# 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

 `1` ```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: pliao3@emory.edu.

## 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```## 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.