paramExtract: Extract parameters from a data frame of Mplus estimates

Description

This is a simple convenience function designed to facilitate looking at specific parameter types by easily return a subset of a data frame with those types only. It is designed to follow up the results returned from the readModels function.

Usage

paramExtract(
  x,
  params = c("regression", "loading", "undirected", "expectation", "variability", "new")
)

Value

A subset data frame with the parameters of interest.

Arguments

x: A data frame (specifically the type returned by readModels) containing parameters. Should be specific such as unstandardized and the data frame must have a column called ‘paramHeader’.
params: A character string indicating the types of parameters to be returned. Options currently include ‘regression’, ‘loading’, ‘undirected’, ‘expectation’, ‘variability’, and ‘new’ for new/additional parameters. Regressions include regression of one variable ON another. ‘loading’ include indicator variables (which are assumed caused by the underlying latent variable) and variables in latent growth models (BY or |). Undirected paths currently only include covariances, indicated by the WITH syntax in Mplus. Expectation paths are the unconditional or conditional expectations of variables. In other words those parameters related to the first moments. For independent variables, these are the means, \(E(X)\) and the conditional means or intercepts, \(E(X | f(\theta))\) where \(f(\theta)\) is the model, some function of the parameters, \(\theta\). Finally ‘variability’ refers to both variances and residual variances, corresponding to the second moments. As with the expectations, variances are unconditional for variables that are not predicted or conditioned on any other variable in the model whereas residual variances are conditional on the model. Note that R uses fuzzy matching so that each of these can be called via shorthand, ‘r’, ‘l’, ‘u’, ‘e’, and ‘v’.

Author

Joshua F. Wiley <jwiley.psych@gmail.com>

Examples

Run this code

if (FALSE) {
  test <- mplusObject(
    TITLE = "test the MplusAutomation Package and my Wrapper;",
    MODEL = "
      mpg ON wt hp;
      wt WITH hp;",
    usevariables = c("mpg", "wt", "hp"),
    rdata = mtcars)

  res <- mplusModeler(test, "mtcars.dat", modelout = "model1.inp", run = 1L)

  # store just the unstandardized parameters in 'd'
  d <- res$results$parameters$unstandardized
  # extract just regression parameters
  paramExtract(d, "regression")
  # extract other types of parameters using shorthand
  paramExtract(d, "u")
  paramExtract(d, "e")
  paramExtract(d, "v")
}