Hi,

If I am correct you can use the library glmmML in which you will find a 
bootstrap method to test for random effect (cluster) with binomial GLMM 
for example.

In your case it should be:

library(glmmML)

random.model<- glmmML(sex ~hwp+hcp, data =ydata,


           cluster= colony, family="binomial",method="Laplace", boot=4000)

## with cluster to specify your random effect (which terms are correlated, boot the number of bootstrap (default =0), family : binomial or Poisson and method: Laplace or ghq

Hope this will help!
Thomas




Le 20:59, Simone Santoro a écrit :
> Hi everybody,
>
>
> If I am correct, you can compare a model with random effect with
>   the same model without the random effect by using the nlme function,
> like this:
>
>
> no.random.model<- gls(Richness ~ NAP * fExp,
>
>                method =REML", data = RIKZ)
>
> random.model<- lme(Richness ~NAP * fExp, data =IKZ,
>
>            random =1 | fBeach, method = "REML")
>
> anova(no.random.model,random.model)
>
>
> But, nlme is valid only for the gaussian family, isn't it? In my case I have a mixed model with binomial family, like this:
>
>
> random.model<- lme(sex ~hwp+hcp, data =ydata,
>
>
>            random =1 | colony, method = "REML")
>
>
> where "sex" is a binary variable, "hwp" and "hcp" are continuous variable and "colony" is a factor with two levels.
>
> I want to compare this model with another one without the random
> effect, I have tried with the lme4 but after this I cannot figure out
> how to build this same model without the random effect in order to make
> it comparable to the random effect model.
>
>
> Thanks for any help.
>
> Simone
>   		 	   		
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>
>

-- 
Thomas Merkling, Doctorant (PhD Student)
Web Page <http://www.edb.ups-tlse.fr/Merkling-Thomas.html>

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