This might shock some people, bit AIC does not give The Truth. If you have a model that fits almost as well, but is simpler, then I don't see a problem with using it. It's worth checking how much less of the variation is explain (e.g. using R^2), and also how different the fitted models are.
AIC has a tendency to give overly complex models (especially with lots of data), so I often use BIC instead, which tends too far in the other direction. Or, if the full model isn't too big, I don't bother with model selection, and report the full model. HTH Bob Bob O'Hara Tel: +49 69 798 40226 (in Germany) Mobile: +49 1515 888 5440 WWW: http://www.bik-f.de/root/index.php?page_id=219 Blog: http://blogs.nature.com/boboh/ Journal of Negative Results - EEB: www.jnr-eeb.org >>> Lee Davis <[email protected]> 12/01/10 23:44 PM >>> I have what might seem to be a simple question regarding AIC and parsimony, and yet the answers I have found on the subject are unsatisfactory. So, opinions please. Here is the scenario: Let's say that one is using AIC for the selection of nested models to avoid multiple LRT comparisons. Should you always choose the model with deltaAIC = 0 as the best? What if there is a model with deltaAIC <2 that has fewer terms? Should it be chosen in the pursuit of parsimony? Or should you report some support for both models? If so, what is the proper language in this case? Let's assume that we are avoiding model averaging. Thanks, Lee -- Lee Davis Graduate Assistant State University of New York College of Environmental Science & Forestry Department of Environmental & Forest Biology 452 Illick Hall, 1 Forestry Drive, Syracuse, NY 13210
