While analysing the effect of environmental data on the activity of an animal species (the latter given as count data) I am fitting negative binomial GLMs with one predictor using the MASS library in R. Unfortunatley, the data set is very small (n=7 to 9).
In some cases, the theta value in glm.nb gets very large (accompanied with the warning "iteration limit reached"), possibly indicating that there's no overdispersion and a poisson GLM might be a better choice. Using a poisson GLM, however, a residual deviance of e.g. 150 on 7 degrees of freedom indicates that there actually is overdispersion - or did I miss something?
Using a quasi-poisson GLM works, but I would like to retain ML-based measures such as AIC and Vuong test for model comparison. Any suggestions for alternative approaches are greatly appreciated!