row(i).t()) + - kronXi_t * omegaXiXi * (beta0 + l1 * x.row(i).t())) + l2 * u.row(i).t()).t(); - } - } - return Ey; -} - - -// [[Rcpp::export]] -arma::mat sigmaQmlCpp(Rcpp::List m, int t) { - int numEta = Rcpp::as(m["numEta"]); - int numXi = Rcpp::as(m["numXi"]); - arma::mat gammaXi = Rcpp::as(m["gammaXi"]); - arma::mat omegaXiXi = Rcpp::as(m["omegaXiXi"]); - arma::mat l1 = Rcpp::as(m["L1"]); - arma::mat l2 = Rcpp::as(m["L2"]); - arma::mat x = Rcpp::as(m["x"]); - arma::mat u = Rcpp::as(m["u"]); - arma::mat Sigma1 = Rcpp::as(m["Sigma1"]); - arma::mat Sigma2ThetaEpsilon = Rcpp::as(m["Sigma2ThetaEpsilon"]); - arma::mat psi = Rcpp::as(m["psi"]); - arma::mat Ie = Rcpp::as(m["Ieta"]); - arma::mat sigmaE = arma::mat(t * numEta, numEta); - arma::mat Binv = Rcpp::as(m["Binv"]); - arma::mat varZ = varZCpp(omegaXiXi, Sigma1, numEta); - arma::mat kronXi = Rcpp::as(m["kronXi"]); - - int firstRow, lastRow, firstCol = 0, lastColSigmaE = numEta - 1, - lastColKOxx = numXi * numEta - 1; - arma::mat kronXi_t; - arma::mat Sigma2; - if (int(Binv.n_rows) > numEta) { - arma::mat Binv_t; - for (int i = 0; i < t; i++) { - firstRow = i * numEta; - lastRow = (i + 1) * numEta - 1; - - kronXi_t = - kronXi.submat(firstRow, firstCol, lastRow, lastColKOxx); - Binv_t = Binv.submat(firstRow, firstCol, lastRow, lastColSigmaE); - - Sigma2 = Binv_t * psi * Binv_t.t() + Sigma2ThetaEpsilon; - sigmaE.submat(firstRow, firstCol, lastRow, lastColSigmaE) = - (Binv_t * (gammaXi + 2 * kronXi_t * omegaXiXi)) * Sigma1 * - (Binv_t * (gammaXi + 2 * kronXi_t * omegaXiXi)).t() + Sigma2 + - Binv_t * varZ * Binv_t.t(); - } - } else { - varZ = Binv * varZ * Binv.t(); - Sigma2 = Binv * psi * Binv.t() + Sigma2ThetaEpsilon; - for (int i = 0; i < t; i++) { - firstRow = i * numEta; - lastRow = (i + 1) * numEta - 1; - - kronXi_t = - kronXi.submat(firstRow, firstCol, lastRow, lastColKOxx); - - sigmaE.submat(firstRow, firstCol, lastRow, lastColSigmaE) = - (Binv * (gammaXi + 2 * kronXi_t * omegaXiXi)) * Sigma1 * - (Binv * (gammaXi + 2 * kronXi_t * omegaXiXi)).t() + Sigma2 + varZ; - } - } - - return sigmaE; -} - - -// [[Rcpp::export]] -arma::mat calcKronXi(Rcpp::List m, int t) { - int numEta = Rcpp::as(m["numEta"]); - int numXi = Rcpp::as(m["numXi"]); - arma::mat beta0 = Rcpp::as(m["beta0"]); - arma::mat omegaXiXi = Rcpp::as(m["omegaXiXi"]); - arma::mat l1 = Rcpp::as(m["L1"]); - arma::mat x = Rcpp::as(m["x"]); - arma::mat Ie = Rcpp::as(m["Ieta"]); - // dimensions of a single kronecker product (A (kron) B) = (m x n) (kron) (p x q) = (mp x nq) - // in this case: (numEta x numEta) (kron) (1 x numXi) = (numEta x numEta * numXi) - arma::mat out = arma::mat(t * numEta, numXi * numEta); - - for (int i = 0; i < t; i++) { - out.submat(i * numEta, 0, (i + 1) * numEta - 1, numXi * numEta - 1) = - arma::kron(Ie, beta0.t() + x.row(i) * l1.t()); - } - return out; -} - - -// [[Rcpp::export]] -arma::mat calcBinvCpp(Rcpp::List m, int t) { - int numEta = Rcpp::as(m["numEta"]); - int numXi = Rcpp::as(m["numXi"]); - int kOmegaEta = Rcpp::as(m["kOmegaEta"]); - arma::mat gammaEta = Rcpp::as(m["gammaEta"]); - - arma::mat Ie = Rcpp::as(m["Ieta"]); - arma::mat B = Ie - gammaEta; - arma::mat omegaEtaXi = Rcpp::as(m["omegaEtaXi"]); - - if (numEta == 1) return Ie; - else if (kOmegaEta == 0) return arma::inv(B); - - arma::mat kronXi = Rcpp::as(m["kronXi"]); - arma::mat B_t = arma::mat(t * numEta, numEta); - - int firstRow, lastRow, firstCol = 0, lastColB = numEta - 1, - lastColKOxx = numXi * numEta - 1; - arma::mat kronXi_t; - - for (int i = 0; i < t; i++) { - firstRow = i * numEta; - lastRow = (i + 1) * numEta - 1; - - kronXi_t = - kronXi.submat(firstRow, firstCol, lastRow, lastColKOxx); - - B_t.submat(firstRow, firstCol, lastRow, lastColB) = - arma::inv(B - kronXi_t * omegaEtaXi); - } - - return B_t; -} - - -arma::vec logNormalPdf(const arma::vec& x, const arma::vec& mu, const arma::mat& sigma) { - - int n = x.n_elem; - arma::vec result = arma::zeros(n); - - // Constants - double log_2pi = std::log(2.0 * M_PI); - for (int j = 0; j < int(sigma.n_cols); j++) { - for (int i = 0; i < n; i++) { - double diff = x(i, j) - mu(i, j); - double sigma_sq = sigma(i, j) * sigma(i, j); - - // Log of the normal distribution PDF equation - result(i) += -0.5 * log_2pi - std::log(sigma(i, j)) - 0.5 * (diff * diff) / sigma_sq; - } - } - return result; -} - - -// [[Rcpp::export]] -arma::vec dnormCpp(const arma::vec& x, const arma::vec& mu, const arma::vec& sigma) { - return logNormalPdf(x, mu, sigma); -} - - -// [[Rcpp::export]] -arma::mat varZCpp(arma::mat Omega, arma::mat Sigma1, int numEta) { - arma::mat varZ = arma::mat(numEta, numEta); - int subRows = Omega.n_rows / numEta; - for (int i = 0; i < numEta; i++) { - varZ(i, i) = varZSubOmega(Omega.submat(i * subRows, 0, - (i + 1) * subRows - 1, (Omega.n_cols - 1)), Sigma1); - } - return varZ; -} - - -double varZSubOmega(arma::mat Omega, arma::mat Sigma1) { - - int ds = Sigma1.n_rows; - double varZ = 0; - - for (int i = 0; i < ds; i++) { - for (int j = 0; j < ds; j++) { - for (int k = 0; k < ds; k++) { - for (int s = 0; s < ds; s++) { - varZ += Omega(i, j) * Omega(k, s) * - (Sigma1(i, j) * Sigma1(k, s) + - Sigma1(i, k) * Sigma1(j, s) + - Sigma1(i, s) * Sigma1(j, k)); - } - } - } - } - double trOmegaSigma1 = arma::trace(Omega * Sigma1); - return varZ - trOmegaSigma1 * trOmegaSigma1; -} - - -arma::vec traceOmegaSigma1(const arma::mat OmegaSigma1, const int numEta) { - arma::vec trace = arma::vec(numEta); - int subRows = OmegaSigma1.n_rows / numEta; - for (int i = 0; i < numEta; i++) { - for (int j = 0; j < int(OmegaSigma1.n_cols); j++) { - trace(i) += OmegaSigma1(i * subRows + j, j); - } - } - return trace; -} diff --git a/src/multiplyIndicators.cpp b/src/multiplyIndicators.cpp deleted file mode 100644 index 0fc3946..0000000 --- a/src/multiplyIndicators.cpp +++ /dev/null @@ -1,28 +0,0 @@ -#include -#include -#include -using namespace std; -using namespace Rcpp; - -//' Multiply indicators -//' @param df A data DataFrame -//' @return A NumericVector -//' @export -// [[Rcpp::export]] -NumericVector multiplyIndicatorsCpp(DataFrame df) { - if (df.size() <= 1) { - return df[0]; - } - NumericVector x = df[0]; - NumericVector y = df[1]; - NumericVector product = x*y; - - // Delete the two first entries - df.erase(df.begin(), df.begin() + 2); - // Add product to the first entry - df.push_front(product); - // Recursing - return multiplyIndicatorsCpp(df); -} - - diff --git a/src/mvnorm.cpp b/src/mvnorm.cpp deleted file mode 100644 index 5659ee3..0000000 --- a/src/mvnorm.cpp +++ /dev/null @@ -1,65 +0,0 @@ -// [[Rcpp::depends("RcppArmadillo")]] -#include -#include "mvnorm.h" - - -static double const log2pi = std::log(2.0 * M_PI); - - -void inplace_tri_mat_mult(arma::rowvec &x, arma::mat const &trimat){ - arma::uword const n = trimat.n_cols; - - for(unsigned j = n; j-- > 0;){ - double tmp(0.); - for(unsigned i = 0; i <= j; ++i) - tmp += trimat.at(i, j) * x[i]; - x[j] = tmp; - } -} - - -// [[Rcpp::export]] -arma::vec rep_dmvnorm(const arma::mat &x, - const arma::mat &expected, - const arma::mat &sigma, const int t) { - arma::vec out = arma::vec(t); - int ncolSigma = sigma.n_cols; - int firstRow, lastRow, firstCol, lastCol; - for (int i = 0; i < t; i++) { - firstRow = i * ncolSigma; - lastRow = (i + 1) * ncolSigma - 1; - firstCol = 0; - lastCol = ncolSigma - 1; - - out(i) = dmvnrm_arma_mc(x.row(i), expected.row(i), - sigma.submat(firstRow, firstCol, lastRow, lastCol), true)(0); - } - return out; -} - - -// [[Rcpp::export]] -arma::vec dmvnrm_arma_mc(arma::mat const &x, - arma::rowvec const &mean, - arma::mat const &sigma, - bool const logd = true) { - using arma::uword; - uword const n = x.n_rows, - xdim = x.n_cols; - arma::vec out(n); - arma::mat const rooti = arma::inv(trimatu(arma::chol(sigma))); - double const rootisum = arma::sum(log(rooti.diag())), - constants = -(double)xdim/2.0 * log2pi, - other_terms = rootisum + constants; - - arma::rowvec z; - for (uword i = 0; i < n; i++) { - z = (x.row(i) - mean); - inplace_tri_mat_mult(z, rooti); - out(i) = other_terms - 0.5 * arma::dot(z, z); - } - - if (logd) - return out; - return exp(out); -} diff --git a/src/mvnorm.h b/src/mvnorm.h deleted file mode 100644 index 390c2d8..0000000 --- a/src/mvnorm.h +++ /dev/null @@ -1,12 +0,0 @@ -#ifndef mvnorm_h -#define mvnorm_h - - -void inplace_tri_mat_mult(arma::rowvec &x, arma::mat const &trimat); -arma::vec dmvnrm_arma_mc(arma::mat const &x, - arma::rowvec const &mean, - arma::mat const &sigma, - bool const logd); - - -#endif // !mvnorm_h diff --git a/tests/testthat/test_get_pi_data_syntax.R b/tests/testthat/test_get_pi_data_syntax.R deleted file mode 100644 index f6122c1..0000000 --- a/tests/testthat/test_get_pi_data_syntax.R +++ /dev/null @@ -1,14 +0,0 @@ -m1 <- ' - # Outer Model - X =~ x1 + x2 + x3 - Y =~ y1 + y2 + y3 - Z =~ z1 + z2 + z3 - - # Inner model - Y ~ X + Z + X:Z -' -est <- modsem_pi(m1, oneInt) -lav_est <- extract_lavaan(est) -syntax <- get_pi_syntax(m1) -data <- get_pi_data(m1, oneInt) -lavaan::sem(syntax, data) diff --git a/tests/testthat/test_lav_models.R b/tests/testthat/test_lav_models.R deleted file mode 100644 index 655c7c5..0000000 --- a/tests/testthat/test_lav_models.R +++ /dev/null @@ -1,79 +0,0 @@ -set.seed(123) -models <- list(m1 = ' - # latent variables - ind60 =~ x1 + x2 + x3 - dem60 =~ y1 + y2 + y3 + y4 - dem65 =~ y5 + y6 + y7 + y8 - # regressions - dem60 ~ ind60 - dem65 ~ ind60 + dem60 - dem65 ~ ind60:dem60 - # residual covariances - y1 ~~ y5 - y2 ~~ y4 + y6 - y3 ~~ y7 - y4 ~~ y8 - y6 ~~ y8 - ', - m3 = ' visual =~ x1 + x2 + x3 - textual =~ x4 + x5 + x6 - speed =~ x7 + x8 + x9 - visual ~ speed + textual + speed:textual', - m4 = 'visual =~ x1 + start(0.8)*x2 + start(1.2)*x3 - textual =~ x4 + start(0.5)*x5 + start(1.0)*x6 - speed =~ x7 + start(0.7)*x8 + start(1.8)*x9 - visual ~ speed + textual + speed:textual', - m5 = '# three-factor model - visual =~ x1 + x2 + x3 - textual =~ x4 + x5 + x6 - speed =~ x7 + x8 + x9 - visual ~ speed + textual + 0.1*speed:textual - # intercepts with fixed values - x1 + x2 + x3 + x4 ~ 0.5*1', - m6 = '# three-factor model - visual =~ x1 + x2 + x3 - textual =~ x4 + x5 + x6 - speed =~ x7 + x8 + x9 - visual ~ speed + textual + speed:textual - # intercepts - x1 ~ 1 - x2 ~ 1 - x3 ~ 1 - x4 ~ 1 - x5 ~ 1 - x6 ~ 1 - x7 ~ 1 - x8 ~ 1 - x9 ~ 1' - -) - - - -data <- list(d1 = lavaan::PoliticalDemocracy, - d3 = lavaan::HolzingerSwineford1939, - d4 = lavaan::HolzingerSwineford1939, - d5 = lavaan::HolzingerSwineford1939, - d6 = lavaan::HolzingerSwineford1939) - -nativeMethods <- allNativeMethods[allNativeMethods != "pind"] -methods <- list(m1 = nativeMethods[nativeMethods != "ca"], - m3 = nativeMethods, - m4 = nativeMethods, - m5 = nativeMethods, - m6 = nativeMethods[nativeMethods != c("uca", "ca")]) - - -estimates <- vector("list", length(models)) -for (i in seq_along(estimates)) { - estimates[[i]] <- runMultipleMethods(models[[i]], data = data[[i]], - methods = methods[[i]], - estimator = "ML") - -} - -# testing plot function -plot_interaction(x = "ind60", z = "dem60", y = "dem65", xz = "ind60:dem60", - vals_z = c(-0.5, 0.5), model = estimates[[1]][["rca"]]) -plot_interaction(x = "speed", z = "textual", y = "visual", xz = "speed:textual", - vals_z = c(-0.5, 0.5), model = estimates[[2]][["ca"]]) diff --git a/tests/testthat/test_lms.R b/tests/testthat/test_lms.R deleted file mode 100644 index 3889dc1..0000000 --- a/tests/testthat/test_lms.R +++ /dev/null @@ -1,100 +0,0 @@ -devtools::load_all() -m1 <- " -# Outer Model - X =~ x1 - Z =~ z1 - x1 ~~ 0.1 * x1 - Y =~ y1 - -# Inner model - Y ~ a * X + a * Z - Y ~~ Y - Y ~ b * X:Z + 0.05 * X:X - b == a * 1.2 -" -# funnily enough, the starting parameters from the double centering approach -# give better loglikelihoods than the ones arrived at by the EM algorithm -# i.e., the loglikelihood decreases from the starting parameters -startTime1 <- Sys.time() -est1 <- modsem(m1, oneInt, - method = "lms", - optimize = TRUE, verbose = TRUE, - convergence = 1e-2, -) -duration1 <- Sys.time() - startTime1 -plot_interaction("X", "Z", "Y", "X:Z", -3:3, c(-0.5, 0.5), est1) -print(summary(est1, adjusted.stat = TRUE)) - -# I have no clue why, but changing the ordering of how the interaction terms -# are specified, ends up changing the number of iterations (and results ever -# so slightly) -- even though the matrices are exactly the same. This can be -# seen through the fact that the starting loglikelihoods are the same (if optimized) -# indicating that the matrices are the same (i.e,. produce the same results, when -# given the same values). -# Solution: slightly different results from lavaan, giving slightly different -# starting parameters -tpb <- " -# Outer Model (Based on Hagger et al., 2007) - LATENT_VAR_ATT =~ a1 * att1 + a2 * att2 + att3 + att4 + att5 - SN =~ s1 * sn1 + sn2 - PBC =~ p1 * pbc1 + pbc2 + pbc3 - INT =~ i1 * int1 + int2 + int3 - BEH =~ b1 + b2 - -# Inner Model (Based on Steinmetz et al., 2011) - # Causal Relationsships - INT ~ gamma_int_att * LATENT_VAR_ATT + b * SN + b * PBC - BEH ~ 0.2 * INT + a * PBC - BEH ~ PBC:INT - gamma_int_att == a - p1 == 1 - a2 == 1 - s1 == 1 - i1 == 1 -" - -covModel <- ' -PBC ~ a * LATENT_VAR_ATT + SN -' - -startTime2 <- Sys.time() -testthat::expect_warning({ - est2 <- modsem(tpb, TPB, - method = "lms", optimize = TRUE, verbose = TRUE, - convergence = 1, cov.syntax = covModel, - nodes = 16, robust.se = TRUE) - }, regexp = "It is recommended .