Plotting overlaid ROC curves I'm trying to make overlaid ROC curves to represent successive improvements in model performance when particular predictors are added one at a time to the model. I want one ROC curve for each of about 5 nested models (which I will define manually), all overlaid in one plot. For example:
    #outcome var
    y = c(rep(0,50), rep(1, 50))

    #predictors
    x1 = y + rnorm(100, sd = 1)
    x2 = y + rnorm(100, sd = 4)

    #correlations of predictors with outcome
    cor(x1, y)
    cor(x2, y)

    library(Epi)
    ROC(form = y ~ x1, plot = "ROC)
    ROC(form = y ~ x1 + x2, plot = "ROC")

I'd want the two ROC curves on the same plot (and ideally without the distracting model info in the background). Any ggplot/graphics gurus willing to lend a hand?
 A: The caTools package provides the colAUC function.  Use it and set the plotROC argument to TRUE.  I have been satisfied with the graphs it produces.
A: If you'd like to overlay the ROC curves over each other, you can use the roc function from the pROC R package to get the sensitivity and specificity values and plot them out manually, 
    #outcome var
    y = c(rep(0,50), rep(1, 50))
#predictors
x1 = y + rnorm(100, sd = 1)
x2 = y + rnorm(100, sd = 4)


model1 = glm(y ~ x1, family = binomial())
pred1 = predict(model1)
model2 = glm(y ~ x1 + x2, family = binomial())
pred2 = predict(model2)

library(pROC)
roc1 = roc(y, pred1)
roc2 = roc(y, pred2)

Specificity and Sensitivity Values
> str(roc1)
List of 15
\$ percent           : logi FALSE
\$ sensitivities     : num [1:101] 1 1 0.98 0.98 0.98 0.98 0.98 0.98 0.96...
\$ specificities     : num [1:101] 0 0.02 0.02 0.04 0.06 0.08 0.1 0.12 0.12 
 ...

or use the plot function as
plot(roc1, col = 1, lty = 2, main = "ROC")
plot(roc2, col = 4, lty = 3, add = TRUE)


Also, there is also the pROC::ggroc function for ggplot2 plotting abilities.
