Function to fit GLMs in Mplus — mplusGLM • MplusAutomation

The purpose of this function is to make it (relatively) easy to fit (most) generalized linear models in Mplus. Fitting GLMs in Mplus offers advantages such as using full information maximum likelihood for missing data, robust estimators (default used is MLR), and standard errors adjusted for clustering (planned; not currently available via mplusGLM(). The overarching aim of this function is to make most GLMs as easy to fit in Mplus as they are in R.

mplusGLM(formula, data, idvar = "", ...)

Arguments

formula	An R formula class object as used in `glm()`. Note that currently, only basic formula are accepted. On the fly recoding, arthimetic, and on the fly interactions do not currently work.
data	A dataset.
idvar	Optional. A character string indicating the name of the ID variable. Not currently used but may be used in future.
...	Additional arguments passed to helper functions. For example `.mplusMultinomial()`.

Value

A list of results and Mplus model object.

Details

Note that although there are benefits to fitting GLMs in Mplus. Caution also is warranted. Using full information maximum likelihood for missing data requires a number of assumptions. These may be (badly) violated. mplusGLM() requires the analyst to check these as appropriate.

Currently, mplusGLM() only supports multinomial outcomes. More outcomes are planned in the future including binary, continuous/normal, and count outcomes.

Author

Joshua F. Wiley <jwiley.psych@gmail.com>

Examples

if (FALSE) {
set.seed(1234)
tmpd <- data.frame(
  x1 = rnorm(200),
  x2 = rnorm(200),
  x3 = cut(rnorm(200),
           breaks = c(-Inf, -.7, .7, Inf),
           labels = c("a", "b", "c")))
tmpd$y <- cut(rnorm(200, sd = 2) + tmpd$x1 + tmpd$x2 + I(tmpd$x3 == "b"),
              breaks = c(-Inf, -.5, 1, Inf),
              labels = c("L", "M", "H"))
test <- mplusGLM(y ~ x1 + x2 + x3, data = tmpd)
}