Nothing
WeightedAUC <- structure(function
### Calculate the exact area under the ROC curve.
(tpr.fpr
### Output of WeightedROC: data.frame with the true positive rate
### (TPR) and false positive rate (FPR).
){
stopifnot(is.data.frame(tpr.fpr))
stopifnot(nrow(tpr.fpr) > 1)
for(var.name in c("TPR", "FPR")){
## Ensure that the curve is sorted in decreasing order.
stopifnot(diff(tpr.fpr[[var.name]]) <= 0)
}
right <- tpr.fpr[-nrow(tpr.fpr),]
left <- tpr.fpr[-1,]
width <- right$FPR - left$FPR
rect.area <- left$TPR * width
triangle.h <- right$TPR - left$TPR
triangle.area <- triangle.h * width / 2
my.auc <- sum(rect.area, triangle.area)
my.auc
### Numeric scalar.
}, ex=function(){
library(WeightedROC)
## Compute the AUC for this weighted data set.
y <- c(0, 0, 1, 1, 1)
w <- c(1, 1, 1, 4, 5)
y.hat <- c(1, 2, 3, 1, 1)
tp.fp <- WeightedROC(y.hat, y, w)
(wauc <- WeightedAUC(tp.fp))
## For the un-weighted ROCR example data set, verify that our AUC is
## the same as that of ROCR/pROC.
if(require(microbenchmark) && require(ROCR) && require(pROC)){
data(ROCR.simple, envir=environment())
microbenchmark(WeightedROC={
tp.fp <- with(ROCR.simple, WeightedROC(predictions, labels))
wroc <- WeightedAUC(tp.fp)
}, ROCR={
pred <- with(ROCR.simple, prediction(predictions, labels))
rocr <- performance(pred, "auc")@y.values[[1]]
}, pROC={
proc <- pROC::auc(labels ~ predictions, ROCR.simple, algorithm=2)
}, times=10)
rbind(WeightedROC=wroc, ROCR=rocr, pROC=proc) #same
}
## For the un-weighted pROC example data set, verify that our AUC is
## the same as that of ROCR/pROC.
data(aSAH, envir=environment())
table(aSAH$s100b)
if(require(microbenchmark)){
microbenchmark(WeightedROC={
tp.fp <- with(aSAH, WeightedROC(s100b, outcome))
wroc <- WeightedAUC(tp.fp)
}, ROCR={
pred <- with(aSAH, prediction(s100b, outcome))
rocr <- performance(pred, "auc")@y.values[[1]]
}, pROC={
proc <- pROC::auc(outcome ~ s100b, aSAH, algorithm=2)
}, times=10)
rbind(WeightedROC=wroc, ROCR=rocr, pROC=proc)
}
})
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