My lab is conducting an experiment with a nested, repeated measures design, and would like feedback on the proper design for the analysis.
Each plot receives 3 treatments (food provisioning from a central feeder, order randomized among plots) over 3 consecutive sessions. Within each plot, a binary response (animal detected or not) is measured repeatedly (several checks) at points arranged at several distances from the feeder. There are multiple replicate points per distance, but the number of replicate points differs among distances. Points are numbered 1,2,3,... in each distance and plot. The experiment was run concurrently for all plots and, for all plots, the response variable (detection) is generally greater in the second and third sessions than the first. We're trying this as a GEE analysis in GENMOD, treating plot and point as random subjects, and treating treatments, distances, and session as fixed effects. The scientific hypothesis being tested indicates that the relationship between detection rate and distance from the feeder should differ among treatments.
1) Would "LOGOR=" be more appropriate than "type=" in this case?
2) Should we account for serial autocorrelation (type=AR1)?
3) Would we be better off using GLIMMIX? Pros / cons?
4) Are there other important questions that I'm not asking?