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.
u...@host.com
-
Frank Harrell
Department of Biostatistics, Vanderbilt University
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