[R-sig-ME] single argument anova for GLMMs not yet implemented

Andrew Robinson A.Robinson at ms.unimelb.edu.au
Fri Dec 12 01:00:46 CET 2008

Hi Drew,

On Thu, Dec 11, 2008 at 03:52:06PM -0600, Andrew J Tyre wrote:
> I also like the explanation of quasi-likelihood vs. glmm, but I can say 
> from an ecological perspective I frequently encounter situations in which 
> I have included all the random effects of blocks, plots, times etc, and 
> still have massive amounts of overdispersion. A student in my Ecological 
> Statistics class examined repeated counts of grasshoppers in plots that 
> have or have not received nitrogen addition. A poisson family glmm gives a 
> nice account of the effects of total veg biomass, date, and nitrogen 
> addition, but the residual deviance  is  > 1700 for a sample size of about 
> 400. I would love to be able to fit a negative binomial model in that 
> case; I typically resort to using WinBUGS and MCMC to do this, but that is 
> beyond what I can get my students to do in a one semester course. 

This looks like a promising example (so to speak) ... have you tried
fitting a poisson family glmm and a negative binomial hierarchical
model to these data in WinBUGS?  if so, how do the models compare
within that framework?
> I have encountered situations in which even using a negative binomial 
> model (for counts) or beta-binomial type model ( for proportion of success 
> data) are insufficient to explain the variability in ecological 
> situations. In these cases I usually have reason to believe that there is 
> a discrete mixture going on - ie the observations are coming from two or 
> more distinct populations which have not been distinguished by anything 
> the observer can record, or thought to record (immune status for parasite 
> hosts, for example). I have tried quasi- family models in those cases, but 
> always felt a little uncomfortable drawing much in the way of inference. I 
> understand likelihood! 

I'd suggest that if you have reason to believe that you have an
underlying discrete mixture but no way to tease out the identity, then
any modelling should be treated with great caution!  Or maybe EM would
help?  Treat the population identity as a latent variable?  Simon
Blomberg told me about a really nice simple example in which he did
that kind of thing, over a beer a few months ago.  It might or might
not be relevant here.  



Andrew Robinson  
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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