I want to use PROC MCMC to run a random-effects linear negative binomial (NB1) model and estimate ~200,000 random intercepts (as an alternative to NLMIXED). Since the OUTPOST= dataset can only save up to 32,767 variables, what approaches can I take to be able to output the posterior samples for all of the ~200K random-effects parameters?
An easy way is to assume the posterior on each effect is Gaussian:
1) Monitor the posterior distributions of the random effects.
2) Write ODS output postsumint=Px;
3) In a subsequent datastep, sample from the mean and Standard deviation given in Px (or just use the posterior info in Px.)
Let me know if this isn't what you are thinking...
G
April 27 – 30 | Gaylord Texan | Grapevine, Texas
Registration is open
Walk in ready to learn. Walk out ready to deliver. This is the data and AI conference you can't afford to miss. Register now and lock in 2025 pricing—just $495!
ANOVA, or Analysis Of Variance, is used to compare the averages or means of two or more populations to better understand how they differ. Watch this tutorial for more.