I'm working with a pretty large dataset (n = 4,500) where 10% of my points (pixels in a GIS landscape) are 1s and the rest are 0s. The full model for my data looks something like this:
model.full = glm(pond~elev+slope+landform+strmord+wcover, family=binomial, data)
Independent variables are elevation, slope and vegetation cover (all continuous), landform (categorical 4 levels), and stream order (categorical 4 levels). The response is a variable that takes a value of one if the pixel was used by an animal and 0 otherwise.
The values of the residual deviance are 2220.6 with 4420 df. This is slightly above .5 which means my data are underdispersed. I have two questions:
- Is this really a problem?
- Is there any way to deal with this (i.e: alternative model structure)?