Thank you @SteveDenham for your comments. I do have both the texts you suggest but haven't yet been able to go through SAS for Mixed Models. The other text is helpful is some regards and not yet in others. There is a lot of material that maybe only a statistics professor would fully understand, but I do try to reach out for help. I keep asking "what would my data need to look like for these models to be appropriate" and "what would need to be different for the model to work." One issue that is a perennial problem for me is that effective treatments often have no variance among replications, i.e., all zeros or 100s, and this will often cause the lsmeans to have no estimate or make no sense, even if it is just one treatment out of 12. All zeros can be an indication of a very successful treatment, but it will blow up the analysis and leave me unable to determine differences between treatments. I have used the negative binomial and Poisson distributions in the GLIMMIX model, and sometimes they just don't work. The old methods, while maybe less robust or correct, will always run and produce an output that seems reasonable. For those of us trying to use the newer methods, this is a real issue. Unlearning the old would be more effective if the new was more understandable, I think. Thanks again, Mark
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