`compare`

compares NP classification methods and provide the regions where one method is better than the other. The two NP-ROC curves are both required to have band = TRUE.

1 |

`roc1` |
the first nproc object. |

`roc2` |
the second nproc object. |

`plot` |
whether to generate the two NP-ROC plots and mark the area of significant difference. Default = 'TRUE'. |

`col1` |
the color of the region where roc1 is significantly better than roc2. Default = 'black'. |

`col2` |
the color of the region where roc2 is significantly better than roc1. Default = 'red'. |

A list with the following items.

`alpha1` |
the alpha values where roc1 is significantly better than roc2. |

`alpha2` |
the alpha values where roc2 is significantly better than roc1. |

`alpha3` |
the alpha values where roc1 and roc2 are not significantly different. |

`confidence` |
represents the confidence level. |

Xin Tong, Yang Feng, and Jingyi Jessica Li (2016), Neyman-Pearson (NP) classification algorithms and NP receiver operating characteristic (NP-ROC) curves, manuscript, http://arxiv.org/abs/1608.03109.

`npc`

, `nproc`

, `predict.npc`

and `plot.nproc`

1 2 3 4 5 6 7 8 9 |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.