On Fri, May 23, 2008 at 12:05 PM, David Hewitt <[EMAIL PROTECTED]> wrote: > > > >>> On Thu, May 22, 2008 at 5:55 PM, Caroline Lehmann: >>> Models were compared and ranked using AICc. I would suggest modifying >>> this to BIC since there >>> are so many measurements. >> >> Is there theory to support this suggestion? I find choosing an *IC to >> be a confusing issue and would appreciate any pointers to theory, >> simulations, etc that may shed some light on the subject. >> > > Choosing a model selection method is indeed a confusing process. However, it > does not simply depend on the number of measurements. Oversimplifying > things, AIC/AICc are used for model selection following maximum likelihood > model fitting and BIC (and Bayes factors) are used in a Bayesian context > (likelihood + priors). The two approaches are different in a number of ways. >
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 > So NO, there is no theory specifically pointing to BIC and a Bayesian > strategy because there are "lots of measurements". However, there is more > theory than you (and I) care to know about regarding when to use a Bayesian > framework versus a "pure" likelihood framework. And, as in all academic > disputes, there is no clear consensus. > > There's AIC/AICc, BIC, DIC, TIC, etc. and then simulation-based criteria as > well (about which I know zip). You can read Burnham and Anderson (2002) to > get their opinions about AIC and the information-theoretic strategy, and > among many other references I think EJ Wagenmakers sums up the Bayesian > perspective well in the 2007 paper listed here ("pratical solution to the > p-value problem"): > > http://users.fmg.uva.nl/ewagenmakers/papers.html > > It's a good read, even if you disagree with his conclusions about the > Bayesian strategy. > > 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? thank you, Kingsford Jones > > > ----- > David Hewitt > Research Fishery Biologist > USGS Klamath Falls Field Station (USA) > -- > View this message in context: > http://www.nabble.com/nlme-model-specification-tp17375109p17433342.html > Sent from the r-sig-ecology mailing list archive at Nabble.com. > > _______________________________________________ > R-sig-ecology mailing list > R-sig-ecology@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
