Clean up some formatting, and rewrite parts of the README and methods…

… vignette
Kss2k · Oct 24, 2024 · b88a0ad · b88a0ad
1 parent 11c786c
commit b88a0ad
Show file tree

Hide file tree

Showing 3 changed files with 53 additions and 42 deletions.
diff --git a/R/constraints_pi_ca.R b/R/constraints_pi_ca.R
@@ -1,5 +1,4 @@
 # Functitions for specifying constraints in the constrained approach
-# modsem(method = "ca"). Last updated: 29.05.2024
 
 
 labelFactorLoadings <- function(parTable) {
@@ -14,14 +13,17 @@ labelFactorLoadings <- function(parTable) {
 
 specifyFactorLoadingsSingle <- function(parTable, relDf) {
   latentProdName <- stringr::str_c(rownames(relDf), collapse = "")
+
   for (indProd in colnames(relDf)) {
-    indProdLabel <- createLabelLambda(indProd, latentProdName)
+    indProdLabel     <- createLabelLambda(indProd, latentProdName)
     indsInProdLabels <- createLabelLambda(relDf[[indProd]], rownames(relDf))
-    vecLhsRhs <- c(indProdLabel, stringr::str_c(indsInProdLabels,
-                                                collapse = " * "))
-    newRow <- createParTableRow(vecLhsRhs, op = "==")
+    vecLhsRhs        <- c(indProdLabel, stringr::str_c(indsInProdLabels,
+                                                       collapse = " * "))
+
+    newRow   <- createParTableRow(vecLhsRhs, op = "==")
     parTable <- rbind(parTable, newRow)
   }
+
   parTable
 }
 
@@ -129,10 +131,9 @@ 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 ",
+  stopif(nrow(relDf) > 2, "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 = "")
@@ -144,12 +145,11 @@ specifyVarCovSingle <- function(parTable, relDf) {
                               FUN = function(x) trace_path(parTable, x, x))
 
   labelCovElems <- trace_path(parTable, elemsInProdTerm[[1]],
-                             elemsInProdTerm[[2]]) |> paste0(" ^ 2")
+                              elemsInProdTerm[[2]]) |> paste0(" ^ 2")
 
   lhs <- labelLatentProd
   rhs <- paste(stringr::str_c(labelsElemsInProd, collapse = " * "),
-               labelCovElems,
-               sep = " + ")
+               labelCovElems, sep = " + ")
   varLatentProd <- createParTableRow(c(lhs, rhs), op = "==")
 
   # covariances between elems and latents
@@ -170,30 +170,26 @@ specifyVarCovSingle <- function(parTable, relDf) {
     labelVarIndProd <- createLabelVar(indProd)
 
     labelsFactorLoadings <- vector("character", length = nrow(relDf))
-    labelsVarLatents <- vector("character", length = nrow(relDf))
-    labelsVarInds <- vector("character", length = nrow(relDf))
+    labelsVarLatents     <- vector("character", length = nrow(relDf))
+    labelsVarInds        <- vector("character", length = nrow(relDf))
 
-    for (latent in 1:nrow(relDf)) {
+    for (latent in seq_len(nrow(relDf))) {
       labelsFactorLoadings[[latent]] <- 
         createLabelLambdaSquared(relDf[latent, indProd], 
                                  rownames(relDf)[[latent]])
       labelsVarLatents[[latent]] <- 
         trace_path(parTable, rownames(relDf)[[latent]],
-                  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 = " * ")
+    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 = " + ")
+    rhs  <- paste(rhs1, rhs2, rhs3, sep = " + ")
 
     constrained.varProdInds[[indProd]] <- createParTableRow(c(lhs, rhs), op = "==")
   }

diff --git a/README.md b/README.md
@@ -5,8 +5,8 @@
 [![CRAN](https://www.r-pkg.org/badges/version/modsem)](https://cran.r-project.org/package=modsem)
 [![PKGDOWN-Build](https://github.com/kss2k/modsem/actions/workflows/pkgdown.yml/badge.svg)](https://github.com/kss2k/modsem/actions/workflows/pkgdown.yml)
 <!-- badges: end -->
-This is a package which allows you to perform interactions between latent variables (i.e., moderation) in CB-SEM. 
-See https://www.modsem.org for a tutorial.
+`modsem` is an `R`-package for estimating interaction (i.e., moderation) effects between latent variables
+in structural equation models (SEMs). See https://www.modsem.org for a tutorial.
 
 # To Install 
 ```
@@ -20,8 +20,12 @@ 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.
+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.
+Different approaches can be categorized into two groups: 
+Product Indicator (PI) and Distribution Analytic (DA) approaches.
 
+## Product Indicator (PI) Approaches:
 - `"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.
@@ -31,6 +35,8 @@ There are a number of approaches for estimating interaction effects in SEM. In `
 - `"dblcent"` = double centering approach (Marsh., 2013)
   - default 
 - `"pind"` = basic product indicator approach (not recommended)
+
+## Distribution Analytic (DA) Approaches
 - `"lms"` = The Latent Moderated Structural equations (LMS) approach, see the [vignette](https://kss2k.github.io/modsem/articles/lms_qml.html)
 - `"qml"` = The Quasi Maximum Likelihood (QML) approach, see the [vignette](https://kss2k.github.io/modsem/articles/lms_qml.html)
 - `"mplus"` 

diff --git a/vignettes/methods.Rmd b/vignettes/methods.Rmd
@@ -19,21 +19,27 @@ knitr::opts_chunk$set(
 ```{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.
+Different approaches can be categorized into two groups: 
+Product Indicator (PI) and Distribution Analytic (DA) 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)
--   `"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
+## Product Indicator (PI) Approaches:
+- `"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)
+
+## Distribution Analytic (DA) Approaches
+- `"lms"` = The Latent Moderated Structural equations (LMS) approach, see the [vignette](https://kss2k.github.io/modsem/articles/lms_qml.html)
+- `"qml"` = The Quasi Maximum Likelihood (QML) approach, see the [vignette](https://kss2k.github.io/modsem/articles/lms_qml.html)
+- `"mplus"` 
+  - estimates model through Mplus, if it is installed
 
 ```{r, eval = FALSE}
 
@@ -47,10 +53,13 @@ Z =~ z1 + z2 + z3
 Y ~ X + Z + X:Z 
 '
 
+# Product Indicator Approaches
 modsem(m1, data = oneInt, method = "ca")
 modsem(m1, data = oneInt, method = "uca")
 modsem(m1, data = oneInt, method = "rca")
 modsem(m1, data = oneInt, method = "dblcent")
+
+# Distribution Analytic Approaches
 modsem(m1, data = oneInt, method = "mplus")
 modsem(m1, data = oneInt, method = "lms")
 modsem(m1, data = oneInt, method = "qml")