Hi Statalists,

I was trying to do a two-part modeling for my dataset, in which observed vars are zero-inflated. I followed the steps in this publication: Kim (2009) Two-Part Factor Mixture Modeling: Application to an Aggressive Behavior Measurement Instrument. Basically, here's what I did:

1) Step 1: based on my raw data, I created dichotomous vars: v1_d, v2_d, v3_d; and then I created continuous vars, wherein 0s are treated missing, so I have v1_c, v2_c, v3_c

2) Step 2: EFA and LCA for dichotomous data

3) Step 3: EFA and LPA for continuous data

4) Step 4: EFA for the combination of dichotomous and continuous data identified the best two-part model with 2 factors from dichotomous data, and 1 factor from continuous data, and here is my codes:

Code:
gsem ( F1 -> v1_d v2_d v3_d, logit) ( F2 ->  v4_d v5_d v6_d, logit) ( F3 -> v1_c v2_c v3_c v4_c v5_c v6_c) ///
, nocapslatent nonrtolerance  latent(F1 F2 F3) cov(F1*F2 F1*F3 F3*F2) difficult
5) Step 5: as suggested in the publication, "the latent classes can be added to the two-part model (i.e. the model I identified in step 4) and correlated if necessary". However, I am not quite sure how to do that in Stata codes. Is it possible to identify separate classes from different factors in the SAME model? If so, how should I write the Stata codes? Any though/comments would be much appreciated!

Thank-you in advance,
Yingyi