Frank, Thats great thanks for the advice, i appreciate that brier score, AUC etc are a better method of validation and discrimination but when it comes to predictions of new data
> d <- data.frame(x1=c(.1,.5),x2=c(.5,.15)) > predict(f, d, type="fitted.ind") > > y=good y=better y=best > 1 0.3199710 0.3560355 0.3239935 > 2 0.4153257 0.3437086 0.2409657 > > predict mean(y) using codes 1,2,3 > > >> predict(f, d, type='mean', codes=TRUE) > > 1 2 > 2.004022 1.825640 How do i use this information to assign x1 and x2 into a category on the response scale (good,better,best?) Thanks John On 1 Oct 2010, at 12:14, Frank Harrell wrote: John, Don't conclude that one category is the most probable when its probability of being equaled or exceeded is a maximum. The first category would always be the winner if that were the case. When you say y=best remember that you are dealing with a probability model. Nothing is forcing you to classify an observation, and unless the category's probability is high, this may be dangerous. You might do well to consider a more smooth approach such as using the generalized roc area (C-index) or its related rank correlation measure Dxy. Also there are odds ratios. Frank ----- Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/Interpreting-the-example-given-by-Frank-Harrell-in-the-predict-lrm-Design-help-tp2883311p2891623.html Sent from the R help mailing list archive at Nabble.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. ______________________________________________ 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.