nlme model specification
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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