Hi Steve, Thanks for always coming through! You were right on the money on PROC SORT. I do have the same estimates now. The initial code generated the Fit Statistics below. My understanding is that the scale parameter (23.5181) is an inverse of the variance of Incx, the response variable in this case. If that is correct, the variance would be: Var = Exp(23.1581)/(1+Exp(23.1581) =1? I am sure I missed something here because this does not seem correct, does it?. Fit Stats If I were to use {Mean*(1-mean)/(1+Scale)}, since there is no mean in the output, I coded mean in the following fashion: Proc glimmix data = DAL96 method = quadrature plots=all; class Loc Rep Cultivar TRT; model Incx = TRT /solution d=beta link=logit; random intercept / subject=Rep(Loc); output out=overdisp2 pearson=pearson; run; PROC MEANS DATA=overdisp2 mean var; var pearson; run; The mean= -0.0197244 and variance = 0.8623935. Somehow, I am not happy with the negative mean unless it is supposed to be exp(-0.0197244)=0.98 and variance becomes 2.37? What are your thoughts: Any ideas on a better code for this and for predicted estimates? Thanks a mill once again, DNY
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