Dear lmer users,

I compute several models with lmer (poisson distribution, correction for 
overdispersion). 

I run several models with the same random effect and different structures for 
fixed effect (a full model and different models with only some of the factors of 
the full model). 

I select the model with the minimal AIC. In fact, two models are within 2 AIC 
units:
- a model (with lowest AIC) with one factor, X1.
- another model (at 0.8 of the first) with two factors, X1 and X2.

Looking at the p-values, X1 is significant in both models while X2 is not 
significant, despite the low AIC associated to the model. How to interpret the 
apparent contradiction between the non-significant p-value and the low AIC? 


>From an old message on the list, I can see that p-values  are not good. 
But the bayesian approach mcmcsample does not run either with lmer.

How to get the p-values associated with the fixed effects?
Thank you for your help
Doris Gomez


      
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