[R-sig-ME] How to get dispersion parameter from a binomial mixed, model? (Ben Bolker)

Ben Bolker bbolker at gmail.com
Mon Dec 17 17:21:11 CET 2012

Highland Statistics Ltd <highstat at ...> writes:

> From: Ben Bolker <bbolker at ...>
>   <v_coudrain at ...> writes:
> > Thank you very much for this link. The implemented function
> >  "overdisp_fun" allowed me to get what I want.
> > My data are indeed overdispersed and I think about adding an
> > individual random effect, since
> > quasi-binomial distribution are not supported by lmer. I may use
> > penalized quasi likelihood, but I know that it is quite controverse.
> >  Maybe someone could give me some advice?
>    It's hard to give completely general advice.  I don't know what
> (for example) Zuur et al say in their books.  I generally have a mild

> AFZ: In our 2012 volume we carried out a simulation study (using the
> owl data) for a Poisson GLMM with observation level random intercept
> epsilon. We were curious to see what happens if the observation
> level random effect is much larger than the random effect that was
> already in the model (for nests). Would it disappear...or would it
> stay as before?  And what happens if the observation level RE is the
> same magnitude, or smaller than the random effect nest?
> The method itself seems to perform ok. What I don't like of the
> observation level random intercept is that quite often the
> observation level random intercept causes a perfect fit for the
> model (assuming you use the fitted function...it includes the
> observation level RE). Well..  perhaps I should re-phrase this. I
> don't like that the fitted function in glmer includes the
> observation level random intercept.

For what it's worth, the development version of lme4 includes a
predict() function that allows inclusion or exclusion of different
random effects in the prediction, so it would be easy enough
to generate the fitted value you wanted this way.
(I'm sorry to keep referring everyone to the development version,
but I really don't want to write all of these functions twice!)
> My preference is to use an NB GLMM for overdispersed count data
> (assuming that the overdispersion is not caused by something
> else). For binomial data, perhaps the beta-binomial?

glmmADMB (the best bet) doesn't currently include the beta-binomial
as an options although it wouldn't be awfully hard to add it.
Otherwise I guess you'd need something in the BUGS/JAGS/Stan,
or AD Model Builder, family to implement this ...

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