* between endogenous variables .*") -duration2 <- Sys.time() - startTime2 -plot_interaction(x = "INT", z = "PBC", y = "BEH", vals_z = c(-0.5, 0.5), model = est2) -print(summary(est2, H0 = FALSE)) -var_interactions(est2) -standardized_estimates(est2) -vcov(est2) -modsem_inspect(est2) -coef(est2) -coefficients(est2) - - -tpb2 <- ' -# Outer Model (Based on Hagger et al., 2007) - ATT =~ att1 + att2 + att3 + att4 + att5 - SN =~ sn1 + sn2 - PBC =~ pbc1 + pbc2 + pbc3 - INT =~ int1 + int2 + int3 - BEH =~ b1 + b2 - -# Inner Model (Based on Steinmetz et al., 2011) - # Covariances - ATT ~~ SN + PBC - PBC ~~ SN - # Causal Relationsships - INT ~ a * ATT + b * SN + c * PBC - BEH ~ INT + PBC - BEH ~ INT:PBC -' - -testthat::expect_warning({ - modsem(tpb, TPB, method = "lms", - convergence = 1000, - nodes = 16, calc.se = FALSE) - }, regexp = "It is recommended .* between exogenous and endogenous .*") diff --git a/tests/testthat/test_mplus.R b/tests/testthat/test_mplus.R deleted file mode 100644 index 4800382..0000000 --- a/tests/testthat/test_mplus.R +++ /dev/null @@ -1,24 +0,0 @@ -devtools::load_all() -m1 <- ' -# Outer Model -X =~ x1 -X =~x2 -Z =~ z1 + z2 -Y =~ y1 + y2 - - -# Inner model -Y ~ X + Z -Y ~ X:Z -' -run <- tryCatch({ - MplusAutomation::detectMplus() - TRUE - }, - error = function(e) FALSE -) -if (run) { - mplus <- modsem(m1, oneInt, method = "mplus") - print(summary(mplus)) - plot_interaction(x = "X", z = "Z", y = "Y", xz = "X:Z", vals_z = c(-0.5, 0.5), model = mplus) -} diff --git a/tests/testthat/test_multigroup.R b/tests/testthat/test_multigroup.R deleted file mode 100644 index b7feefd..0000000 --- a/tests/testthat/test_multigroup.R +++ /dev/null @@ -1,23 +0,0 @@ -devtools::load_all() -HS.model <- ' visual =~ x1 + x2 + x3 - textual =~ x4 + x5 + x6 - speed =~ x7 + x8 + x9 - visual ~ c(v_t1, v_t2) * textual + speed - interaction := v_t2 - v_t1' - -fit_lavaan <- lavaan::sem(HS.model, - data = lavaan::HolzingerSwineford1939, - group = "school") - -# using modsem -fit_modsem <- modsem(HS.model, - data = lavaan::HolzingerSwineford1939, - group = "school") - - -lavaanEst <- lavaan::parameterEstimates(fit_lavaan) -lavaanEst[is.na(lavaanEst)] <- -999 -modsemEst <- lavaan::parameterEstimates(fit_modsem$lavaan) -modsemEst[is.na(modsemEst)] <- -999 -testthat::expect_equal(lavaanEst, modsemEst) - diff --git a/tests/testthat/test_pathtracer.R b/tests/testthat/test_pathtracer.R deleted file mode 100644 index 5c29ab7..0000000 --- a/tests/testthat/test_pathtracer.R +++ /dev/null @@ -1,47 +0,0 @@ -devtools::load_all() - -m1 <- modsemify( - ' -y1 ~ x1 -x1 ~ x2 -x2 ~ x3 -x3 ~ x1 - ' -) -testthat::expect_warning(trace_path(m1, "x1", "x1"), - "non-recursive model \\(infinite loop\\)") -m2 <- modsemify( - ' -y1 ~ x1 -x1 ~ x2 -x2 ~ x3 -x3 ~ x4 -x1 ~~ x1 -x2 ~~ x2 -x3 ~~ x3 -x4 ~~ x4 - ' -) - -testthat::expect_equal(trace_path(m2, "x3", "x3"), "(x3~~x3 + x3~x4 ^ 2 * x4~~x4)") -testthat::expect_equal(trace_path(m2, "x4", "x4"), "(x4~~x4)") - -m3 <- modsemify('visual =~ x1 + x2 + x3 - textual =~ x4 + x5 + x6 - speed =~ x7 + x8 + x9 - visual ~ speed + textual + speed:textual') - -testthat::expect_true(is.na(trace_path(m3, "textual", "textual"))) - - -m4 <- modsemify(' - # Outer Model - X =~ x1 + x2 +x3 - Y =~ y1 + y2 + y3 - Z =~ z1 + z2 + z3 - - # Inner model - Y ~ X + Z + X:Z -') - -testthat::expect_equal(trace_path(m4, "X", "X", missing.cov = TRUE), "(X~~X)") diff --git a/tests/testthat/test_qml.R b/tests/testthat/test_qml.R deleted file mode 100644 index 1593fd7..0000000 --- a/tests/testthat/test_qml.R +++ /dev/null @@ -1,87 +0,0 @@ -devtools::load_all() -set.seed(123) - -m1 <- ' -X =~ x1 + x2 + x3 -Y =~ y1 + y2 + y3 -Z =~ z1 + z2 + z3 -Y ~ X + Z + a * X:Z -' - -est1 <- modsem(m1, data = oneInt, convergence = 1e-2, method = "qml", - robust.se = TRUE) -print(summary(est1, scientific = TRUE)) -plot_interaction(x = "X", z = "Z", y = "Y", xz = "X:Z", vals_z = c(-0.5, 0.5), model = est1) - -tpb <- ' -# Outer Model (Based on Hagger et al., 2007) - ATT =~ att1 + att2 + att3 + att4 + att5 - SN =~ sn1 + sn2 - PBC =~ pbc1 + pbc2 + pbc3 - INT =~ int1 + int2 + int3 - BEH =~ b1 + b2 - -# Inner Model (Based on Steinmetz et al., 2011) - BEH ~ INT + PBC - INT ~ ATT + SN + PBC - BEH ~ PBC:INT -' - -est2 <- modsem(tpb, data = TPB, method = "qml", - robust.se = TRUE, - standardize = TRUE, convergence = 1e-2) -print(summary(est2, H0 = FALSE)) - -testthat::expect_equal(standardized_estimates(est2), - parameter_estimates(est2)) - -calcCovParTable("BEH", "BEH", parameter_estimates(est2)) |> - testthat::expect_equal(1) - -vcov(est2) -modsem_inspect(est2) -coef(est2) -coefficients(est2) - -# Observed Variables -m3 <- ' -X =~ x1 -Y =~ y1 -Z =~ z1 -Y ~ X + Z + X:Z -' - -est3 <- modsem(m3, data = oneInt, convergence = 1e-2, method = "qml", - robust.se = TRUE) -print(summary(est3, scientific = TRUE)) -plot_interaction(x = "X", z = "Z", y = "Y", xz = "X:Z", vals_z = c(-0.5, 0.5), model = est3) - -tpb2 <- ' -# Outer Model (Based on Hagger et al., 2007) - ATT =~ att1 + att2 + att3 + att4 + att5 - SN =~ sn1 + sn2 - PBC =~ pbc1 + pbc2 + pbc3 - INT =~ int1 #+ int2 + int3 - BEH =~ b1 + b2 - -# Inner Model (Based on Steinmetz et al., 2011) - BEH ~ INT + PBC - INT ~ ATT + SN + PBC - BEH ~ PBC:INT -' - -est4 <- modsem(tpb2, data = TPB, method = "qml", - robust.se = TRUE, - standardize = TRUE, convergence = 1e-2) -print(summary(est4, H0 = FALSE)) - -testthat::expect_equal(standardized_estimates(est4), - parameter_estimates(est4)) - -calcCovParTable("BEH", "BEH", parameter_estimates(est4)) |> - testthat::expect_equal(1) - -vcov(est4) -modsem_inspect(est4) -coef(est4) -coefficients(est4) diff --git a/tests/testthat/test_quadratic_effects.R b/tests/testthat/test_quadratic_effects.R deleted file mode 100644 index b13bd7b..0000000 --- a/tests/testthat/test_quadratic_effects.R +++ /dev/null @@ -1,32 +0,0 @@ -devtools::load_all() -m1 <- ' -X =~ x1 + x2 + x3 -Y =~ y1 + y2 + y3 -Z =~ z1 + z2 + z3 -Y ~ X + Z + X:X -' -methods <- c("rca", "ca", "dblcent", "lms", "qml") -ests <- vector("list", length(methods)) -names(ests) <- methods - - -for (method in methods) { - if (method == "lms") { - ests[[method]] <- modsem(m1, data = oneInt, method = method, - convergence = 1e-3) - } else { - ests[[method]] <- modsem(m1, data = oneInt, method = method) - } -} - -nlsemModel <- ' -ENJ =~ enjoy1 + enjoy2 + enjoy3 + enjoy4 + enjoy5 -CAREER =~ career1 + career2 + career3 + career4 -SC =~ academic1 + academic2 + academic3 + academic4 + academic5 + academic6 -CAREER ~ ENJ + SC + ENJ:ENJ + SC:SC + ENJ:SC -' - -est_qml2 <- modsem(nlsemModel, data = jordan, method = "qml", - mean.observed = FALSE, convergence = 1e-2) -est_rca2 <- modsem(nlsemModel, data = jordan, method = "rca") -est_dblcent2 <- modsem(nlsemModel, data = jordan, method = "dblcent") diff --git a/tests/testthat/test_sam.R b/tests/testthat/test_sam.R deleted file mode 100644 index 6307aca..0000000 --- a/tests/testthat/test_sam.R +++ /dev/null @@ -1,13 +0,0 @@ -devtools::load_all() - -m1 <- ' -X =~ x1 + x2 + x3 -Y =~ y1 + y2 + y3 -Z =~ z1 + z2 + z3 -Y ~ X + Z + X:Z -' - -est <- lavaan::sam(m1, lapplyDf(oneInt, function(x) x - mean(x)), se = "none") -summary(est) - -plot_interaction(x = "X", z = "Z", y = "Y", xz = "X:Z", vals_z = c(-0.5, 0.5), model = est) diff --git a/tests/testthat/test_syntax.R b/tests/testthat/test_syntax.R deleted file mode 100644 index 49704d8..0000000 --- a/tests/testthat/test_syntax.R +++ /dev/null @@ -1,110 +0,0 @@ -devtools::load_all() -models <- list(m1 = ' - # latent variables - ind60 =~ x1 + x2 + x3 - dem60 =~ y1 + y2 + y3 + y4 - dem65 =~ y5 + y6 + y7 + y8 - # regressions - dem60 ~ ind60 - dem65 ~ ind60 + dem60 - # residual covariances - y1 ~~ y5 - y2 ~~ y4 + y6 - y3 ~~ y7 - y4 ~~ y8 - y6 ~~ y8 - ', - m3 = ' visual =~ x1 + x2 + x3 - textual =~ x4 + x5 + x6 - speed =~ x7 + x8 + x9 ', - m4 = 'visual =~ x1 + start(0.8)*x2 + start(1.2)*x3 - textual =~ x4 + start(0.5)*x5 + start(1.0)*x6 - speed =~ x7 + start(0.7)*x8 + start(1.8)*x9', - m5 = '# three-factor model - visual =~ x1 + x2 + x3 - textual =~ x4 + x5 + x6 - speed =~ x7 + x8 + x9 - # intercepts with fixed values - x1 + x2 + x3 + x4 ~ 0.5*1', - m6 = '# three-factor model - visual =~ x1 + x2 + x3 - textual =~ x4 + x5 + x6 - speed =~ x7 + x8 + x9 - # intercepts - x1 ~ 1 - x2 ~ 1 - x3 ~ 1 - x4 ~ 1 - x5 ~ 1 - x6 ~ 1 - x7 ~ 1 - x8 ~ 1 - x9 ~ 1', - m7 = ' # direct effect - Y ~ c*X - # mediator - M ~ a*X - Y ~ b*M - # indirect effect (a*b) - ab := a*b - # total effect - total := c + (a*b) - ' -) - - -set.seed(1234) -X <- rnorm(100) -M <- 0.5*X + rnorm(100) -Y <- 0.7*M + rnorm(100) -d7 <- data.frame(X = X, Y = Y, M = M) - -data <- list(d1 = lavaan::PoliticalDemocracy, - d3 = lavaan::HolzingerSwineford1939, - d4 = lavaan::HolzingerSwineford1939, - d5 = lavaan::HolzingerSwineford1939, - d6 = lavaan::HolzingerSwineford1939, - d7 = d7) - - -estimates <- vector("list", length(models)) -for (i in seq_along(estimates)) { - estimates[[i]]$lav <- tryCatch({ - est <- lavaan::sem(models[[i]], data = data[[i]]) - est - }, - warning = function(e) { - warning("Warning in lav model ", i) - warning(capturePrint(e)) - }, - error = function(e) { - est <- NA - warning("Error in lav model ", i) - }, - finally = { - est - }) - estimates[[i]]$modsem <- tryCatch({ - est <- modsem(models[[i]], data = data[[i]], estimator = "ML") - est - }, - warning = function(e) { - warning("Warning in modsem model ", i) - est - }, - error = function(e) { - est <- NA - warning("Error in modsem model ", i) - }, - finally = { - est - }) -} - -for (est in estimates) { - lavaanEst <- lavaan::parameterEstimates(est$lav) - lavaanEst[is.na(lavaanEst)] <- -999 - modsemEst <- lavaan::parameterEstimates(est$modsem$lavaan) - modsemEst[is.na(modsemEst)] <- -999 - testthat::expect_equal(lavaanEst, modsemEst) -} diff --git a/tests/testthat/test_tpb.R b/tests/testthat/test_tpb.R deleted file mode 100644 index b66e592..0000000 --- a/tests/testthat/test_tpb.R +++ /dev/null @@ -1,25 +0,0 @@ -devtools::load_all() -tpb <- ' -# Outer Model (Based on Hagger et al., 2007) - ATT =~ att1 + att2 + att3 + att4 + att5 - SN =~ sn1 + sn2 - PBC =~ pbc1 + pbc2 + pbc3 - INT =~ int1 + int2 + int3 - BEH =~ b1 + b2 - -# Inner Model (Based on Steinmetz et al., 2011) - # Covariances - ATT ~~ SN + PBC - PBC ~~ SN - # Causal Relationsships - INT ~ a * ATT + b * SN + c * PBC - BEH ~ INT + PBC - BEH ~ INT:PBC -' - -method <- c("ca", "rca", "uca", "dblcent") -ests <- lapply(method, function(m) modsem(tpb, data = TPB, method = m)) -estsMatch <- lapply(method, function(m) modsem(tpb, data = TPB, method = m, - match = TRUE)) - -print(summary(ests[[1]])) diff --git a/vignettes/.gitignore b/vignettes/.gitignore deleted file mode 100644 index beddcf9..0000000 --- a/vignettes/.gitignore +++ /dev/null @@ -1,4 +0,0 @@ -*.html -*.R -set_eval_*.bash -*.swp diff --git a/vignettes/customizing.Rmd b/vignettes/customizing.Rmd deleted file mode 100644 index 03f6cc5..0000000 --- a/vignettes/customizing.Rmd +++ /dev/null @@ -1,91 +0,0 @@ ---- -title: "customizing interaction terms" -output: rmarkdown::html_vignette -vignette: > - %\VignetteIndexEntry{customizing interaction terms} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} ---- - -```{r, include = FALSE} -EVAL_DEFAULT <- FALSE -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>", - eval = EVAL_DEFAULT -) -``` - -```{r setup, eval = TRUE} -library(modsem) -``` - -By default, modsem() creates product indicators for you, based on the interaction specified in your model. Behind the scenes we can see that modsem() creates in total 9 variables (product indicators) used as the indicators for your latent product. - -```{r} -m1 <- ' -# Outer Model -X =~ x1 + x2 + x3 -Y =~ y1 + y2 + y3 -Z =~ z1 + z2 + z3 - -# Inner model -Y ~ X + Z + X:Z -' - -est1 <- modsem(m1, oneInt) -cat(est1$syntax) -``` - -Whilst this often is sufficient, you might want some control over how these indicators are created. In