I have data where I measured the distribution of individuals among 4 patches that differ in known resource density (a continuous variable). Groups of 12 individuals were observed and their presence in the 4 patch types was recorded 10 times (every 20 minutes). Trials differed in the presence or absence of another species. If I only had two patches, I believe I would use a GLMM (family = binomial) with arena_ID as a random variable and presence/absence of other species as a fixed effect.
However, with 4 patch types I want to use patch type (amount of resources) as a continuous fixed effect. However, the percent patch types always sums to 100% (so a regression of percent in patch versus resource in patch seems somewhat incorrect). And the data is multinomial, rather binomial.
I have been calculating the slope of percent in patch versus patch resource for each arena_ID, and then asking if the collection of slopes differ from 0 using a t-test, but I am looking for a better way.
Hi Barney
You may want to consider a GLMM (family=poisson) and use counts of individuals on each of the patches over time. Use of random and fixed effects are analogous to the binomial case.
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