Estimate latent profile analyses using OpenMx

This function is a wrapper around mx_mixture to simplify the specification of latent profile models, also known as finite mixture models. By default, the function estimates free means for all observed variables across classes.

mx_profiles(
  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

Returns an mxModel.

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) {
data("empathy")
df <- empathy[1:6]
mx_profiles(data = df,
            classes = 2) -> res
}