I have run a Bayesian GLMM model in SAS (proc MCMC) and have aquired the posterior means and 95% Highest Posterior Density (HPD) intervals. I'm looking to validate this in R but I am struggling to get my results to match (datasets are consistent with each other). I have created models in R using 'brm' and 'jags.model' and then used 'MCMCsummary' to get the HPD intervals, but I just can't get my SAS and R results to match.
How different are the results when you run SAS proc MCMC and compare to another run of proc MCMC?
Are you controlling with a seed to ensure no variation ?
if so , try different seeds and first get a feeling for the variance introduced by the pure random aspects of what you are doing. This has to be a first step before comparing output to any other system or approach.
Great question. I am using the same seed in both SAS and R, but I shall do as you suggest. I had assumed that using the same seed would have ensured that both sets of results would have at least remained similar...