I was able to fit a semi-parametric Bayesian GLMM model using DPpackage. It took me many hours to sample from the posterior distribution (DPM prior):
I compared the results from DPpackage and that from MCMCglmm, and they are not that different, and the latter took only a small fraction of the time required by the former!
The lack of difference in results puzzled me. I compared from results from random effect logistic regression assuming Gaussian random effect and results from NPML, assuming a nonparametric distribution of the random effect, the differences are quite significant.
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Using DP prior instead of DPM prior, it took about 4.7 hours to run the model. The results are slightly different and the parameter I am interested in increased from .41 to .42. Now I am trying PT prior and see how it goes.
DPpackage is a exciting new tool for applied researchers, and A LOT OF new and cool things can be done with it. With convenient new Bayesian tools like MCMCpack, MCMCglmm, and DPpackage, I will not be surprised to see more Bayesian publications coming out in social sciences.