I am trying to construct GLMs to explore the relationship between woodpecker abundance and 9 predictor variables (tree density, canopy cover, and so on). My data were collected as counts of woodpeckers, but I averaged the counts from multiple visits to each site (each site had between 1-6 visits made). So my data are not whole integers. They contain decimals, and I have a decent amount of zeros (when woodpeckers were not observed).
I am hitting problems with Poisson distribution because of the decimals. I tried adding an offset function but I still get errors in R because of the decimals:
>In dpois(y, mu, log = TRUE) : non-integer x = 0.917000...
Gamma distribution won't work because I have zeros. Is negative binomial the best bet? Maybe I am misunderstanding Neg Bin but I thought it was best for presence-absence data, and not continuous count data.
Here is the script I tried for the offset. I may be doing this wrong:
model <- glm(woodpeckercount ~ treedensity+canopy+snags+offset(log(visits)), data=woodpeckers, family=poisson)
visits is the number of visits I made to each survey site (ranges from 1 to 6). It is the number I divided the total woodpecker count by to get the average. When doing an offset, would I use the total woodpecker count for each site as my response variable instead of the average (in other words, should I not divide woodpecker count by the number of visits, since the offset function accounts for this?). I want to be sure I am accounting for unequal sampling effort between sites.