Ana Vazquez (who is in charge of the development of pedigreemm I shall 
think) responded me that the package was not meant to deal with this 
kind of model.

If I understood correctly, the function is not designed to fit GLMM with 
p=n (i.e. number of effects equal to the number of observations = 
generalized animal model in my case).


Le 08/05/2012 00:05, David Duffy a écrit :
>
> n Thu, 3 May 2012, Pierre de Villemereuil wrote:
>
> > Dear all,
> >
> > I'm having trouble with pedigreemm and simulated binary data. From the
> > simulation, I know the heritability should be 0.5 and MCMCglmm give me
> > for this dataset 0.53. Yet, pedigreemm yields a h? of 0.22. I'm not sure
> > of how much bias I should expect from a Gauss-Hermite quadrature
> > algorithm, but this still annoys me somehow...
> >
> > My model is a binomial using a probit link with only the additive
> > genetic random effect (Va variance component):
> > 
> *m1<-pedigreemm(phen~(1|animal),data=data,family="binomial"(link="probit"),
> >  nAGQ=10,REML=TRUE,pedigree=list(animal=ped2),verbose=TRUE)
>
> Looks OK to me.  If you make your pedigree simpler, eg unrelated 
> sibships of fixed size (ie simple cluster
> model) or full sibs and half sibs, then you can get a direct estimate 
> of the heritability from the recurrence
> risks to test against for any single dataset.
>
> --
> | David Duffy (MBBS PhD)                                         ,-_|\
> | email: davidD@qimr.edu.au  ph: INT+61+7+3362-0217 fax: -0101  /     *
> | Epidemiology Unit, Queensland Institute of Medical Research   \_,-._/
> | 300 Herston Rd, Brisbane, Queensland 4029, Australia  GPG 4D0B994A v
>


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