R help - Jul 2013 - FW: Nested and/or crossed and 2 level random factor

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.


 		 	   		   		 	   		  
	[[alternative HTML version deleted]]

This is not the best place for this post.

Post instead to r-sig-mixed-models or r-sig-ecology .

Cheers,
Bert

On Thu, Jul 11, 2013 at 8:48 AM, Linda B?rgi <patili_buergi at
thotmail.com> wrote:
>
>
>
>
> 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.
>
>
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
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http://www.R-project.org/posting-guide.html
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-- 

Bert Gunter
Genentech Nonclinical Biostatistics

Internal Contact Info:
Phone: 467-7374
Website:
http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm