Hello,

I am analyzing the results from an artificial selection experiment and have a question regarding the random effects structure. My experimental design is fairly simple: there are two treatments, with 5 replicates for each treatment, measuring a response from each replicate each generation for 20 generations. I am interested not only in the effects of treatment, but in a main effect of time, well as the interaction (looking for a difference between treatments over time). I have looked over quite a few of the examples regarding the structure for such an experiment and have found two alternative models that seem appropriate, but I am unclear what the difference is. These models are:

lmer1<- Response~treatment*generation + (1 | generation/replicate); a more typical nested effects structure

lmer2<- Response~treatment*generation + (generation | replicate) ; following the sleepstudy design described in section 2 of the 2012 lme4 implementation guide by Dr. Bates.

I am sure part of my confusion is not really understanding what it means to put something in front of the grouping factor, versus a nested design. It seems as though the second one could be more appropriate for a repeated measures framework, but I am at a loss to explain why.

Thanks for your help,

Kevin Burls

Kevin Burls
Ph.D. candidate
EECB Program
University of Nevada, Reno
kburls@unr.edu<mailto:kburls@unr.edu>
http://wolfweb.unr.edu/~kburls
www.nevadabugs.org<http://www.nevadabugs.org>




	[[alternative HTML version deleted]]

