I am attempting Bayes H0 imputation of a longitudinal CFA model involving latent (categorical indicators) and observed variables (also categorical) at three time points. I am using type=complex to account for clustering. For the latent factor indicators, I am imposing cross-time constrains on loadings and thresholds.
When I specify correlations between one of the latent variables and an observed categorical variable, the analysis will not run unless I use algorithm=gibbs(rw). However, the model seems to be more susceptible to convergence problems with gibbs(rw), and it is quite slow.
Do you have suggestions for diagnosing convergence problems with the gibbs(rw) algorithm? I have increased iterations without success. There are factor indicators with low endorsement proportions.
Thank you for your response Linda. I assumed that type=complex was functional because the analysis proceeded and the cluster variable was reported in the analysis summary in the output.
I am correlating the latent variable and the observed categorical variables to represent within-time correlations in a panel model. The latent and observed variables reflect separate constructs measured at three time points. So there will be autoregressive paths for each construct across time, predictive regressions among them across time, and within-time correlations among them. Is this approach allowed in Mplus?