
I have used the glm function to fit a series of models using a poisson error structure.
e.g:
Model 1: Y is a function of a + bX
Model 2: Y is a function of a

I have tried to compare models using AIC, but do not get sensible results (lower AICs for the null, intercept only, model despite the alternate model containing highly significant parameters).  



I found the following explanation in the online R manual, that seemed to be relevant:

"There is a potential problem in using glm fits with a variable scale, as in that case the deviance is not simply related to the maximized log-likelihood. The "glm" method for function extractAIC makes the appropriate adjustment for a gaussian family, but may need to be amended for other cases. (The binomial and poisson families have fixed scale by default and do not correspond to a particular maximum-likelihood problem for variable scale.) "

My question is, how do you amend the function for the poisson family?
Should I be using AIC, or is there a better information criterion? - I want a method that has the flexibility to compare alternative models that (unlike in my example) are not simply nested families of additive variables.

Many thanks,
Jennie Bee


~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Jennie Bee

Conservation and Community Ecology Group
Department of Plant Sciences
University of Cambridge
Downing Street 
Cambridge
CB2 3EA

Tel: +44 (0)1223 330213 (office); 07890 971 374 (mobile)
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