# Compare two NP classification methods at different thresholds.

### Description

`compare`

compares NP classification methods and provide the regions where one method is better than the other. The two NP-ROC curves are required to have the same alpha value sequence as well as having conf = TRUE.

### Usage

1 |

### Arguments

`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'. |

### Value

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. |

### References

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.

### See Also

`npc`

, `nproc`

, `predict.npc`

and `plot.nproc`

### Examples

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