overdispersion estimation in a binomial GLMM

Thanks to Robert and Ben for their precious answers !

I tried the dispersion function it works and gives me a 1.244403 value.
As it is not really bigger than 1, should I treat my model as
overdispersed ?

Moreover I tried to fit an observation level random effect as suggested
by Ben Bolker, but it gives me an error message !


dispersion_glmer(model)#  1.244403
baba$obs <- 1:nrow(baba)

 model_OD <-
+ glmer(propNb~SexA*SexB*AgeA+(1|Nest)+(1|obs),data=baba,family="binomial")
Number of levels of a grouping factor for the random effects
is *equal* to n, the number of observations

So what is the problem with this ?

Thanks a lot !!
Thomas Merkling


Le 20:59, Ben Bolker a ?crit :
On 01/20/2011 11:40 AM, espesser wrote:

  Here is a small function to compute the dispersion of

a  binomial  model, according to  a previous answer of D. Bates on the
topic:

  dispersion_glmer<- function(modelglmer)
{

## computing  estimated scale  ( binomial model)
#following  D. Bates :
#That quantity is the square root of the penalized residual sum of
#squares divided by n, the number of observations, evaluated as:

n<- length(modelglmer at resid)

return(  sqrt( sum(c(modelglmer at resid, modelglmer at u) ^2) / n ) )
}



-- Robert Espesser
CNRS UMR 6057 - Universit? de Provence
5 Avenue Pasteur - BP 80975
13604 AIX-EN-PROVENCE Cedex 1

Tel: +33 (0)442 95 36 26

Le 20/01/2011 16:59, Thomas Merkling a ?crit :

Dear list members,

I am trying to fit a binomial GLMM and I wonder if there is
overdispersion. I'm not sure to know how to do it. I tried to fit with
"quasibinomial" family but apparently it doesn't exist anymore in lme4.

I also tried this but I am not sure that it is true for mixed models.

model<-lmer(propNb~SexA*SexB*AgeA+(1|Nest),data=baba,family="binomial")

k<- attr(logLik(model),"df") #
n<- length(fitted(model))
pearsonresid<- (1/(n-k)) * sum(resid(model,"pearson")2) # 1.731892
dev<- deviance(model)/(n-k) #2.378512

One more thing: how to deal with this model if there is
overdispersion ?

Thanks by advance,
Best,

   If there is overdispersion, the current advice is to fit an
observation-level random effect:

baba$obs<- 1:nrow(baba)

model_OD<-
glmer(propNb~SexA*SexB*AgeA+(1|Nest)+(1|obs),data=baba,family="binomial")