[R-sig-eco] nlme model specification

[Simon Blomberg]

On Fri, 2008-05-23 at 14:42 -0700, David Hewitt wrote:
> 
> Kingsford Jones wrote:
> > 
> > I don't think it is useful to put this in a Bayesian vs. frequentist
> > framework. Burnham and Anderson write:
> > 
> > "AIC can be justified as Bayesian using a
> > 'savvy' prior on models that is a function of sample size and the number
> > of
> > model parameters Furthermore, BIC can be derived as a non-Bayesian result.
> > Therefore, arguments about using AIC versus BIC for model selection cannot
> > be
> > from a Bayes versus frequentist perspective."
> > 
> > see:
> > http://www2.fmg.uva.nl/modelselection/presentations/AWMS2004-Burnham-paper.pdf
> > 
> 
> Model selection doesn't reduce to AIC vs. BIC, or to Bayesian vs.
> frequentist. AIC and BIC are only two approaches for model selection, after
> all. That was part of my main point. Nonetheless, the fact remains that
> Bayesian methods differ from "pure" likelihood methods, in principle and in
> practice. If you're going to use BIC, how will you choose your priors? 

BIC assumes a unit reference prior. That is, a prior containing
information equivalent to one observation.

> It's
> a practical issue. EJW has done a lot of work on model selection and I
> thought his papers were a good intro to the variety of approaches.
> 
> 
> 
> >> All that said, since you're dealing with random effects, Bayesian
> >> approaches
> >> do appear to have the upper hand at present, and a shift in that
> >> direction
> >> may be warranted.
> > 
> > Can you expound on the last paragraph?
> > 
> 
> Others on the list are far better positioned than I to expound, but as a
> lurker in stats journals I see a lot more work on model selection methods
> for models with random effects in a Bayesian context. For instance, type
> "random effects model selection" into Google and almost all the first 20
> results are Bayesian. David Anderson told me personally that he thinks I-T
> methods (AICc) are really struggling with random effects. I don't honestly
> know how the various packages in R calculate the AIC values for models with
> random effects (of course, you can look and see), but I'd guess it's
> something you have to be rather careful about. I still need to read Pinheiro
> and Bates, obviously.
> 
> -----
> David Hewitt
> Research Fishery Biologist
> USGS Klamath Falls Field Station (USA)
-- 
Simon Blomberg, BSc (Hons), PhD, MAppStat. 
Lecturer and Consultant Statistician 
Faculty of Biological and Chemical Sciences 
The University of Queensland 
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email: S.Blomberg1_at_uq.edu.au

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