On 1 September 2013 11:31, Frank von Delft <[email protected]>wrote:
> >> 2. >> I'm struck by how small the improvements in R/Rfree are in Diederichs & >> Karplus (ActaD 2013, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3689524/); >> the authors don't discuss it, but what's current thinking on how to >> estimate the expected variation in R/Rfree - does the Tickle formalism >> (1998) still apply for ML with very weak data? >> > Frank, another point just occurred to me: the main reason for using Rfree as a model selection criterion is to detect overfitting in cases where you're comparing models with different numbers of parameters. That doesn't apply here since you're comparing the same model. In that case you would be much better off comparing Rwork since it has a much lower variance than Rfree (in fact lower by a factor of 19 if you use the usual 5% of reflections for the test set). Cheers -- Ian
