[R-sig-ME] Repeated measures and crossed/nested factors

[Linda Buergi]

Dear All,

I have two quick questions about my study design. For 4 years, once every
season, we destructively sampled larvae on bushes (the same bushes 
every time) and measured parasitism on these larvae. We had 10 bushes 
per location and two locations. 
We are interested in whether 
parasitism changed over the years and varied with season. With repeated 
measures on bushes, and bushes nested in location, my model looks like 
this:

model<-glmmPQL(parasitism ~ year:season + year + season, random=~1|location/bush, family=binomial)

Question 1: A reviewer of our paper suggested that seasons are nested 
within years and that we should include this in the model. However, I 
think seasons are crossed with years, not nested. If that's the case, 
can I leave the model as is (as far as season and years are concerned)?

Question
2: I know it is ridiculous to have location as a random factor since it
only has two levels. I've read a lot in the archives and people usually
suggest to leave that factor out altogether. But leaving it out is not 
an option because levels of parasitism 
vary significantly with location (but that is of no interest to us, 
hence not really a fixed factor). Could I just include it as a 
covariate?  glmmPQL(parasitism ~ year:season + year + season + location,
random=~1|bush, family=binomial)? 

Thank you already for any answers and suggestions!

PS.
I used glmmPQL instead of lmer because without the 
over-/underdispersion function in lmer everything was highly 
significant, whereas with glmmPQL it is not.

Linda Buergi
Department of Environmental Science, Policy and Managment
University of California, Berkeley