[R-sig-eco] nlme model specification

[David Hewitt]

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? 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)
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