Hi, all.

I am working with a dataset that describes diabetes status (0 or 1) for individual residents of multiple states, across multiple years. I'm interested in obtaining age/sex/race adjusted estimates of prevalence for state/year combinations that have some shrinkage to account for small sample size, so I've specified a crossed effects model:

Code:
melogit diabetes age sex race || _all: R.year || _state:

predict re*, reffects
While the model ran successfully, I was not able to obtain predictions in a reasonable amount of run time. I subsequently came across some info suggesting fully Bayesian crossed effects models are more tractable, so I specified the following:

Code:
 bayes, saving(output/bayes, replace): melogit diabetes age sex race || _all: R.year || _state:
This model has run, but I am not really understanding how to obtain estimates of the random effects for state and year. They do not seem to be in the output from -bayesstats- or in the output file.

Thanks for any suggestions!