Bert Gunter <gunter.berton <at> gene.com> writes: > > John: > > 1. As always, and as requested (see posting guide), a small > reproducible example might help.
Bert is right that things aren't well defined. However, AIC is still *widely* used for nonlinear models. For the sloppy folks among us, here are some useful (?) pieces of information: 1. nls counts the variance of the residual error as a parameter 2. As long as you compute AIC *within the same framework* (e.g. comparing nls fits to each other, or glm fits, or nlme fits ...) these decisions are generally made consistently. Comparing across model types requires attention to detail to make sure that parameters are counted using similar rules, and that additive constants are consistently included or not. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.