Hi all,
I am conducting the literature review and would like to fit GMMs using OpenMx. I have several questions. Firstly, after looking into R scripts from previous topics related to GMMs model, I think the first step for the mixture model is to estimate mixing components and parameters of each component. Then at the second step, we calculate posterior probabilities for each observation. Is it right? If so, may I know the algorithm to get the FIML? I mean almost all publications I read employed EM algorithm to obtain parameters and posterior probabilities simultaneously; if OpenMx estimates them separately, does it also utilize EM or another algorithm?
Secondly, some articles mentioned "switching labels" issue for latent class analysis. Though as my understanding, OpenMx does not have this issue by allowing us to construct a labeled submodel for each latent class, I am still wondering if any available approach can be used to prove it. Any advice would be appreciated.
Thanks in advance!