What answers have you found and why were they unsatisfactory? First, use AICc, not AIC. See Burnham and Anderson 2002 or Anderson 2008 (Primer).
Second, I think most all literature is rather clear that models with deltaAICc < 2 are basically equivalent. So of course you should report support for models in that group, however many there may be. There are plenty of examples in the literature of language to accompany such situations. Finally, if you have this kind of model selection uncertainty, ignoring the advantages of multimodel inference, including model-averaging, is a rather strange decision. Dave Hewitt http://profile.usgs.gov/dhewitt --------------------------------------------------------------------- From: Lee Davis <[email protected]> Subject: AIC and Occam's Razor 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
