[R-sig-ME] single argument anova for GLMMs not yet implemented
Gavin Simpson
gavin.simpson at ucl.ac.uk
Thu Dec 11 22:43:15 CET 2008
On Thu, 2008-12-11 at 14:58 -0600, Douglas Bates wrote:
> On Thu, Dec 11, 2008 at 2:52 PM, Andrew Robinson
> <A.Robinson at ms.unimelb.edu.au> wrote:
> > Echoing Murray's points here - nicely put, Murray - it seems to me
> > that the quasi-likelihood and the GLMM are different approaches to the
> > same problem.
>
> I agree and I also appreciate Murray's elegant explanation.
>
> > Can anyone provide a substantial example where random effects and
> > quasilikelihood have both been necessary?
>
> I'm kind of waiting for Ben Bolker to let us know how things look from
> his perspective. I seem to remember that Ben and others in ecological
> fields were concerned about overdispersion, even after incorporating
> random effects.
Not wanting to preempt Ben or anything, but yes, we ecologists are very
concerned about overdispersion. However, and I say this as someone new
to this field (mixed models, not ecology), the quasilikelihood approach
seems far more of a fudge to avoid having to think about where the
overdispersion is coming from. I find the negative binomial far more
intuitive to deal with than working around the problems not having a
proper likelihood brings (inference, model selection, information
stats). Often, the course of overdispersion is of direct interest
itself.
In the GLM arena I find hurdle and ZIP and ZINB models far more
interpretable in ecological terms than fudging the problem with
quasilikelihood methods. And after-all, that is what I am interested in;
models I can interpret in ecological terms.
My two-penneth,
G
>
> >
> > Best wishes,
> >
> > Andrew
> >
> >
> > On Fri, Dec 12, 2008 at 09:11:39AM +1300, Murray Jorgensen wrote:
> >> The following is how I think about this at the moment:
> >>
> >> The quasi-likelihood approach is an attempt at a model-free approach to
> >> the problem of overdispersion in non-Gaussian regression situations
> >> where standard distributional assumptions fail to provide the observed
> >> mean-variance relationship.
> >>
> >> The glmm approach, on the other hand, does not abandon models and
> >> likelihood but seeks to account for the observed mean-variance
> >> relationship by adding unobserved latent variables (random effects) to
> >> the model.
> >>
> >> Seeking to combine the two approaches by using both quasilikelihood
> >> *and* random effects would seem to be asking for trouble as being able
> >> to use two tools on one problem would give a lot of flexibility to the
> >> parameter estimation; probably leading to a very flat quasilikelihood
> >> surface and ill-determined optima.
> >>
> >> But all of the above is only thoughts without the benefit of either
> >> serious attempts at fitting real data or doing serious theory so I will
> >> defer to anyone who has done either!
> >>
> >> Philosophically, at least, there seems to be clash between the two
> >> approaches and I doubt that attempts to combine them will be successful.
> >>
> >> Murray Jorgensen
> >>
> >>
> >
> > --
> > Andrew Robinson
> > Department of Mathematics and Statistics Tel: +61-3-8344-6410
> > University of Melbourne, VIC 3010 Australia Fax: +61-3-8344-4599
> > http://www.ms.unimelb.edu.au/~andrewpr
> > http://blogs.mbs.edu/fishing-in-the-bay/
> >
>
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Dr. Gavin Simpson [t] +44 (0)20 7679 0522
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