[R-sig-ME] single argument anova for GLMMs not yet implemented
A.Robinson at ms.unimelb.edu.au
Thu Dec 11 21:52:00 CET 2008
Echoing Murray's points here - nicely put, Murray - it seems to me
that the quasi-likelihood and the GLMM are different approaches to the
Can anyone provide a substantial example where random effects and
quasilikelihood have both been necessary?
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
Department of Mathematics and Statistics Tel: +61-3-8344-6410
University of Melbourne, VIC 3010 Australia Fax: +61-3-8344-4599
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