I am using AICc for small sample sizes to compare 8 a priori models (including null model). I fitted my models using a GLMM due to the nested nature of my data and defined the family as 'poisson' based on a visual inspection of the error structure from my residual vs fitted values plot.
After running my analysis using lmer from the lme4 package in R, I obtain very large log likelihood values, e.g., LL = -120995125, which makes the delta AICc values very large as well, e.g., difference between 'best' fit model and second ranking model is 12422654. This makes comparison within the model set rather dubious.
I was wondering if anyone has encountered a similar problem or if there is something I am missing when using family=poisson for lmer in my model fitting.