Dear all,
I recently ran a model selection (AIC-based) to investigate the role of several etho-ecological factors in shaping the emission of parasites in my study species. My data are counts showing overdispersion. I therefore fitted my models using the function glmmadmb with family=nbinom. Visual inspection of residuals (normality, heteroschedasticity, independence) suggested the global model fitted the data adequately, and I'm pretty happy with the results of my analysis. For the sake of curiosity, however, I tried to re-run the model selection using the function glmer, with family=poisson, adding the observation-level as a random factor (1|obs) to account for overdispersion, as recently suggested. In this case, however, visual inspection of residuals for the global model were not very satisfactory. After running the model selection, the results were quite different from those obtained with glmmadmb (not dramatically different, but still...). Given I have no deep knowledge into the philosophy behind the use of glmer with (1|obs), I was wondering:
1) when one should rely on the use of glmer with (1|obs) to account for overdispersion? (i.e. is the check of residuals for the global model the key issue here?)
2) why did I find such a difference in the outcome of the 2 model selections?
Kind regards,
Luca