[R-sig-ME] Variance explained by random factor

Douglas Bates bates at stat.wisc.edu
Thu Aug 14 11:21:46 CEST 2008

On Thu, Aug 14, 2008 at 11:10 AM, Ken Beath <ken at kjbeath.com.au> wrote:
> On 14/08/2008, at 1:17 AM, Renwick, A. R. wrote:
>> I am currently trying to run a lmer model with poisson distrubution.  I
>> tested the model with a model without the random effect and it inferred that
>> I should include the random effect:
>> ma1<-glm(RoundedOverlap~sess+breedfem,family=poisson,data=Male)
>> mixed<-lmer(RoundedOverlap~sess+breedfem+sess:breedfem+(1|Site),family=poisson,data=Male)
>> #test to see if sig difference between glm and glmm
>> as.numeric(2*(logLik(mixed)-logLik(ma)))
>> #99.16136
>> pchisq(99.16136,1,lower=FALSE)
>> #2.327441e-23  so should use a GLMM
> The problem may be due to the random effects model containing an interaction
> term sess:breedfem that the glm doesn't.

I agree.  The result from the likelihood ratio test is actually
evaluating the significance of the interaction term, not the random
effects term.

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