Variance explained by random factor
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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