R: Calculating expected Receiver Operating Characteristics Curve...

e.roc {ROCS} | R Documentation

## Calculating expected Receiver Operating Characteristics Curve in the presence of imperfect reference standards.

### 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: 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

    ## 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)

[Package ROCS version 1.3 Index]