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fixed various functions, added tests
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@@ -16,3 +16,4 @@ vignettes/jss.bst | |
| vignettes/jss.cls | ||
| ^\.github$ | ||
| ^codecov\.yml$ | ||
| ^.covrignore | ||
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| @@ -0,0 +1,6 @@ | ||
| R/mssplot-deprecated.R | ||
| R/plot.ssp-deprecated.R | ||
| R/ssp-deprecated.R | ||
| R/ssplot-deprecated.R | ||
| R/ssplotM-deprecated.R | ||
| R/SSplotter-deprecated.R |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,137 @@ | ||
| #' Average Marginal Effects of Covariates of Non-homogenous Hidden Markov Models | ||
| #' | ||
| #' @param model A Hidden Markov Model of class `nhmm` or `mnhmm`. | ||
| #' @param variable Name of the variable of interest. | ||
| #' @param values Vector containing two values for `variable`. | ||
| #' @param newdata Optional data frame which is used for marginalization. | ||
| #' @param nsim Non-negative integer defining the number of samples from the | ||
| #' normal approximation of the model parameters used in | ||
| #' computing the approximate quantiles of the estimates. If `0`, only point | ||
| #' estimates are returned. | ||
| #' @param probs Vector defining the quantiles of interest. Default is | ||
| #' `c(0.025, 0.975)`. | ||
| #' @export | ||
| average_marginal_effect <- function( | ||
| model, variable, values, newdata = NULL, | ||
| nsim = 0, | ||
| probs = c(0.025, 0.975)) { | ||
| stopifnot_( | ||
| checkmate::test_count(nsim), | ||
| "Argument {.arg nsim} should be a single non-negative integer." | ||
| ) | ||
| stopifnot_( | ||
| checkmate::test_string(x = variable), | ||
| "Argument {.arg variable} must be a single character string." | ||
| ) | ||
| stopifnot_( | ||
| length(values) != 2, | ||
| "Argument {.arg values} should contain two values for | ||
| variable {.var variable}.") | ||
| if (is.null(newdata)) { | ||
| time <- model$time_variable | ||
| id <- model$id_variable | ||
| stopifnot_( | ||
| is.data.frame(newdata), | ||
| "Argument {.arg newdata} must be a {.cls data.frame} object." | ||
| ) | ||
| stopifnot_( | ||
| !is.null(newdata[[id]]), | ||
| "Can't find grouping variable {.var {id}} in {.arg newdata}." | ||
| ) | ||
| stopifnot_( | ||
| !is.null(newdata[[time]]), | ||
| "Can't find time index variable {.var {time}} in {.arg newdata}." | ||
| ) | ||
| stopifnot_( | ||
| !is.null(newdata[[variable]]), | ||
| "Can't find time variable {.var {variable}} in {.arg newdata}." | ||
| ) | ||
| } else { | ||
| stopifnot( | ||
| !is.null(object$data), | ||
| "Model does not contain original data and argument {.arg newdata} is | ||
| {.var NULL}." | ||
| ) | ||
| newdata <- model$data | ||
| } | ||
|
|
||
| beta_i_raw <- stan_to_cpp_initial( | ||
| model$estimation_results$parameters$beta_i_raw | ||
| ) | ||
| beta_s_raw <- stan_to_cpp_transition( | ||
| model$estimation_results$parameters$beta_s_raw | ||
| ) | ||
| beta_o_raw <- stan_to_cpp_emission( | ||
| model$estimation_results$parameters$beta_o_raw, | ||
| 1, | ||
| C > 1 | ||
| ) | ||
| newdata[[variable]] <- values[1] | ||
| model <- update(model, newdata = newdata) | ||
| X_initial1 <- t(model$X_initial) | ||
| X_transition1 <- aperm(model$X_transition, c(3, 1, 2)) | ||
| X_emission1 <- aperm(model$X_emission, c(3, 1, 2)) | ||
| newdata[[variable]] <- values[2] | ||
| model <- update(model, newdata = newdata) | ||
| X_initial2 <- t(model$X_initial) | ||
| X_transition2 <- aperm(model$X_transition, c(3, 1, 2)) | ||
| X_emission2 <- aperm(model$X_emission, c(3, 1, 2)) | ||
|
|
||
| ame_pi <- get_pi(beta_i_raw, X_initial1, 0) - | ||
| get_pi(beta_i_raw, X_initial2, 0) | ||
| ame_A <- get_A(beta_s_raw, X_transition1, 0) - | ||
| get_A(beta_s_raw, X_transition2, 0) | ||
| ame_B <- if (model$n_channels == 1) { | ||
| get_B(beta_o_raw, X_emission1, 0) - get_B(beta_o_raw, X_emission2, 0) | ||
| } else { | ||
| get_multichannel_B(beta_o_raw, X_emission1, S, C, M, 0) - | ||
| get_multichannel_B(beta_o_raw, X_emission2, S, C, M, 0) | ||
| } | ||
| browser() | ||
| if (nsim > 0) { | ||
| stopifnot_( | ||
| checkmate::test_numeric( | ||
| x = probs, lower = 0, upper = 1, any.missing = FALSE, min.len = 1L | ||
| ), | ||
| "Argument {.arg probs} must be a {.cls numeric} vector with values | ||
| between 0 and 1." | ||
| ) | ||
| chol_precision <- chol(-model$estimation$hessian) | ||
| U <- backsolve(chol_precision, diag(ncol(chol_precision))) | ||
| x <- matrix(rnorm(nsim * ncol(U)), nrow = nsim) %*% U | ||
| x <- t(sweep(x, 2, c(beta_i_raw, beta_s_raw, beta_o_raw), "+")) | ||
| p_i <- length(beta_i_raw) | ||
| p_s <- length(beta_s_raw) | ||
| p_o <- length(beta_o_raw) | ||
|
|
||
| pi_samples <- apply( | ||
| x[seq_len(p_i), ], 2, function(z) { | ||
| z <- array(z, dim = dim(beta_i_raw)) | ||
| z <- stan_to_cpp_initial(z) | ||
| get_pi(z, X_initial1) - get_pi(z, X_initial2) | ||
| } | ||
| ) | ||
| A_samples <- apply( | ||
| x[p_i + seq_len(p_s), ], 2, function(z) { | ||
| z <- array(z, dim = dim(beta_s_raw)) | ||
| z <- stan_to_cpp_transition(z) | ||
| unlist(get_A(z, X_transition1)) - | ||
| unlist(get_A(z, X_transition2)) | ||
| } | ||
| ) | ||
| B_samples <- apply( | ||
| x[p_i + p_s + seq_len(p_o), ], 2, function(z) { | ||
| z <- array(z, dim = dim(beta_o_raw)) | ||
| z <- stan_to_cpp_emission(z, 1, model$n_channels > 1) | ||
| unlist(get_B(aperm(z, c(2, 3, 1)), X_emission1)) - | ||
| unlist(get_B(aperm(z, c(2, 3, 1)), X_emission2)) | ||
| } | ||
| ) | ||
|
|
||
| quantiles <- fast_quantiles(samples, probs) | ||
| for(i in seq_along(probs)) { | ||
| transition_probs[paste0("q", 100 * probs[i])] <- quantiles[, i] | ||
| } | ||
| } | ||
| class(out) <- "ame" | ||
| } |
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