Variance explained by random factor
Fanatstic. Thanks such a lot for that command. The 'ldL2' = 2.178613e-11 'usqr' = 0 Is it not strange though that the test to see if sig difference between glm and glmm is so highly significant? as.numeric(2*(logLik(mixed)-logLik(ma))) #99.16136 pchisq(99.16136,1,lower=FALSE) #2.327441e-23 so should use a GLMM -----Original Message----- From: dmbates at gmail.com [mailto:dmbates at gmail.com] On Behalf Of Douglas Bates Sent: 14 August 2008 10:44 To: Renwick, A. R. Cc: Ken Beath; r-sig-mixed-models at r-project.org Subject: Re: [R-sig-ME] Variance explained by random factor
On Thu, Aug 14, 2008 at 11:24 AM, Renwick, A. R. <a.renwick at abdn.ac.uk> wrote:
Many apologise but the glm model I compared was ma not ma1 and thus did have the interaction term:
ma<-glm(RoundedOverlap~sess+breedfem+sess:breedfem ,family=poisson,data=Male) mixed<-lmer(RoundedOverlap~sess+breedfem+sess:breedfem+(1|Site),family =poisson,data=Male)
In that case it could be that the deviance or log-likelihood is not being evaluated correctly in glmer. Look at the slot named 'deviance' in the lmer fit. It should be a named numeric vector. The names of interest are 'disc', the discrepancy for the generalized linear models (this is the deviance without the compensation for the null deviance), 'ldL2', the logarithm of the square of the determinant of the Cholesky factor of a second-order term, and usqr, the squared length of the transformed random effects. For a mixed-effects model in which the variance of the random effects is estimated as zero, both 'ldL2' and 'usqr' should be zero. You can check these values in mixed at deviance
-----Original Message----- From: dmbates at gmail.com [mailto:dmbates at gmail.com] On Behalf Of Douglas Bates Sent: 14 August 2008 10:22 To: Ken Beath Cc: Renwick, A. R.; r-sig-mixed-models at r-project.org Subject: Re: [R-sig-ME] 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),fami l y=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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