[R] Evaluating the significance of the random effects in GLMM
Ben Bolker
bbolker at gmail.com
Wed Jan 23 16:08:33 CET 2013
Gabriela Agostini <gabrielaagostini18 <at> gmail.com> writes:
>
[snip]
> I am working with GLMM using the binomial family
> I use the following codes
>
> I dropped no significant terms, refitting the model and comparing the
> changes with likelihood:
>
> G.1<-lmer(data$Ymat~stu+spi+stu*sp1+(1|ber),data=data,family="binomial")
> G.1b<-lmer(data$Ymat~stu+spi+(1|ber),data=data,family="binomial")
>
> anova (G.1,G.2)
>
> But, when I want to evaluate the significance of random effect (1|ber)
> I cannot use a likelihood-ratio test, probably because the link
> function of both models is different.
>
A couple of minor comments:
* you should probably use Ymat rather than data$Ymat
as your response, it will make post-processing easier
* in your first model do you really mean stu*sp1 rather than stu*spi?
* since A*B is equivalent to A+B+A:B, your first model specification is
equivalent (assuming you really meant stu*spi) to stu*spi OR stu+spi+stu:spi.
This won't change your answers but will be clearer to experienced R users.
I don't understand why anova() won't work in this case. At least
for the example you've shown us, it should. The link functions aren't
different.
Please (1) follow up to r-sig-mixed-models at r-project.org and (2)
try to provide a little more information: a reproducible example if
possible (http://tinyurl.com/reproducible-000).
PS the section in http://glmm.wikidot.com/faq may provide some
useful background on testing random effects.
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