e.roc: Calculating expected Receiver Operating Characteristics Curve...

In ROCS: Receiver Operating Characteristics Surface

Description

This function builds an eROC curve and returns the expected values of TPR, FPR, and TDR. It also calculates the AUC of the eROC curve. The 95% bootstrap percentile confidence interval for the AUC is provided.

Usage

e.roc(x, mu, method='RNA', bt.ci=TRUE, bt.nreps=100, do.plot=TRUE)

Arguments

x	Vector; the scores yielded by the classifier.
mu	Vector; the probabilistic confidence assigned by the imperfect reference standard.
method	The method used to compute the cumulative distribution function for the Poisson binomial distribution. “DFT-CF” for the DFT-CF method, “RF” for the recursive formula, “RNA” for the refined normal approximation, “NA” for the normal approximation.
bt.ci	Whether to compute the bootstrap confidence interval.
bt.nreps	The number of bootstrap replicates.
do.plot	Whether to plot the eROC curve.

Details

The eROC curve is a generalization of ROC curve given the class membership uncertainties. See the reference for the definition of the eROC curve.

Value

Returns the area under the eROC curve, the expected values of TPR, FPR, and TDR.

Author(s)

Peizhou Liao. Email: [e-mail].

References

Liao P, Wu H, and Yu T (2016). ROC Curve Analysis in the Presence of Imperfect Reference Standards. Stat Biosci doi:10.1007/s12561-016-9159-7.

Examples

## normal scores
sample.p <- rnorm(100, mean=1, sd=sqrt(2))  
sample.n <- rnorm(100, mean=-1, sd=sqrt(2))  

## probabilistic confidence
mu.p <- rbeta(100, shape1=5, shape2=1)
mu.n <- rbeta(100, shape1=1, shape2=5)

## combine the sample
x.sample <- c(sample.p, sample.n)
mu.sample <- c(mu.p, mu.n)

## build eROC curve
e.roc.fit <- e.roc(x=x.sample, mu=mu.sample)

ROCS documentation built on May 2, 2019, 9:42 a.m.