In a study on a bird species, I observe 558 locations. Each location is assigned one of 4 cases:
- never occupied by the species (never)
- occupied in the past but abandoned now (past)
- occupied in the past and now (always)
- not occupied in the past, but occupied now (current)
I have a set of explanatory variables (climate and landscape-structure). A simplified version of the data looks like this:
>bird.data location_ID case region temperature forest.coverage 1 current A 7.6 33 2 always A 8.1 65 3 current B 7.4 82 4 never A 9.0 11 5 always C 6.8 22 6 past A 8.1 46 7 past B 7.8 51 8 current C 7.9 52 ... ... ... ... ...
In R, I want to test weather the explanatory variables have an effect on the past and current occurence of the bird species. As a start, I want to compute univariate glmm using the
region is supposed to act as a random factor. Similar to anova, I hope that this can help to show wether the four groups differ in their explanatory variables.
I would try something like
lmer(case~temperature + forest.coverage + (1|region), data=bird.data)
However, I am not familiar with modeling categorical response variables. Are there any rules to follow? Especially: where can I start in Order to determine a useful family for my case?