I am working on a GMM where I would like to include time-varying moderators. Has anyone attempted this? If so, what does your syntax look like? I keep running into all kinds of errors and can't seem to find a lot our there on this issue.
The estimates for the mean intercept and slope fro each class are: class1 i*-.505 s*-0.058; class2 i*-.407 s*0.041. The estimates of the regression part of mrj15 ON pst15,Ö., mrj20 ON pst20 are -.94, -.48, -.66, -.89, -.95, and -.98, respectively.
I have two questions: 1) How can I plot (by hand) these two classes and taking into account the estimates from the time-varying covariates, or the PLOT3 option can do it for me? 2) Probably this is related to the first one. How can I put in specific values for the time-varying covariate and estimate the outcome from this model. Can Mplus do it?
This is a further explanation from the former question. The data set that I have is longitudinal with a cohort sequential design with 7 time points, reflecting subjectís ages from 15 to 20 years old. I have self reported substance use measures from subjects as outcome so that I believe the zero inflated passion (ZIP) is best option for this analysis. We are looking for mixtures, first by fixing intercepts and slopes to zero (a second step will be taking this solution and continue with intercepts and slopes random) , with time varying covariates (TVC). I was able to run this model but when I request TECH7 option Iíve get all classes with zero weighted means in all classes what is the program doing? Would you please advice.
I don't think Mplus provides this plot because of the combination of numerical integration and x's influencing the u's. Unfortunately, it is tricky to do this yourself because with non-continuous outcomes and random effects, you have to use numerical integration to get the estimated outcome probabilities. We have this on our list to add for future plot extensions.
I have a question about GMM. I want to add a time-varying factor, because the subjects were measured at different times in their life. I also want to abstract classes from the data. Is this possible? Because when I try to put time-varying variables (the different time points) in the model, Mplus says I must say TYPE = RANDOM. And for the mixture analyses with classes i must say TYPE = MIXTURE. Is this not possible? Thanks in advance, Nanda
There is no overall model fit statistic when means, variances, and covariances are not sufficient statistics for model estimation. You can compare nested models using -2 times the loglikelihood difference which is distributed as chi-square.