On Aug 4, 2010 8:13pm, Chris Howden <ch...@trickysolutions.com.au> wrote: > Hi Chris,
> If u want good predictive ability, which is exactly what u do want when > using a model for prediction, then why not use its predictive ability as a > model selection criteria? Because this will typically lead to overfitting the data, ie getting a great fit to the 'training' set but then doing miserably on future data? Unless you do something like split the data set into a training and a validation set, or use cross-validation (which is a more sophisticated version of the same idea), just finding the model with the best predictive capability on a specified data set will *not* give you a good model in general. That's why approaches such as AIC, corrected R^2, and so forth, include a penalty for model complexity. Unless I'm missing something really obvious, in which case I apologize. Ben Bolker [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology