Closed florianhartig closed 3 years ago
Hi,
this is expected behavior.
I think the misunderstanding here is the statistical definition of overdispersion. Overdispersion is not a thing that can be defined absolutely for a dataset, but it is defined with respect to the model that you fit. Overdispersion means that the data is more dispersed than expected under a particular model.
Thus, what the DHARMa residuals tell you is that
The DHARMa results are therefore fully identical to what the AIC tells you - the beta-binomial is preferable.
Possibly, the confusion comes from the fact that glmmTMB seem to define overdispersion wrt to a "base model", in this case the binomial, and then call the dispersion parameter in the beta-binomial "overdispersion parameter". However, note that if a beta-binomial has a dispersion parameter of 5, it means it is more dispersed than the binomial, not that the fitted beta-binomial is overdispersed (as the fitted model now has actually the correct dispersion).
This is not the first time that I had questions about this wrt glmmTMB output, maybe I'll suggest to the glmmTMB developers to re-consider the naming of the dispersion parameter (as it seems to confuse people).
Question from a user