Dear all,
I know that this may be a already debated topic, but even searching  
the R-help and the r-sig-mixed-models archives I can not find a reply  
to my doubts...(but see Ben Bolkers' reply to my similar quest in r- 
help).

I am performing a binomial glmm analysis using the lmer function in  
the lme4 package (last release, just downloaded). I am using the  
"Laplace method".

However, I am not sure about what I should do to test for the  
significance of fixed effects in the binomial case: Is it correct to  
test a full model against a model from which I remove the fixed  
effect I want to test using the anova(mod1.lmer, mod2.lmer) method  
and then relying on the model with the lower AIC (or on the Log- 
likelihood test?)?

Would you advice me to use the glmmML function instead? (I am not  
sure where the differences are with lmer)

I thank in advance for your help!

best regards,
Achaz von Hardenberg

Ben Bolker wrote:
 >The short answer is that testing fixed effects in GLMMs
 >is difficult and dangerous.  Likelihood ratio tests on fixed
 >effect differences [which is generally what anova() does]
 >in a random-effects model are unreliable
 >(see Pinheiro and Bates 2000).  Most of the world does
 >F tests with various corrections on the denominator
 >degrees of freedom, but this is contentious (in particular,
 >Doug Bates, the author of lme4, disagrees).  lme4 will
 >eventually let you use an MCMC sampling method to test
 >fixed effects but that may or may not be working
 >in the current version.

 >I would try this question again on the r-sig-mixed
 >mailing list.

 > good luck,
 > Ben Bolker

Dr. Achaz von Hardenberg
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