I am running an LCA with 19 binary predictors and an N of 223. The best fitting model has two classes (entropy = .87). I have regressed this model on three covariate predictors. I am fine with and, in fact, want the predictors to influence class membership.
Model: %Overall% c#1 on catag2 k6cat aids_eve;
1) These predictors reduce the N to 189 so I have rerun the model using multiple imputation. Using MI, I can't get the conditional probabilities of the indicators, only thresholds. Is there a way to get the results in probability scale?
2) I have two binary outcomes included using the auxiliary statement. Mplus won't run these using DCAT, which I use because I want odds ratios for the distal variables. What other option works?
3) I want to regress the distal outcomes the covariates to obtain direct effects (as well as the indirect effects through latent class), how is this specified in the model statement?
Once the model is running on non-imputed data, can I run the exact same model using imputed data? I presently get this error message for the imputed data:
Auxiliary variables with E, R, DU3STEP, DE3STEP, or BCH are not available for TYPE=IMPUTATION.
With respect to number 3, perhaps I was unclear...
In the current statements I have
usevar ind1-ind19 age aids; auxiliary (e) hospital er;
Then in the model statement, as indicated, I have:
%Overall% c#1 on age aids;
If I then add to that statement (as I interpreted from your suggestion):
hospital on age aids;
I get this error message:
*** ERROR in MODEL command Unknown variable(s) in an ON statement: HOSPITAL
If I try to include hospital on the usevar AND auxiliary command, I get the same error message.
In other words, it seems to be telling me I can't have a variable that is defined as a distal outcome on the auxiliary command and also include that variable in the model as a DV regressed on one (or more) of the covariate predictors of latent class.