single argument anova for GLMMs not yet implemented
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. Can anyone provide a substantial example where random effects and quasilikelihood have both been necessary? 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/