Hello all,


I am trying to use an IT approach to analyse some ecological data. I have a mixed model with nested random effects (using glmer in package lme4). I initially fit the model with a Poisson error distribution, but it was overdispersed so I added an observation-level random effect to deal with the overdispersion. Model check plots are then very good. However, my next step in the IT approach is to generate a set of candidate models (using function 'dredge'). I have two choices now, and I don't know which is correct / best:

Choice 1. Start with the Poisson lognormal model (i.e. I have already dealt with the overdispersion) and generate a model set using AICc.

Choice 2. Start with the Poisson model, which is overdispersed, and generate a model set using QAICc and specifying the dispersion parameter.

Is it acceptable to do either of these, or should I stick to Choice 2, which is what the example code does in the MuMIn package help documentation? (However, if I go for choice 2, then the model check plots of the global model aren't great, and yet there's no way of checking the averaged model for goodness of fit - so how do I know the model fit is OK?).

Thank you very much for any help you can give. I realise it's not a coding issue but haven't had any luck finding an answer anywhere else.


--
Alexa Varah

Centre for Agri-Environmental Research (CAER)
School of Agriculture, Policy & Development
University of Reading, RG6 6AR

Email: a.varah@pgr.reading.ac.uk

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