Description Usage Arguments Details Value Examples

This function uses bootstrap to generate five types of equi-tailed two-sided confidence intervals of ROC-AUC with different required percentages and output a dataframe with AUCs, lower CIs, and higher CIs of all methods and groups.

1 | ```
roc_auc_with_ci(data, conf= 0.95, type='bca', R = 100)
``` |

`data` |
A data frame contains true labels of multiple groups and corresponding predictive scores. |

`conf` |
A scalar contains the required level of confidence intervals, and the default number is 0.95. |

`type` |
A vector of character strings includes five different types of equi-tailed two-sided nonparametric confidence intervals (e.g., "norm","basic", "stud", "perc", "bca"). |

`R` |
A scalar contains the number of bootstrap replicates, and the default number is 100. |

A data frame is required for this function as input. This data frame should contains true label (0 - Negative, 1 - Positive) columns named as XX_true (e.g. S1_true, S2_true and S3_true) and predictive scores (continuous) columns named as XX_pred_YY (e.g. S1_pred_SVM, S2_pred_RF). Predictive scores could be probabilities among [0, 1] and other continuous values. For each classifier, the number of columns should be equal to the number of groups of true labels. The order of columns won't affect results.

`norm` |
Using the normal approximation to calculate the confidence intervals. |

`basic` |
Using the basic bootstrap method to calculate the confidence intervals. |

`stud` |
Using the studentized bootstrap method to calculate the confidence intervals. |

`perc` |
Using the bootstrap percentile method to calculate the confidence intervals. |

`bca` |
Using the adjusted bootstrap percentile method to calculate the confidence intervals. |

1 2 | ```
data(test_data)
roc_auc_with_ci_res <- roc_auc_with_ci(test_data, conf= 0.95, type='bca', R = 100)
``` |

WandeRum/multiROC documentation built on May 17, 2019, 6:08 a.m.

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