general, modsem() has two mechanisms for giving control over the creating of indicator products: 1. By specifying the measurement model of your latent product your self, and 2. By using the mean() and sum() function, collectively known as parceling operations. - -## Specifying The Measurement Model - -By default, modsem() creates all possible combinations of different product indicators. However, another common approach is to match the indicators by order. For example, let's say you have an interaction between the laten variables X and Z: 'X =\~ x1 + x2' and 'Z =\~ z1 + z2'. By default you would get 'XZ =\~ x1z1 + x1z2 + x2z1 + x2z2'. If you wanted to use the *matching approach* you would want to get 'XZ =\~ x1z1 + x2z2' instead. To achieve this you can use the 'match = TRUE' argument. - -```{r} -m2 <- ' -# Outer Model -X =~ x1 + x2 -Y =~ y1 + y2 -Z =~ z1 + z2 - -# Inner model -Y ~ X + Z + X:Z -' - -est2 <- modsem(m2, oneInt, match = TRUE) -summary(est2) -``` - -## More complicated models - -I you want even more control you can use the `get_pi_syntax()` and -`get_pi_data()` functions, -such that you can extract the modified syntax and data from modsem, -and alter them accordingly. This can be particularly useful in -cases where you want to estimate a model using a feature in lavaan, -which isn't available in modsem. -For example, (as of yet) the syntax for both ordered- and -multigroup models isn't as flexible as in lavaan. -Thus you can modify the auto-generated syntax -(with the altered dataset) from modsem to suit your needs. - -```{r, eval = TRUE} -m3 <- ' -# Outer Model -X =~ x1 + x2 -Y =~ y1 + y2 -Z =~ z1 + z2 - -# Inner model -Y ~ X + Z + X:Z -' -syntax <- get_pi_syntax(m3) -cat(syntax) -``` - -```{r, eval = TRUE} -data <- get_pi_data(m3, oneInt) -head(data) -``` diff --git a/vignettes/interaction_two_etas.Rmd b/vignettes/interaction_two_etas.Rmd deleted file mode 100644 index 540237e..0000000 --- a/vignettes/interaction_two_etas.Rmd +++ /dev/null @@ -1,106 +0,0 @@ ---- -title: "interaction effects between endogenous variables" -output: rmarkdown::html_vignette -vignette: > - %\VignetteIndexEntry{interaction effects between endogenous variables} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} ---- - -```{r, include = FALSE} -EVAL_DEFAULT <- FALSE -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>", - eval = EVAL_DEFAULT -) -``` - -```{r setup} -library(modsem) -``` - -## The Problem -Interaction effects between two endogenous (i.e., dependent) variables work as you would expect for the product -indicator methods (`"dblcent", "rca", "ca", "uca"`). For the lms- and qml approach however, -it is not as straight forward. - -The lms- and qml approach can (by default) handle interaction effects between endogenous and exogenous -(i.e., independent) variables, -but not interaction effects between two endogenous variables. When there is an interaction -effect between two endogenous variables, the equations cannot easily be written in 'reduced' -form -- meaning that normal estimation procedures won't work. - -## The Solution -This being said, there is a work-around for these limitations for both the lms- and qml-approach. -In essence, the model can be split into two parts, one linear and one non-linear. -Basically, you can replace the covariance matrix used in the estimation of the non-linear model, -with the model-implied covariance matrix from a linear model. Thus you can treat an -endogenous variable as if it were exogenous -- given that it can be expressed in -a linear model. - -## Example -Let's consider the the theory of planned behaviour (TPB) where we wish to -estimate the quadratic effect of INT on BEH (INT:INT). With the following model: - -```{r} -tpb <- ' -# Outer Model (Based on Hagger et al., 2007) - ATT =~ att1 + att2 + att3 + att4 + att5 - SN =~ sn1 + sn2 - PBC =~ pbc1 + pbc2 + pbc3 - INT =~ int1 + int2 + int3 - BEH =~ b1 + b2 - -# Inner Model (Based on Steinmetz et al., 2011) - INT ~ ATT + SN + PBC - BEH ~ INT + PBC - BEH ~ INT:INT -' -``` - -Since INT is an -endogenous variable, its quadratic term (i.e., an interaction effect with itself) -would include two endogenous variables. Thus we would ordinarily not be able to estimate -this model using the lms- or qml-approach. -However, we can split the model into two parts, one linear and one non-linear. -While INT is an endogenous variable, it can be expressed in a linear model -- -since it is not affected by any interaction terms: - - -```{r} -tpb_linear <- 'INT ~ PBC + ATT + SN' -``` - -We could then remove this part from the original model, giving us: - -```{r} -tpb_nonlinear <- ' -# Outer Model (Based on Hagger et al., 2007) - ATT =~ att1 + att2 + att3 + att4 + att5 - SN =~ sn1 + sn2 - PBC =~ pbc1 + pbc2 + pbc3 - INT =~ int1 + int2 + int3 - BEH =~ b1 + b2 - -# Inner Model (Based on Steinmetz et al., 2011) - BEH ~ INT + PBC - BEH ~ INT:INT -' -``` - -We could now just estimate the non-linear model, since INT now is -an exogenous variable. This would however not incorporate the structural model -for INT. To address this, we can make modsem replace the covariance matrix (phi) -of (INT, PBC, ATT, SN) with the model-implied covariance matrix from the linear model, -whilst estimating both models simultaneously. To acheive this, we can use the -`cov.syntax` argument in `modsem`: - - -```{r} -est_lms <- modsem(tpb_nonlinear, data = TPB, cov.syntax = tpb_linear, method = "lms") -summary(est_lms) - -est_qml <- modsem(tpb_nonlinear, data = TPB, cov.syntax = tpb_linear, method = "qml") -summary(est_qml) -``` diff --git a/vignettes/lavaan.Rmd b/vignettes/lavaan.Rmd deleted file mode 100644 index db59428..0000000 --- a/vignettes/lavaan.Rmd +++ /dev/null @@ -1,42 +0,0 @@ ---- -title: "using lavaan functions" -output: rmarkdown::html_vignette -vignette: > - %\VignetteIndexEntry{using lavaan functions} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} ---- - -```{r, include = FALSE} -EVAL_DEFAULT <- FALSE -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>", - eval = EVAL_DEFAULT -) -``` - -```{r setup} -library(modsem) -``` -If you're using one of the product indicator approaches, you might want to use some lavaan -functions on top of the estimated lavaan-object. -To do so you can extract the lavaan-object using the `extract_lavaan()` function. - -```{r} -library(lavaan) - -m1 <- ' -# Outer Model -X =~ x1 + x2 + x3 -Y =~ y1 + y2 + y3 -Z =~ z1 + z2 + z3 - -# Inner model -Y ~ X + Z + X:Z -' - -est <- modsem(m1, oneInt) -lav_object <- extract_lavaan(est) -bootstrap <- bootstrapLavaan(lav_object, R = 500) -``` diff --git a/vignettes/lms_qml.Rmd b/vignettes/lms_qml.Rmd deleted file mode 100644 index 12c5424..0000000 --- a/vignettes/lms_qml.Rmd +++ /dev/null @@ -1,100 +0,0 @@ ---- -title: "LMS and QML approaches" -output: rmarkdown::html_vignette -vignette: > - %\VignetteIndexEntry{LMS and QML approaches} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} ---- - -```{r, include = FALSE} -EVAL_DEFAULT <- FALSE -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>", - eval = EVAL_DEFAULT -) -``` - -```{r setup} -library(modsem) -``` -# The Latent Moderated Structural Equations (LMS) and the Quasi Maximum Likelihood (QML) approach -Both the LMS- and QML approach works on most models, but interaction effects with -endogenous can be a bit tricky to estimate (see the [vignette](https://kss2k.github.io/intro_modsem/articles/interaction_two_etas.html). -Both approaches (particularily the LMS approach) are quite computationally intensive, and are thus partly implemented in C++ (using Rcpp and RcppArmadillo). -Additionally starting parameters are estimated using the double centering approach (and the means of the observed variables) -are used to generate good starting parameters for faster convergence. If you want to see the progress of the estimation process you can use ´verbose = TRUE´. - -## A Simple Example -Here you can see an example of the LMS approach for a simple model. -By default the summary function calculates fit measures compared to a null model (i.e., the same model without an interaction term). - -```{r} -library(modsem) -m1 <- ' -# Outer Model - X =~ x1 - X =~ x2 + x3 - Z =~ z1 + z2 + z3 - Y =~ y1 + y2 + y3 - -# Inner model - Y ~ X + Z - Y ~ X:Z -' - -lms1 <- modsem(m1, oneInt, method = "lms") -summary(lms1, standardized = TRUE) # standardized estimates -``` - -Here you can see the same example using the QML approach. - -```{r} -qml1 <- modsem(m1, oneInt, method = "qml") -summary(qml1) -``` - -## A more complicated example -Here you can see an example of a more complicated example using the model from the -theory of planned behaviour (TPB), where there are two endogenous variables, -where there is an interaction between an endogenous and exogenous variable. -When estimating more complicated models with the LMS-approach, -it is recommended that you increase the number of -nodes used for numerical integration. By default the number of nodes is set to 16, -and can be increased using the nodes argument. The argument has no effect on the QML approach. -When there is an interaction effect between an endogenous and exogenous variable, -it is recommended that you use at least 32 nodes for the LMS-approach. -You can also get robust standard errors by setting `robust.se = TRUE` in the -`modsem()` function. - -Note: If you want the lms-approach to give as similar results as possible to -mplus, you would have to increase the number of nodes (e.g., `nodes = 100`). - -```{r} -# ATT = Attitude, -# PBC = Perceived Behavioural Control -# INT = Intention -# SN = Subjective Norms -# BEH = Behaviour -tpb <- ' -# Outer Model (Based on Hagger et al., 2007) - ATT =~ att1 + att2 + att3 + att4 + att5 - SN =~ sn1 + sn2 - PBC =~ pbc1 + pbc2 + pbc3 - INT =~ int1 + int2 + int3 - BEH =~ b1 + b2 - -# Inner Model (Based on Steinmetz et al., 2011) - INT ~ ATT + SN + PBC - BEH ~ INT + PBC - BEH ~ INT:PBC -' - -lms2 <- modsem(tpb, TPB, method = "lms", nodes = 32) -summary(lms2) - -qml2 <- modsem(tpb, TPB, method = "qml") -summary(qml2, standardized = TRUE) # standardized estimates -``` - diff --git a/vignettes/methods.Rmd b/vignettes/methods.Rmd deleted file mode 100644 index f8f2d0d..0000000 --- a/vignettes/methods.Rmd +++ /dev/null @@ -1,57 +0,0 @@ ---- -title: "methods" -output: rmarkdown::html_vignette -vignette: > - %\VignetteIndexEntry{methods} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} ---- - -```{r, include = FALSE} -EVAL_DEFAULT <- FALSE -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>", - eval = EVAL_DEFAULT -) -``` - -```{r setup} -library(modsem) -``` - -There are a number of approaches for estimating interaction effects in SEM. In modsem(), the method = "method" argument allows you to choose which to use. - -- `"ca"` = constrained approach (Algina & Moulder, 2001) -- `"uca"` = unconstrained approach (Marsh, 2004) -- `"rca"` = residual centering approach (Little et al., 2006) - - default -- `"dblcent"`= double centering approach (Marsh., 2013) -- `"pind"` = basic product indicator approach (not recommended) -- `"lms"` = The Latent Moderated Structural equations approach - - note: there can not be an interaction between two endogenous variables. -- `"qml"` = The Quasi Maximum Likelihood approach. - - note: can only be done if you have a single endogenous (dependent) variable. -- `"mplus"` - - estimates model through Mplus, if it is installed - -```{r, eval = FALSE} - -m1 <- ' -# Outer Model -X =~ x1 + x2 + x3 -Y =~ y1 + y2 + y3 -Z =~ z1 + z2 + z3 - -# Inner model -Y ~ X + Z + X:Z -' - -modsem(m1, data = oneInt, method = "ca") -modsem(m1, data = oneInt, method = "uca") -modsem(m1, data = oneInt, method = "rca") -modsem(m1, data = oneInt, method = "dblcent") -modsem(m1, data = oneInt, method = "mplus") -modsem(m1, data = oneInt, method = "lms") -modsem(m1, data = oneInt, method = "qml") -``` diff --git a/vignettes/modsem.Rmd b/vignettes/modsem.Rmd deleted file mode 100644 index cb72e02..0000000 --- a/vignettes/modsem.Rmd +++ /dev/null @@ -1,158 +0,0 @@ ---- -title: "modsem" -output: rmarkdown::html_vignette -vignette: > - %\VignetteIndexEntry{modsem} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} ---- - -```{r, include = FALSE} -EVAL_DEFAULT <- FALSE -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>", - eval = EVAL_DEFAULT -) -``` - -```{r setup} -library(modsem) -``` -# The Basic Syntax - -modsem basically introduces a new feature to the lavaan-syntax -- the semicolon operator (":"). -The semicolon operator works the same way as in the lm()-function. In order to specify an interaction effect between two variables, you join them by Var1:Var2, -Models can