plotROC(
  x,
  models,
  times,
  xlab = "1-Specificity",
  ylab = "Sensitivity",
  col,
  lwd,
  lty = 1,
  cex = 1,
  pch = 1,
  legend = TRUE,
  auc.in.legend = TRUE,
  brier.in.legend = FALSE,
  add = FALSE,
  ...
)

## binary
set.seed(18)
if (requireNamespace("randomForest",quietly=TRUE)){
library(randomForest)
library(prodlim)
bdl <- sampleData(40,outcome="binary")
bdt <- sampleData(58,outcome="binary")
bdl[,y:=factor(Y)]
bdt[,y:=factor(Y)]
fb1 <- glm(y~X1+X2+X3+X4+X5+X6+X7+X8+X9+X10,data=bdl,family="binomial")
fb2 <- randomForest(y~X1+X2+X3+X4+X5+X6+X7+X8+X9+X10,data=bdl)
xb <- Score(list("glm"=fb1,"rf"=fb2),y~1,data=bdt,
            plots="roc",metrics=c("auc","brier"))
plotROC(xb,brier.in.legend=1L)

# with cross-validation
## Not run: 
xb3 <- Score(list("glm"=fb1,"rf"=fb2),y~1,data=bdl,
            plots="roc",B=3,split.method="bootcv",
            metrics=c("auc"))

## End(Not run)
}
## survival
set.seed(18)
library(survival)
sdl <- sampleData(40,outcome="survival")
sdt <- sampleData(58,outcome="survival")
fs1 <- coxph(Surv(time,event)~X3+X5+X6+X7+X8+X10,data=sdl,x=TRUE)
fs2 <- coxph(Surv(time,event)~X1+X2+X9,data=sdl,x=TRUE)
xs <- Score(list(model1=fs1,model2=fs2),Hist(time,event)~1,data=sdt,
            times=5,plots="roc",metrics="auc")
plotROC(xs)
## competing risks
data(Melanoma)
f1 <- CSC(Hist(time,status)~age+sex+epicel+ulcer,data=Melanoma)
f2 <- CSC(Hist(time,status)~age+sex+logthick+epicel+ulcer,data=Melanoma)
x <- Score(list(model1=f1,model2=f2),Hist(time,status)~1,data=Melanoma,
            cause=1,times=5*365.25,plots="roc",metrics="auc")
plotROC(x)