[R-sig-ME] overdispersion estimation in a binomial GLMM

[Thomas Merkling]

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 écrit :
> -----BEGIN PGP SIGNED MESSAGE-----
> Hash: SHA1
>
> 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 - Université 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 écrit :
>>> 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")
>
>
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-- 
Thomas Merkling, Doctorant (PhD Student)

Laboratoire "Evolution et Diversité Biologique" -EDB
UMR 5174 - bât 4R3 b2 - bureau 226

Université Paul Sabatier Toulouse 3
118, route de Narbonne
31062 TOULOUSE Cedex O9, FRANCE

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