Ramón Casero Cañas wrote: > Frank E Harrell Jr wrote: > >>This makes me think you are trying to go against maximum likelihood to >>optimize an improper criterion. Forcing a single cutpoint to be chosen >>seems to be at the heart of your problem. There's nothing wrong with >>using probabilities and letting the utility possessor make the final >>decision. > > > I agree, and in fact I was thinking along those lines, but I also needed > a way of evaluating how good is the model to discriminate between > abnormal and normal cases, as opposed to e.g. GOF. The only way I know > of is using area under ROC (thus setting cut-off points), which also > followed neatly from Michael Dewey comments. Any alternatives would be > welcome :) >
To get the ROC area you don't need to do any of that, and as you indicated, it is a good discrimination measure. The lrm function in the Design package gives it to you automatically (C index), and you can also get it with the Hmisc package's somers2 and rcorr.cens functions. ROC area is highly related to the Wilcoxon 2-sample test statistic for comparing cases and non-cases. -- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html