area under roc curve
ROC area does not measure goodness of prediction but does measure pure predictive discrimination. The generalization of the ROC area is the C-index for continuous or censored Y. See for example the rcorr.cens function in the Hmisc package. Frank
agent dunham wrote:
Dear all, I want to measure the goodness of prediction of my linear model. That's why I was thinking about the area under roc curve. I'm trying the following, but I don't know how to avoid the error. Any help would be appreciated. library(ROCR) model.lm <- lm(log(outcome)~log(v1)+log(v2)+factor1) pred<-predict(model.lm) pred<-prediction(as.numeric(pred), as.numeric(log(outcome))) auc<-performance(pred,"auc") Error en prediction(as.numeric(pred), as.numeric(log(outcome))) : Number of classes is not equal to 2. ROCR currently supports only evaluation of binary classification tasks. user at host.com
----- Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/area-under-roc-curve-tp3447420p3448377.html Sent from the R help mailing list archive at Nabble.com.