[R-sig-ME] overdispersion estimation in a binomial GLMM
Ben Bolker
bbolker at gmail.com
Thu Jan 20 19:01:55 CET 2011
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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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