I just saw this question and thought the following might help. If you would like to fit the model without an R side matrix (without the random _residual_ statement in your code) which will lead to a random coefficient model with a G side matrix, I think the non-convergence problem might be resolved using NLOPTIONS. With the given data example above, the following code leads to convergence with four iterations. However, the problem of nonconvergence with very large datasets still remains to be a problem. You have to of course check if that is what you want. proc glimmix data=subtype; class center; model y = height / dist=bin link=logit solution; random intercept height / type =un subject= center; nloptions tech=NRRIDG; run; ... View more