This is far from a solution.  I count 15 classification variables (I don't know how many levels for each, and some of which do not appear in the model statement, e.g. s1_12), and 8 continuous covariates (age, famsize, s1_13, tot_rm, rm1_11, rm1_12, rm1_nets, and age*sex).  Some of these look like classification variables as well, but you may well wish to treat them as continuous.  So, I wonder about two things--complete collinearity (or separation) between some of the continuous covariates, and whether there is enough data to estimate everything.  Given that these are not problems, what happens if you delete the INITGLM option?  You may still get mivque(0) problems, but it is a cheap try.  I would also try dropping the ddfm=betwithin, as I don't see any within subject variability being modeled.  As a last resort, try running this in PROC MIXED and save the covariance parameters to a data set that you call as starting values with a PARMS statement. ... View more