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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,
  variances = "equal",
  covariances = "zero",
  run = TRUE,
  expand_grid = FALSE,
  ...
)

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,

variances

Character vector. Specifies which variance components to estimate. Defaults to "equal" (constrain variances across classes); the other option is "varying" (estimate variances freely across classes). Each element of this vector refers to one of the models you wish to run.

covariances

Character vector. Specifies which covariance components to estimate. Defaults to "zero" (covariances constrained to zero; this corresponds to an assumption of conditional independence of the indicators); other options are "equal" (covariances between items constrained to be equal across classes), and "varying" (free covariances across classes).

run

Logical, whether or not to run the model. If run = TRUE, the function calls mixture_starts and run_mx.

expand_grid

Logical, whether or not to estimate all possible combinations of the variances and covariances arguments. Defaults to FALSE.

...

Additional arguments, passed to functions.

Value

A list of class mixture_list.

Details

The procedure is as follows:

  1. Construct a latent profile model for the continuous indicators using mx_profiles().

  2. Construct a latent class model for the categorical indicators using mx_lca().

  3. Combine the models from steps 1. and 2. into one joint model.

If run = TRUE, simulated annealing is used to estimate the mixture model, as explained in Van Lissa, Garnier-Villareal, & Anadria (2023). However, the inclusion of categorical indicators often leads to a large ordinal error, which automatically initiates a 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(tidySEM)
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
set.seed(1)
res <- mx_mixed_lca(data = df, classes = 2)
}
} # }