Thankyou very much for your answer.

I will comment in the following lines:

2011/3/3 Ben Bolker <bbolker@gmail.com>

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> On 11-03-02 11:53 AM, alexandre martin wrote:
> > Dear all,
> >
> > I hope my question will not be considered too simple to be asked here. I
> > will try to clearly explicit my problem.
> >
> > I am working on longitudinal data to explore male reproductive success
> (rs).
> > These data are therefore count data and I deal with it by specifying in
> my
> > mixed models (random variable=individual identity) a poisson family.
> > My data present a large number of zeros, as shows the table() function
> > applyed to my response variable:
> >
> > table(rs)
> > 0   1   2   3   4   5   6   7
> > 365  60  20   9   8   5   3   1
> >
> >
> > I am wondering :
> > 1- if it matches with the description of an overdispersion or with a zero
> > inflated distribution.
>
>   If this is the marginal distribution, it's hard to tell. A certain
> fraction of the apparent overdispersion/zero-inflation can be explained
> simply by differences in the means among treatment groups or random
> effect levels.  Beyond that, a large number of zeros can potentially be
> explained either via overdispersion or via zero-inflation (e.g. Warton,
> David I. 2005. Many zeros does not mean zero inflation: comparing the
> goodness-of-fit of parametric models to multivariate abundance data.
> Environmetrics 16, no. 3: 275-289. doi:10.1002/env.702.
> http://dx.doi.org/10.1002/env.702.)
>
>  The easiest way to incorporate overdispersion in the current lme4
> framework is to add an observation-level random effect (as discussed in
> many posts on the list: also see <http://glmm.wikidot.com>).


ok.. I will pick-up informations about "observation-level" random effects
before annoying again you with this question.




> > 2- how it is possible to calculate a c-hat, given that determining the
> > number of parameters in glmm is not an easy task...
>
>   This is a quasi-likelihood approach.  You can get an *approximation*
> of the residual deviance via
>
> sum(residuals(modelfit,type="pearson")^2)
>
> What you use for numbers of parameters (specifically, how you count
> degrees of freedom for random effects) may depend (as also discussed
> frequently on this list and on glmm.wikidot.com) on what you are trying
> do ...
>
I just wonder if I can specify "family=poisson", to take into account that
my response variable (reproductive success) is a count. : reproductive
success = Number of lamb sired per male (from 0 to 7).
After sending to the list my first email, I estimated the dispersion
parameter using the output of a glm : residual variance / df =0.90.
Is it a good method? I assume it is not, given that I do not take into
account the hierarchical structure of my repeted measures, isn't it?



>
>
 > 3- if the realy bad fitted values obtained for the highest reproductive
> > success are the result of a first estimation with a poisson familly.
> >
>
>   See answers above ...
>
>  You can use glmmADMB for negative binomial or zero-inflated
> distributions (or both) with; at the moment it only allows one random
> effect (we're working on it: contact me off-list if you are really
> desperate); you can use MCMCglmm for zero-inflated data (MCMCglmm always
> includes an observation-level random effect).
>
I refit the model (without random effects) with a negative binomial
distribution. AIC(glm.nb)=545.15 VS AIC(glm)=564.8. Naively, I conclude that
a negative binomial distribution is better to "correct" for the excess of
zeros, even if the dispersion parameters are 0.62 and 0.90, respectively for
glm.nb and glm.

But I think it is an unorthodox method.


If the dispersion parameter obtained with glm is not so far from 1 (0.9),
can I just use a poisson family in lmer?




>  Ben Bolker
>
>
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Again, thank you...

Alexandre

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