Thanks for this, I had used
> validate(model0, method="boot",B=200) To get a index.corrected Brier score, However i am also wanting to bootstrap the predicted probabilities output from predict(model1, type = "response") to get a idea of confidence, or am i best just using se.fit = TRUE and then calculating the 95%CI? Does what i want to do make sense? Thanks On 29 Sep 2010, at 13:38, Frank Harrell wrote: Split sample validation is highly unstable with your sample size. The rms package can help with bootstrapping or cross-validation, assuming you have all modeling steps repreated for each resample. Frank ----- Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/Validation-Training-test-data-tp2718523p2718905.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.