Hello SAS Community, I am new to PROC GLIMMIX and I am struggling with an output interpretation that I assumed would be straightforward. I have a dataset that is looking at reproductive output of bee colonies (RS). In my model, I have a treatment effect (trt) and two continuous covariates (cov1 and cov2). The response variable is lognormally distributed, so I have employed the lognormal distribution in my GLIMMIX procedure (using default link function "id"). My code is below. proc glimmix order=data data=colony_repro plots = residualpanel; title 'GLIMMIX for RS w/ cov1 and cov2'; class trt; model RS = trt cov1 cov2 / dist=lognormal s ddfm = satterthwaite; lsmeans trt / cl; The trouble I am having is with my LS MEANS. I took the outputted LS Means for my treatments and associated 95% CLMS and back-transformed them (10^MEAN). Looking at the values in their original scale, they do not seem correct, and the 95% CLMs are enormous. I ran this same model in Proc GLM with the log10-transformed response variable (log10(RS)). The LS Means are very different and the confidence limits are much smaller. This leads me to believe that I have either coded something incorrectly or that I am not appropriately interpreting my LS Means output (e.g. it's not correct to apply the back-transformation as I did). Any thoughts on what I am doing wrong? Thank you for your time!
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