model y ~ general(weightfunc*LL);
run;
I would note that the REPLICATE statement in NLMIXED is very easy to abuse. If you code
REPLICATE M;
then you are indicating that there are M SUBJECTS who have the same identical values of the predictors and the response. As the SAS documentation states, this sort of condition might apply when you have a binomial response modeled as:
P(Nsuccess{i} | Ntot{i}, p=f(x1{i}, x2{i}, ..., x{i}, gamma{i}) )
where Nsuccess{i} and Ntot{i} are the number of successes and total number of trials, x{i} are values for a set of predictor variables, and gamma{i} is a random effect. All of these are specific to the i-th subject. If you have M subjects that share the same values for the number of successes, total number of trials, and predictor variables, then all of those subjects will be assigned the same random effect. You can drop all but one of the subjects from the analysis in order to improve computational efficiency.
This is a much different model than one would employ for fitting a weighted analysis. The weighted analysis gives different importance to certain contributions to the total log-likelihood. I believe it is the weighted analysis employing user-specified log-likelihood which you wish to employ.
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