Lindsay Banin <l.bani...@leeds.ac.uk> wrote >Hi there, > >I am looking to compare nonlinear mixed effects models that have different >nonlinear functions (different types of growth curve)embedded. Most of the >literature I can find focuses on comparing nested models with likelihood >ratios and AIC. Is there a way to compare model fits when models are not >nested, i.e. when the nonlinear functions are not the same? >
What I like to do in these cases (and, in fact, for comparing model fits in general, even if nested) is graph the results of one vs the other, and of each vs. actual values. If the predicted values from the two models are very similar, then I can choose based on complexity or some other criterion; if they are not similar, then which is closer to the actual values? Is the difference large? Is it "worth it"? It's not a formal test, but I often find it illuminating. HTH Peter Peter L. Flom, PhD Statistical Consultant www DOT peterflomconsulting DOT com ______________________________________________ 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.
