I don't think its there with roc.test but you can use library(caTools)
to do it.
It is very easy to compare AUC values like below by using 'sapply' using library(pROC)
as well, I am describing both the methods here:
Example and Setup for both the methods:
Building the model here:
lm1 <- lm(am ~ disp + mpg, data= mtcars)
lm2 <- lm(am ~ disp + hp, data= mtcars)
lm3 <- lm(am ~ disp + wt, data= mtcars)
Predicting the model here:
predict1 <- predict(lm1, newdata=mtcars)
predict2 <- predict(lm2, newdata=mtcars)
predict3 <- predict(lm3, newdata=mtcars)
Method1:
library("caTools")
colAUC(cbind(predict1, predict2, predict3), mtcars$am, plotROC = T)
Output:
[,1] [,2] [,3]
0 vs. 1 0.8380567 0.9433198 0.9433198
If you choose to use plotROC = T
then you will receive a plot comparison between the ROCs
Method2:
auc.val <- sapply(list(predict1, predict2, predict3),function(x)roc(pred=x,resp=mtcars$am)$auc)
Finally calculating the AUC using sapply here:
library(pROC)
auc.val <- sapply(list(predict1, predict2, predict3),function(x)roc(pred=x,resp=mtcars$am)$auc)
would return this:
> auc.val
[1] 0.8380567 0.9433198 0.9433198
In case you are interested printing this with names , use USE.NAMES
in sapply
> auc.val <- sapply(list("lm1" = predict1, "lm2" = predict2,"lm3"= predict3),function(x)roc(pred=x,resp=mtcars$am)$auc, USE.NAMES = T)
> auc.val
lm1 lm2 lm3
0.8380567 0.9433198 0.9433198
roccomp
function. What are looking for exactly, a table with the AUC and 95% confidence intervals, a plot, a p value (with which H0)? – Calimo