On Jan 22, 2010, at 3:53 AM, Na'im R. Tyson wrote:

Dear R-philes,

I am plotting ROC curves for several cross-validation runs of a classifier (using the function below). In addition to the average AUC, I am interested in obtaining a confidence interval for the average AUC. Is there a straightforward way to do this via the ROCR package?

You should probably contact the authors. When I tried using that package a few weeks ago, several of the annotation features were broken. I contacted the author who said there had been problems after converting to S4 method. He also said there would be a fix but not immediately. There has been a release since that time and I tried it, but it did not appear to fix the problems I encountered. All I was able to get were very simple ROC curves without any confidence intervals or marking of levels. I ended up turning to the Epi package for what I needed ( but I did not need confidence intervals so cannot comment on that aspect.)

--
David.


plot_roc_curve <- function(roc.dat, plt.title) {
        #print(str(vowel.ROC))
        pred <- prediction(roc.dat$predictions, roc.dat$labels)
        perf <- performance(pred, "tpr", "fpr")
        perf.auc <- performance(pred, "auc")
        perf.auc.areas <- slot(perf.auc, "y.values")
        curve.area <- mean(unlist(perf.auc.areas))
        #quartz(width=4, height=6)
        plot(perf, col="grey82", lty=3)
        plot(perf,lwd=3,avg="horizontal",spread.estimate="boxplot",
                add=T)
        title(main=plt.title)
        mtext(sprintf("%s%1.4f", "Area under Curve = ", curve.area),
                side=3, line=0, cex=0.8)
}

P.S. After years of studying statistical analysis as a student, I still consider myself a novice.

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David Winsemius, MD
Heritage Laboratories
West Hartford, CT

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