I am using a multilevel modeling approach to model change in a person's symptom score over time (i.e., longitudinal individual growth models). I have been using the lme function in the multilevel package for the analyses, but my problem is that my outcome (symptoms) and one of my predictors (events) are count data, and are non-normal. Do you have any suggestions for how to deal with them? Are there poisson-regression or similar techniques that can be applied to multilevel modeling (lme, nlme, etc.)? Some of my predictors are normal, however, so the technique should be able to accommodate normally distributed data, as well. I have tried square root transformations, and it appears that the data become more normal, but at the expense of some of the consistency in my findings modeling the raw data. Your input would be very helpful and greatly appreciated. Thanks so much! -- View this message in context: http://n4.nabble.com/Multilevel-modeling-with-count-variables-tp1692632p1692632.html Sent from the R help mailing list archive at Nabble.com.
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