latent class model with predicted probabilities?

[Joseph Gerteis]

I have a question that I can't find the answer to. I have a latent class model with covariates, essentially a "1-step" model that approximates a multinomial logistic regression with the latent classes as dependent variable categories. So far so good.

gsem (inputvar1 inputvar2 inputvar3 inputvar4 inputvar5 <- ) ///
(C <- income age male white, logit) ///
[pweight=weightvar], logit startvalues(randomid, draws(8) seed(54321)) em(iter(5)) lclass(C 4)

What I would like to be able to do is then use post estimation commands to generate predicted probabilities for some specific categories of variables. If I was doing a multinomial logistic regression, I would do this with Long and Freese's mtable command as below, or simply with margins. In the present case I would like to be able to do something similar for predicted probabilities of class membership as in the model.

mtable, at (male(1 0) white(1 0)) atmeans ci

Is it possible to do something like this for the model here? If so, does anyone know how? I can't figure it out. (The margins command does run after the model, but returns probabilities for each of the input variables, not for the latent classes).

[Joseph Gerteis]

Sorry, I should have added: Stata/IC 15.1 on mac. So (as above) latent class analysis within gsem

[Jeff Pitblado (StataCorp)]

Joseph can compute marginal predictions for the latent class
probabilities with the predict() option of margins.

The list of suboptions allowed within option predict() of
margins for gsem estimation results is documented
here.

predict after gsem has an option named classpr for
predicting class probabilities. Use option class() to specify
which latent class to compute the probabilities.

Given Joseph's example above, the call to margins would be
something like

margins, predict(classpr class(1))     ///
         predict(classpr class(2))     ///
         predict(classpr class(3))     ///
         predict(classpr class(4))     ///
         at(male=(1 0) white=(1 0))    ///
         atmeans

If Joseph changed the model specification to use factor variables
notation on the variables male and white, i.e.
i.male and i.white, then the call to margins could
be changed to

margins male#white,                    ///
         predict(classpr class(1))     ///
         predict(classpr class(2))     /// 
         predict(classpr class(3))     ///
         predict(classpr class(4))     ///
         atmeans

and the output might look a little better.

I've also attached an example using simulated data.

[Joseph Gerteis]

Thank you for this! I tried a few predict commands but was missing the way to do it.