R help - Apr 2011 - area under roc curve

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

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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
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