Dear all,

I'm performing model selection based on AICc on a set of GLMMs that only vary in their fixed effects. The data comes from 12 transects (5 measures along each transect), then each transect is modelled as a random effect "+(1|transect)". As the response variable was proportions (presences/n), I fitted the models using binomial family and the total number of points (n) as weights.

Given that some models had boundary problems, I ran the model selection on a set of GLMs instead of GLMMs. The results were almost identical in terms of the list of models with the highest support (for 7 response variables where model selection was performed independently).

I'm wondering which approach is correct? Or, as my results show, it does not really matter, because the random effect does not change in my GLMMs?

Thanks,
Nelida.
--
Nélida R. Villaseñor
PhD Scholar
Fenner School of Environment and Society
The Australian National University


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