Dear list;
I am working on a GLMM of wading bird capture rates as a function of species, group size and prey density:
Global_model.glmer <- glmer(CAPTURES~ grpCat*SPECIES + fish_density + (1|SAMPLE) , offset = log(seconds/60), family = poisson, data=Heron.data.nz)

I'm trying to access: 1) the necessity of the random effect, and 2) the overall fit of the model.

For the first question, I would like to compare the models with and without the random term.  However, I can't figure out how to get glmer() to fit the model without the random component.  I realize that I can fit such a model using glm(), but I'm worried that the likelihood's won't be comparable, due to a different omission of constant terms in the likelihoods between packages

For the second problem, I've tried a qq plot of the residuals provided by glmer(), but I'm not sure this is the correct approach.

Any suggestions would be greatly appreciated.

Eric D. Stolen, Ph.D.
Mail Code: IHA-300
Kennedy Space Center, FL  32899
office: 321-476-4119; cell: 321-480-6799; fax: 321-853-2939; email: eric.d.stolen@nasa.gov



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