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.
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