mx_profiles {tidySEM}

Estimate latent profile analyses using OpenMx

Description

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.

Usage

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

## Not run: 
data("empathy")
df <- empathy[1:6]
mx_profiles(data = df,
            classes = 2) -> res

## End(Not run)