Model averaging using QAICc
Diana Virkki <d.virkki <at> griffith.edu.au> writes:
Hi all, I am having some trouble running GLMM's and using model averaging with QAICc. Let me know if you need more detail here: I am trying to run GLMM's on count data in the package glmmADMB with a negative binomial distribution due to overdispersion. The dispersion parameter has now reduced to 2.679 for the global model (from a dispersion parameter of 27.507 with a poisson distribution), and I am not sure if this is still considered too high for running the models?
A dispersion parameter of 27 probably indicates something wonky about the original data. I'm also surprised by a dispersion parameter not close to 1 for the fitted NB model (as the NB model should in principle take care of most of the overdispersion -- the mean square of the Pearson residuals might be slightly different from 1, because the NB shape/overdispersion parameter is calculated by ML, but this is still a suspiciously large value).
I would like to try to use QAICc's for model selection and model averaging with the package MuMIn. I have so far been able to produce a QAICc output only for the models. I read that model averaging with QAICc can be done in MuMIn but cannot find the syntax to get these outputs, including the model weightings, parameter estimates, confidence intervals, and relative variable importance.
Can't help you there. In my experience MuMIn can only model-average the wide range of model types it knows about, but there could easily be features I don't know about.
Any advice would be greatly appreciated. As well as if there are other potential better options for dealing with the overdispersion.
You probably need to look at your data more carefully -- do the model fits seem reasonable? Are there big outliers, or zero-inflation, or ... ? If you are using glmmADMB for mixed model fitting, I would suggest follow-ups go to r-sig-mixed-models at r-project.org ... Ben Bolker