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.
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().
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) { # \dontrun{
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)
} # }