Description Details Abbreviations Functions Dataset Installing and using Author(s) See Also

Tools for estimating Receiver Operating Characteristic (ROC) curves, building confidence bands, comparing several curves both for dependent and independent data, estimating the cumulative-dynamic ROC curve in presence of censored data, and performing meta-analysis studies, among others.

The basic function of the nsROC package is the `gROC`

function. It
will estimate an ROC curve under one of these considerations: larger values of
the marker are associated with a higher probability of being positive (right-sided),
the opposite (left-sided) or when both smaller and larger values of the marker
are associated with having more probability of being positive (both).

Confidence bands for an ROC curve estimate resulting of the previous function can
be computed and displayed by the `ROCbands`

function. Three different
methods are provided to compute them.

Several paired or unpaired ROC curves can be compared with the `compareROCdep`

or `compareROCindep`

function, respectively. In order to compare ROC curves
different statistics can be used, and to approximate the distribution of the statistic in
the paired case both permutation and bootstrap procedures are computed.

Time-dependent ROC curves can be estimated by the cumulative/dynamic approach using
the `cdROC`

function. In order to deal with the right censored problem three
different statistics can be considered.

Meta-analysis of ROC curves following a non-parametric approach can be performed with
the `metaROC`

function. Both the fixed-effects and random-effects model can
be considered.

The following abbreviations are frequently used in this package:

ROC: Receiver Operating Characteristic

AUC: Area Under the (ROC) Curve

Sp: Specificity

Se: Sensitivity

TPR: True-Positive Rate

FPR: False-Positive Rate

`gROC` | ROC curve estimate (generalization included) |

`ROCbands` | Confidence bands for ROC curves |

`compareROCdep` | Comparison of k paired ROC curves |

`compareROCindep` | Comparison of k independent ROC curves |

`cdROC` | Cumulative/dynamic ROC curve estimate |

`metaROC` | Non-parametric ROC curve estimate for meta-analysis |

`plot` | Plot an ROC curve |

`plot` | Plot confidence bands for an ROC curve |

`plot` | Plot a time-dependent ROC curve |

`print` | Print a `groc` object |

`print` | Print a `rocbands` object |

`print` | Print a `cdroc` object |

`checkROC` | Check the data to compute an ROC curve (internal function) |

This package comes with a dataset of 9 papers (meta-analysis) with the number of TP
(true positive), FP (false positive), TN (true negative) and FN (false negative)
about the use of the Interleukin6 (IL6) as a marker for the early detection of
neonatal sepsis: `interleukin6`

.

To install this package:

1 2 | ```
install.packages("nsROC")
``` |

To load the package:

1 2 |

Sonia Perez-Fernandez

Maintainer: Sonia Perez Fernandez <uo217889@uniovi.es>

CRAN packages sde and survival employed in this package.

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