I have a dataset comprised of proportions that measure "activity level" of individual tadpoles, therefore making the values bound between 0 and 1. This data was collected by counting the number of times the individual moved within a certain time interval (1 for movement, 0 for no movement), and then averaged to create one value per individual. My main fixed effect would be "density level".
The issue I am facing is that I have a factor variable, "pond" that I would like to include as a random effect - I do not care about differences between ponds, but would like to account for them statistically. One important point about the ponds is that I only have 3 of them, and I understand it is ideal to have more factor levels (5+) when dealing with random effects.
If it is possible to do, I would like some advice on how to implement a mixed model using
betamix() in R. I have read the R help files, but I usually find them difficult to understand (what each argument parameter really means in the context of my own data AND what the output values mean in ecological terms) and so I tend to work better via examples.
On a related note, I was wondering if I can instead use a
glm() under a binomial family, and logit link, to accomplish accounting for random effects with this kind of data.