Recent Forum Posts

From categories:

All categories

Beanplots

Trevor (guest) 1240442454|%e %b %Y, %H:%M %Z|agohover
in discussion Hidden / Per page discussions » Dynamite plots: unmitigated evil?

Ben,

Excellent book. Thank you for publishing it.

An alternative to above is the beanplot which I have found useful for comparing posteriors from Bayesian analyses. They are similar to the raindrop plots described by Barrowman and Myers (2001) but is contained in a nice R-package. I prefer them to violin or box plots since bimodality can be hidden with those approaches.

http://cran.r-project.org/web/packages/beanplot/index.html

I hope you find this useful to the debate.
Cheers,
Trevor

Barrowman, N.J. and R.A. Myers. A new graphical display for meta-analysis: the Raindrop Plot. Am. Stat.

notes on aic v. bic in sem

jebyrnes (guest) 1228354515|%e %b %Y, %H:%M %Z|agohover
in discussion Hidden / Per page discussions » AIC vs BIC

I've been working with structural equations a lot recently, and have noted large discrepancies quite often between the aic and bic values. Interestingly, the bic often selects for models that actually do not fit the data, but happen to be simpler - particularly when the effect sizes of many coefficients are small. It's made me wary of its usage. The tapering effects argument makes a lot of sense, particularly when applied to ecological data here.

AIC revisited

bbolker 1224275456|%e %b %Y, %H:%M %Z|agohover
in discussion Hidden / Per page discussions » AIC vs BIC

The BIC is consistent and was designed to identify the "true" dimensionality of an underlying model. The AIC is not consistent but has lower error: "if the number of models of the same dimension does not grow very fast in dimension, the average squared error of the model selected by AIC is asymptotically equivalent to the minimum offered by the candidate models … There has been a debate between AIC and BIC in the literature, centring on the issue of whether the true model is finite-dimensional or infinite-dimensional. There seems to be a consensus that, for the former case, BIC should be preferred, and AIC should be chosen for the latter" (Yang 2005). Furthermore, Yang 2005 shows (apparently: I haven't tried to follow the technical details) that you can't have your cake and eat it too — you have to make a decision between consistency and minimizing prediction errors.

Given that consensus I would say it usually makes more sense to think of infinite-dimensional models (or at least much higher dimension than the most complex of the models we try to fit) as being the default case for ecology, and therefore for AIC being preferred.

Yang, Yuhong. 2005. Can the strengths of AIC and BIC be shared? A conflict between model indentification and regression estimation. Biometrika 92, no. 4 (December 1): 937-950. doi:10.1093/biomet/92.4.937.

And the AIC?

Anonymous (128.40.24.x) 1221575401|%e %b %Y, %H:%M %Z|agohover
in discussion Hidden / Per page discussions » AIC vs BIC

The argument against the BIC above certainly makes sense but I don't know of any theory that tells us that the AIC is any better in that respect. It tries to find the number of nonzeroes as well, though in a more liberal and upward biased way… I don't know whether that works well as a justification of using the AIC…

If you want a theory that, instead of estimating an unknown and essentially unobservable underlying truth, gives you what you find "interesting" or "useful", you have to formalise your concept of "usefulness" as a new criterion. Particularly, don't expect to get anything objective or standard from it, because usefulness generally is not objective.

Christian Hennig

testing comments

bbolker 1218564331|%e %b %Y, %H:%M %Z|agohover
in discussion Hidden / Per page discussions » AIC vs BIC

just a test.