tidySEM (version 0.2.10)

mx_mixture: Estimate mixture models using OpenMx

Description

Dynamically creates a batch of mixture models, with intelligent defaults. See Details for more information.

Usage

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

Value

Returns an OpenMx::mxModel().

Arguments

model

Syntax for the model; either a character string, or a list of character strings, or a list of mxModel objects. See Details.

classes

A vector of integers, indicating which class solutions to generate. Defaults to 1L. E.g., classes = 1:6, classes = c(1:4, 6:8).

data

The data.frame to be used for model fitting.

run

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

...

Additional arguments, passed to functions.

Details

Model syntax can be specified in three ways, for ease of use and flexibility:

1. An atomic character string with lavaan syntax. Within this syntax, the character string {C} is dynamically substituted with the correct class number using lsub, for example to set unique parameter labels for each class, or to specify equality constraints. E.g., x ~ m{C}*1 will be expanded to x ~ m1*1 and x ~ m2*1 when classes = 2. The resulting syntax for each class will be converted to an mxModel using as_ram.

2. A list of character strings with lavaan syntax. Each item of the list will be converted to a class-specific mxModel using as_ram.

3. A list of mxModel objects, specified by the user.

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. tools:::Rd_expr_doi("10.1080/10705511.2023.2250920")

Examples

if (FALSE) {
# Example 1: Dynamic model generation using {C}
df <- iris[, 1, drop = FALSE]
names(df) <- "x"
mx_mixture(model = "x ~ m{C}*1
                    x ~~ v{C}*x", classes = 1, data = df)
# Example 2: Manually specified class-specific models
df <- iris[1:2]
names(df) <- c("x", "y")
mx_mixture(model = list("y ~ a*x",
                        "y ~ b*x"),
                        meanstructure = TRUE,
                        data = df) -> res

# Example 3: Latent growth model
df <- empathy[1:6]
mx_mixture(model = "i =~ 1*ec1 + 1*ec2 + 1*ec3 +1*ec4 +1*ec5 +1*ec6
                    s =~ 0*ec1 + 1*ec2 + 2*ec3 +3*ec4 +4*ec5 +5*ec6",
                    classes = 2,
                    data = df) -> res
}