With respect to Hal Caswell's comment that: "First, the idea of thinking about a model with almost the same AIC (or, better, AICc) but fewer terms, in pursuit of "parsimony" is doing parsimony twice. The AIC already accounts for the relative number of parameters. If the model with fewer parameters has a worse AIC, the result is saying that the better model is better even though it has more parameters."
Models within 2 AICc units of one another have virtually the same level of support from the data, so in my view it still makes sense to proceed with the simpler model. By "proceed" I mean estimating and reporting parameters and confidence intervals. When I've taken the time to estimate parameter confidence intervals on simple and more complex models within 2 AICc units of each other, I've usually found that at least one of the parameter confidence intervals of the complex models encompasses zero. And one feels pretty silly trying on the one hand, to claim to have found support for a model, yet on the other hand having to admit that one can't distinguish one of its parameters from zero. Dr. Seth W. Bigelow Biologist, USDA-FS Pacific Southwest Research Station 1731 Research Park Drive, Davis California [email protected] / ph. 530 759 1718
