WeightedAUC: WeightedAUC

In WeightedROC: Fast, Weighted ROC Curves

Description

Calculate the exact area under the ROC curve.

Usage

WeightedAUC(tpr.fpr)

Arguments

tpr.fpr

Output of WeightedROC: data.frame with the true positive rate (TPR) and false positive rate (FPR).

Value

Numeric scalar.

Author(s)

Toby Dylan Hocking

Examples

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)
}

WeightedROC documentation built on Feb. 1, 2020, 9:07 a.m.