Hi, I am working with a data, which is at customer x week x tier level and has other features- P1, P2, P3 and Sales. Treating Sales as a dependent variable and P1, P2 and P3 as independent variables I am planning to run a Bayesian regression model. Additionally, I would like to randomize this model at tier level(there are 4 tiers in the data). For e.g- if Model is Sales = B0+ B1*P1+ B2*P2+B3*P3, I would expect 4 intercepts B0 for each tier, 4 slopes B1 for each tier and so on. I explored GENMOD, but it seems GENMOD does not support randomization. Next, I tested BGLIMM which seems to have worked best in my case but my current SAS version does not support BGLIMM. Lastly, I am left with only PROC MCMC. I have used below codes: proc mcmc data=baymdb outpost=PostOut_re nmc=100 thin=5; ods select Parameters REparameters PostSumInt tracepanel; parms B0-B3 S2 ; parms S2g 1; prior B: ~ normal(0, var=1e6); prior S2 ~ igamma(0.01, scale = 0.01); prior S2g ~ general(0, lower=0); random theta ~ normal(0,var=S2g) subject=tier ; Mu = B0 + B1*P1 + B2*P2 +B3*P3 + theta ; model LN_AHC_ENTNRX_ONE ~ normal(Mu,var=S2); run; However, the output of the above model does not have intercepts and estimates at tier level. I am seeing only overall level B0, B1, B2, B3. Is there anything I am missing or if there is any other procedure which I could use? Thanks in advance for your help.
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