either be estimated using the one of the product indicator approaches ("ca", "rca", "dblcent", "pind") or by using -the latent moderated structural equations approach ("lms"), or the quasi maximum likelihood approach ("qml"). -The product indicator approaches are estimated via lavaan, whilst the lms and qml approaches are estimated via modsem itself. - -## A Simple Example -Here we can see a simple example of how to specify an interaction effect between two latent variables in lavaan. - -```{r} -m1 <- ' - # Outer Model - X =~ x1 + x2 +x3 - Y =~ y1 + y2 + y3 - Z =~ z1 + z2 + z3 - - # Inner model - Y ~ X + Z + X:Z -' - -est1 <- modsem(m1, oneInt) -summary(est1) -``` -By default the model is estimated using the "dblcent" method. If you want to use another method, -but the method can be changed using the method argument. - -```{r} -est1 <- modsem(m1, oneInt, method = "lms") -summary(est1) -``` - -## Interactions Between two Observed Variables - -modsem does not only allow you to estimate interactions between latent variables, but also interactions between observed variables. Here we first run a regression with only observed variables, where there is an interaction between x1 and z2, and then run an equivalent model using modsem(). - -**Regression** - -```{r} -reg1 <- lm(y1 ~ x1*z1, oneInt) -summary(reg1) -``` - -**Using modsem()** In general, when you have interactions between observed variables it is recommended that you use method = "pind". -Interaction effects with observed variables is not supported by the LMS- and QML-approach. -In certain circumstances, you can define a latent variabale with a single indicator to estimate the interaction effect between two observed variables, -in the LMS and QML approach, but it is generally not recommended. - -```{r} -# Here we use "pind" as the method (see chapter 3) -est2 <- modsem('y1 ~ x1 + z1 + x1:z1', data = oneInt, method = "pind") -summary(est2) -``` - -## Interactions between Latent and Observed Variables - -modsem also allows you to estimate interaction effects between latent and observed variables. To do so, you just join a latent and an observed variable by a colon, e.g., 'latent:observer'. As with interactions between observed variables, it is generally recommended that you use method = "pind" for estimating the effect between observed x latent - -```{r} -m3 <- ' - # Outer Model - X =~ x1 + x2 +x3 - Y =~ y1 + y2 + y3 - - # Inner model - Y ~ X + z1 + X:z1 -' - -est3 <- modsem(m3, oneInt, method = "pind") -summary(est3) -``` - -## Quadratic Effects - -In essence, quadratic effects are just a special case of interaction effects. -Thus modsem can also be used to estimate quadratic effects. -```{r} - -m4 <- ' -# Outer Model -X =~ x1 + x2 + x3 -Y =~ y1 + y2 + y3 -Z =~ z1 + z2 + z3 - -# Inner model -Y ~ X + Z + Z:X + X:X -' - -est4 <- modsem(m4, oneInt, "qml") -summary(est4) -``` - -## More Complicated Examples - -Here we can see a more complicated example using the model for the theory of planned behaviour. - -```{r} - -tpb <- ' -# Outer Model (Based on Hagger et al., 2007) - ATT =~ att1 + att2 + att3 + att4 + att5 - SN =~ sn1 + sn2 - PBC =~ pbc1 + pbc2 + pbc3 - INT =~ int1 + int2 + int3 - BEH =~ b1 + b2 - -# Inner Model (Based on Steinmetz et al., 2011) - INT ~ ATT + SN + PBC - BEH ~ INT + PBC + INT:PBC -' -# the double centering apporach -est_tpb <- modsem(tpb, TPB) - -# using the lms approach -est_tpb_lms <- modsem(tpb, TPB, method = "lms") -summary(est_tpb_lms) -``` - -Here is an example included two quadratic- and one interaction effect, -using the included dataset `jordan`. -The dataset is subset of the PISA 2006 dataset. - -```{r} -m2 <- ' -ENJ =~ enjoy1 + enjoy2 + enjoy3 + enjoy4 + enjoy5 -CAREER =~ career1 + career2 + career3 + career4 -SC =~ academic1 + academic2 + academic3 + academic4 + academic5 + academic6 -CAREER ~ ENJ + SC + ENJ:ENJ + SC:SC + ENJ:SC -' - -est_jordan <- modsem(m2, data = jordan) -est_jordan_qml <- modsem(m2, data = jordan, method = "qml") -summary(est_jordan_qml) -``` - -Note: The other approaches work as well, but might be quite slow depending on the number of interaction effects (particularly for the LMS- and constrained approach). diff --git a/vignettes/observed_lms_qml.Rmd b/vignettes/observed_lms_qml.Rmd deleted file mode 100644 index b33db16..0000000 --- a/vignettes/observed_lms_qml.Rmd +++ /dev/null @@ -1,132 +0,0 @@ ---- -title: "observed variables in the LMS- and QML approach" -output: rmarkdown::html_vignette -vignette: > - %\VignetteIndexEntry{observed variables in the LMS- and QML approach} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} ---- - -```{r, include = FALSE} -EVAL_DEFAULT <- FALSE -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>", - eval = EVAL_DEFAULT -) -``` - -```{r setup} -library(modsem) -``` -# The Latent Moderated Structural Equations (LMS) and the Quasi Maximum Likelihood (QML) approach -In contrast to the other approaches, the LMS and QML approaches are designed to -handle latent variables only. Thus observed variables cannot be as easily used, -as in the other approaches. One way of getting around this is by specifying -your observed variable as a latent variable with a single indicator. `modsem()` -will by default constrain the factor loading to `1`, and the residual variance -of the indicator to `0`. Then, the only difference between the latent variable -and its indicator, is that (assuming that it is an exogenous variable) it has -a zero-mean. This will work for both the LMS- and QML -approach in most cases, except for two exceptions. - -## The LMS approach -For the LMS approach you can use the above mentioned approach in almost -all cases, except in the case where you wish to use an observed variable as -a moderating variable. In the LMS approach, you will usually select one variable -in an interaction term as a moderator. The interaction effect is then estimated -via numerical integration, at `n` quadrature nodes of the moderating variable. -This process however, requires that the moderating variable has an error-term, -as the distribution of the moderating variable is modelled as $X \sim N(Az, \varepsilon)$, -where $Az$ is the expected value of $X$ at quadrature point `k`, and -$\varepsilon$ is the error term. If the error-term is zero, the probability of -observing a given value of $X$ will not be computable. In most instances the -first variable in the interaction term, is chosen as the moderator. For example, -if the interaction term is `"X:Z"`, `"X"` will usually be chosen as the moderator. -Thus if only one of the variables are latent, you should put the latent variable -first in the interaction term. If both are observed, you have to specify a -measurement error (e.g., "x1 ~~ 0.1 * x1") for the indicator of the first variable -in the interaction term. - -```{r} -library(modsem) - -# interaction effect between a latent and an observed variable -m1 <- ' -# Outer Model - X =~ x1 # X is observed - Z =~ z1 + z2 # Z is latent - Y =~ y1 # Y is observed - -# Inner model - Y ~ X + Z - Y ~ Z:X -' - -lms1 <- modsem(m1, oneInt, method = "lms") - -# interaction effect between two observed variables -m2 <- ' -# Outer Model - X =~ x1 # X is observed - Z =~ z1 # Z is observed - Y =~ y1 # Y is observed - x1 ~~ 0.1 * x1 # specify a variance for the measurement error -# Inner model - Y ~ X + Z - Y ~ X:Z -' - -lms2 <- modsem(m1, oneInt, method = "lms") -summary(lms2) -``` - -## The QML approach -### If you are using the latest GitHub version -The estimation of the QML approach is different from the LMS approach, -and you do not have the same issue as in the LMS approach. Thus you don't -have to specify a measurement error for moderating variables. - -```{r} -m3 <- ' -# Outer Model - X =~ x1 # X is observed - Z =~ z1 # Z is observed - Y =~ y1 # Y is observed - -# Inner model - Y ~ X + Z - Y ~ X:Z -' - -qml3 <- modsem(m3, oneInt, method = "qml") -summary(qml3) -``` -### If you are using the CRAN version -If you are using the latest CRAN version, there is a slight -caveat, in that all endogenous variables have -to have atleast two indicators. This is due to a transformation, and -the approximation of the distribution of the indicators -in the endogenous variables. This problem will likely be fixed in a later -update, but as of now, latent endogenous variable need at least two indicators. -If a latent variable in the QML approach can be expressed without using an -interaction term, you can in some cases use the 'cov.syntax' argument as -a workaround. If this is the case, see the vignette on interaction effects -between two endogenous variable in the LMS- and QML approach -(`vignette("interaction_two_etas")`) - -```{r} -m4 <- ' -# Outer Model - X =~ x1 # X is observed - Z =~ z1 # Z is observed - Y =~ y1 + y2 # Y needs to be latent, needing atleast two indicators - -# Inner model - Y ~ X + Z - Y ~ X:Z -' - -qml4 <- modsem(m3, oneInt, method = "qml") -summary(qml4) -``` diff --git a/vignettes/plot_interactions.Rmd b/vignettes/plot_interactions.Rmd deleted file mode 100644 index a7b55dc..0000000 --- a/vignettes/plot_interactions.Rmd +++ /dev/null @@ -1,68 +0,0 @@ ---- -title: "plotting interaction effects" -output: rmarkdown::html_vignette -vignette: > - %\VignetteIndexEntry{plotting interaction effects} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} ---- - -```{r, include = FALSE} -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>" -) -``` - -```{r setup} -library(modsem) -``` - -# Plotting interaction effects -Interaction effects can be plotted using the included `plot_interaction` function. -This function takes a fitted model object and the names of the two variables -that are interacting. -The function will plot the interaction effect of the two variables. -The x-variable is plotted on the x-axis and the y-variable is plotted on the y-axis. -And the z-variable decides at what points of z the effect of x on y is plotted. -The function will also plot the 95% confidence interval of the interaction effect. - - -Here we can see a simple example using the double centering approach. -```{r} -m1 <- " -# Outer Model - X =~ x1 - X =~ x2 + x3 - Z =~ z1 + z2 + z3 - Y =~ y1 + y2 + y3 - -# Inner model - Y ~ X + Z + X:Z -" -est1 <- modsem(m1, data = oneInt) -plot_interaction("X", "Z", "Y", "X:Z", -3:3, c(-0.2, 0), est1) - -``` -Here we can see a different example using the LMS approach, in the -theory of planned behavior model. - -```{r} -tpb <- " -# Outer Model (Based on Hagger et al., 2007) - ATT =~ att1 + att2 + att3 + att4 + att5 - SN =~ sn1 + sn2 - PBC =~ pbc1 + pbc2 + pbc3 - INT =~ int1 + int2 + int3 - BEH =~ b1 + b2 - -# Inner Model (Based on Steinmetz et al., 2011) - INT ~ ATT + SN + PBC - BEH ~ INT + PBC - BEH ~ PBC:INT -" - -est2 <- modsem(tpb, TPB, method = "lms") -plot_interaction(x = "INT", z = "PBC", y = "BEH", xz = "PBC:INT", - vals_z = c(-0.5, 0.5), model = est2) -``` diff --git a/vignettes/quadratic.Rmd b/vignettes/quadratic.Rmd deleted file mode 100644 index a6aa82d..0000000 --- a/vignettes/quadratic.Rmd +++ /dev/null @@ -1,57 +0,0 @@ ---- -title: "quadratic effects" -output: rmarkdown::html_vignette -vignette: > - %\VignetteIndexEntry{quadratic effects} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} ---- - -```{r, include = FALSE} -EVAL_DEFAULT <- FALSE -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>", - eval = EVAL_DEFAULT -) -``` - -```{r setup} -library(modsem) -``` -In essence quadratic effects are just a special case of interaction effects – where a variable has an interaction effect with itself. Thus, all of the modsem methods can be used to estimate quadratic effects as well. - -Here you can see a very simple example using the LMS-approach. - -```{r} -library(modsem) -m1 <- ' -# Outer Model -X =~ x1 + x2 + x3 -Y =~ y1 + y2 + y3 -Z =~ z1 + z2 + z3 - -# Inner model -Y ~ X + Z + Z:X + X:X -' - -est1Lms <- modsem(m1, data = oneInt, method = "lms") -summary(est1Lms) -``` - -In this example we have a simple model with two quadratic effects and one interaction effect, using the QML- and double centering approach, using the data from a subset of the PISA 2006 data. - -```{r} -m2 <- ' -ENJ =~ enjoy1 + enjoy2 + enjoy3 + enjoy4 + enjoy5 -CAREER =~ career1 + career2 + career3 + career4 -SC =~ academic1 + academic2 + academic3 + academic4 + academic5 + academic6 -CAREER ~ ENJ + SC + ENJ:ENJ + SC:SC + ENJ:SC -' - -est2Dblcent <- modsem(m2, data = jordan) -est2Qml <- modsem(m2, data = jordan, method = "qml") -summary(est2Qml) -``` - -Note: The other approaches work as well, but might be quite slow depending on the number of interaction effects (particularly for the LMS- and constrained approach).