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Estimate mixed data latent class analysis using OpenMx

Source: R/mixture_mixed-data.R
mx_mixed_lca.Rd

This function simplifies the specification of latent class models with mixed data types: models that estimate membership of a categorical latent variable based on binary/ordinal and continuous indicators. See Details for more information.

Usage

mx_mixed_lca(data = NULL, classes = 1L, run = TRUE, ...)

Arguments

data

The data.frame to be used for model fitting.

classes

A vector of integers, indicating which class solutions to generate. Defaults to 1L. E.g., classes = 1:6,

run

Logical, whether or not to run the model. This should usually be set to TRUE, because this function runs several models to aid in specifying good starting values; see Details.

...

Additional arguments, passed to functions.

Value

Returns an OpenMx::mxModel().

Details

The procedure is as follows:

  1. Estimate a latent profile analysis for the continuous indicators using mx_profiles(). Additional arguments, like variabces = "free", can be passed via .... The estimator uses simulated annealing.

  2. To obtain good starting values for the categorical indicators, use the classes from step 1. to estimate an auxiliary model for the ordinal indicators with BCH().

  3. Estimate a latent class analysis for the categorical indicators using mx_lca(), with the results of step 2. as starting values. The estimator uses OpenMx::mxTryHardOrdinal().

  4. Combine the models from steps 1. and 3. into one joint model. Conduct one final optimization step using OpenMx::mxTryHardOrdinal().

References

Van Lissa, C. J., Garnier-Villarreal, M., & Anadria, D. (2023). Recommended Practices in Latent Class Analysis using the Open-Source R-Package tidySEM. Structural Equation Modeling. doi:10.1080/10705511.2023.2250920

Examples

if (FALSE) { # \dontrun{
if(isTRUE(requireNamespace("OpenMx", quietly = TRUE))) {
library(OpenMx)
# Construct dataset with ordinal and categorical indicators
set.seed(1)
n = 200
mns <- c(rep(0, floor(.3*n)), rep(2, ceiling(.7*n)))
df <- rnorm(4*n, mean = rep(mns, 4))
df <- matrix(df, nrow = n)
df <- t(t(df) * c(1, 2, .5, 1))
df <- data.frame(df)
df$X4 <- cut(df$X4, 3, labels = FALSE)
df$X4 <- OpenMx::mxFactor(df$X4, levels = c(1:3))
# Estimate the model
res <- mx_mixed_lca(data = df, classes = 2)
}
} # }