PROC NLMIXED cannot model correlated errors. It assumes the errors are independent. ... View more

I posted a response yesterday. Not sure why it did not show up. Here it is again --   method=laplace is a maximum likelihood estimation method that honors the distribution assumption.  method=rspl is a pesudo-likelihood estimation method that creates a linearized pseudo-response. Both methods have pros and cons. For a binary response variable, a maximum likelihood based estimation method might be less biased. For more information please refer to the documentation below -- https://go.documentation.sas.com/doc/en/pgmsascdc/v_040/statug/statug_glimmix_details06.htm  It is okay to use dist=binary or dist=binomial in your case. If your response variable is events/trials, then you must usewe use dist=binomial (which is also the default).   lsmeans are computed across the average of other covariates in your model. You can add the E option in the LSMEANS statement to see exactly how it is computed.  By default, LSMEANS are computed over a balanced population. So each level in the group receives the same "fractions" regardless of the sample size in the group. the BYLEVEL option changes the "fractions" based on the group sample size. Again, you can add the E option in the LSMENAS statement to see exactly how that affects the computation of the LSMEANS for your data. Hope this helps, Jill   ... View more

method=laplace is a maximum likelihood based estimation method, which honors the specified distribution. method=rspl ns a pesudo-likelihood estimation method.  Both have pros and cons. See the documentation for details. https://go.documentation.sas.com/doc/en/pgmsascdc/v_040/statug/statug_glimmix_details06.htm Yes. Using either dist=binary or dist=binomial is fine. If you have event/trials syntax as the dependent variable, you would use dist=binomial (default). LSMEANS for A is computed averaged across other covariates in the model. You can add the E option in the LSMEANS statement to see exactly how it is computed. By default LSMEANS are computed over a balanced population, that is, each level receives the same fraction regardless of the sample size in your data. The BYLEVEL option would base the "fraction" on the group sample size in your data. Again, the E option would tell you exactly how that affects the LSMEANS computations. BYLEVEL is explained in the documentation below -https://go.documentation.sas.com/doc/en/pgmsascdc/v_040/statug/statug_glimmix_syntax13.htm#statug.glimmix.gmxlsmbylevel Hope this helps, Jill ... View more