Description Usage Arguments Details Value Author(s) References Examples

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.

1 |

`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. |

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

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

Peizhou Liao. Email: [email protected]

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.

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)
``` |

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