The dependent variable, Y, is a lab value, LID is a laboratory ID, SampleID is specimen ID. A set of identical samples are sent to each lab for analysis. I am using bglimm to calculate a Bayesian intraclass correlation with HPDR for the ICC. That is working fine and the problem description is just for background. My question is about the outpost data set. Here is the code. proc bglimm data=CPT2 seed= 202011 nthreads= -1 nbi=1000 nmc= 5000 outpost= YPost; /* stats= all plots= (trace autocorr density) diagnostics=all ; */ class SampleID LID; model Y = / dist=normal; random SampleID/ covprior=uniform(lower=0,upper=10) nuts ; random LID/ covprior=uniform(lower=0,upper=10) nuts; run; The outpost dataset, named YPost has everything we expect -- posterior values for each parameter for each iteration, but also has six additional columns named Y_82, Y_98, Y_118, Y_138, Y_149, &_158. The analysis is working ok, I am getting what I need out of it, but I would like to understand what these added columns are.
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