Hi RStudio Community,
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.
Any pointers would be much appreciated. TIA.
how different are they ?
I wouldn't say they're hugely different but large enough for me not to consider the results validated.
I just wondered if anyone was aware of any examples using both SAS and R and provided the same (or near enough) the same results?
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...
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