[R-sig-ME] Variance explained by random factor
brandon at brandoninvergo.com
brandon at brandoninvergo.com
Tue Aug 26 02:30:30 CEST 2008
On Wed, 13 Aug 2008 16:17:48 +0100, "Renwick, A. R." <a.renwick at abdn.ac.uk>
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
>
> However,the model output that I get states that the variance explained by
> the random factor is 0:
>
>
> Generalized linear mixed model fit by the Laplace approximation
> Formula: RoundedOverlap ~ sess + breedfem + sess:breedfem + (1 | Site)
> Data: Male
> AIC BIC logLik deviance
> 109.9 127.2 -45.93 91.86
> Random effects:
> Groups Name Variance Std.Dev.
> Site (Intercept) 0 0
> Number of obs: 51, groups: Site, 14
>
> I would really appreciate if somebody could help me understand why the
> variance is 0.
> Many thanks,
> Anna
I'm having a similar problem as Anna, however the responses in this thread
don't seem to apply to my problem. In my case, when I fit a GLMM to my data
(family = Gamma), the variance of the random effect is effectively zero
(1e-12). I also fit a GLM to the data and this model has practically the
same estimates and t-values for all the parameters. However, when I check
for significant differences between the two models via the log-likelihood
ratio test, the result is highly significant. I understand that a model
without the random effects term will be interpreted as a simpler model and
thus it will have a lower log-likelihood value but I don't understand how
the addition of a random effects term with such a small variance can cause
such a large increase in the log-likelihood of the model. Am I missing
something obvious here?
Thanks for your help!
-brandon
Here's my output:
> mixed.model <- lmer(dev.time ~ sex*temp + (1|sleeve.in.temp),
data=data.clean, family=Gamma(link="log"), method="ML")
> summary(mixed.model)
Generalized linear mixed model fit using Laplace
Formula: dev.time ~ sex * temp + (1 | sleeve.in.temp)
Data: data.clean
Family: Gamma(log link)
AIC BIC logLik deviance
29.28 87.32 -3.639 7.277
Random effects:
Groups Name Variance Std.Dev.
sleeve.in.temp (Intercept) 2.5683e-12 1.6026e-06
Residual 5.1366e-03 7.1670e-02
number of obs: 1446, groups: sleeve.in.temp, 54
Fixed effects:
Estimate Std. Error t value
(Intercept) 3.659500 0.005993 610.6
sexm -0.088450 0.008956 -9.9
temp21 -0.207295 0.008132 -25.5
temp23 -0.433156 0.008363 -51.8
temp25 -0.612696 0.008491 -72.2
temp27 -0.755628 0.008297 -91.1
sexm:temp21 0.008189 0.012000 0.7
sexm:temp23 -0.003357 0.012243 -0.3
sexm:temp25 -0.014887 0.012321 -1.2
sexm:temp27 0.010674 0.012405 0.9
Correlation of Fixed Effects:
(Intr) sexm temp21 temp23 temp25 temp27 sxm:21 sxm:23 sxm:25
sexm -0.669
temp21 -0.737 0.493
temp23 -0.717 0.480 0.528
temp25 -0.706 0.472 0.520 0.506
temp27 -0.722 0.483 0.532 0.518 0.510
sexm:temp21 0.499 -0.746 -0.678 -0.358 -0.353 -0.361
sexm:temp23 0.490 -0.731 -0.361 -0.683 -0.346 -0.354 0.546
sexm:temp25 0.486 -0.727 -0.358 -0.349 -0.689 -0.351 0.542 0.532
sexm:temp27 0.483 -0.722 -0.356 -0.346 -0.341 -0.669 0.539 0.528 0.525
> glm.model <- glm(dev.time ~ sex*temp, data=data.clean,
family=Gamma(link="log"), na.action=na.omit)
> summary(glm.model)
Call:
glm(formula = dev.time ~ sex * temp, family = Gamma(link = "log"),
data = data.clean, na.action = na.omit)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.217280 -0.050703 -0.004718 0.043783 0.292775
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.659429 0.006014 608.474 <2e-16 ***
sexm -0.088440 0.008987 -9.841 <2e-16 ***
temp21 -0.207203 0.008161 -25.391 <2e-16 ***
temp23 -0.433163 0.008392 -51.617 <2e-16 ***
temp25 -0.612562 0.008520 -71.895 <2e-16 ***
temp27 -0.755615 0.008326 -90.752 <2e-16 ***
sexm:temp21 0.008232 0.012041 0.684 0.494
sexm:temp23 -0.003237 0.012285 -0.264 0.792
sexm:temp25 -0.015077 0.012363 -1.220 0.223
sexm:temp27 0.010813 0.012448 0.869 0.385
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1
‘ ’ 1
(Dispersion parameter for Gamma family taken to be 0.005172238)
Null deviance: 114.2564 on 1445 degrees of freedom
Residual deviance: 7.2771 on 1436 degrees of freedom
AIC: 5762.4
Number of Fisher Scoring iterations: 3
> as.numeric(-2*(logLik(glm.model)-logLik(mixed.model)))
[1] 5733.084
> pchisq(5733.084,1,lower=FALSE)
[1] 0
I checked the model's deviance as mentioned my Douglas Bates in this thread
and I got this:
> mixed.model at deviance
ML REML
7.277151 NA
More information about the R-sig-mixed-models
mailing list