`R/precrec.R`

`precrec.Rd`

The precrec package contains several functions and `S3`

generics to
provide a robust platform for performance evaluation of binary classifiers.

The precrec package provides the following six functions.

Function | Description |

`evalmod` | Main function to calculate evaluation measures |

`mmdata` | Reformat input data for performance evaluation calculation |

`join_scores` | Join scores of multiple models into a list |

`join_labels` | Join observed labels of multiple test datasets into a list |

`create_sim_samples` | Create random samples for simulations |

`format_nfold` | Create n-fold cross validation dataset from data frame |

The precrec package provides nine different `S3`

generics for the
`S3`

objects generated by the `evalmod`

function.

S3 generic | Library | Description |

`print` | base | Print the calculation results and the summary of the test data |

`as.data.frame` | base | Convert a precrec object to a data frame |

`plot` | graphics | Plot performance evaluation measures |

`autoplot` | ggplot2 | Plot performance evaluation measures with ggplot2 |

`fortify` | ggplot2 | Prepare a data frame for ggplot2 |

`auc` | precrec | Make a data frame with AUC scores |

`part` | precrec | Calculate partial curves and partial AUC scores |

`pauc` | precrec | Make a data frame with pAUC scores |

`auc_ci` | precrec | Calculate confidence intervals of AUC scores |

The `evalmod`

function calculates ROC and Precision-Recall
curves and returns an `S3`

object. The generated `S3`

object can
be used with several different `S3`

generics, such as `print`

and
`plot`

. The `evalmod`

function can also
calculate basic evaluation measures - error rate, accuracy, specificity,
sensitivity, precision, Matthews correlation coefficient, and F-Score.

The `mmdata`

function creates an input dataset for
the `evalmod`

function. The generated dataset contains
formatted scores and labels.

`join_scores`

and `join_labels`

are helper
functions to combine multiple scores and labels.

The `create_sim_samples`

function creates test datasets with
five different performance levels.

`plot`

takes an `S3`

object generated by `evalmod`

as input
and plot corresponding curves.

`autoplot`

uses `ggplot`

to plot curves.

`as.data.frame`

takes an `S3`

object generated by `evalmod`

as input and and returns a data frame with calculated curve points.

`auc`

and `pauc`

returns a data frame with AUC scores
and partial AUC scores, respectively. `auc_ci`

returns confidence intervals
of AUCs for both ROC and precision-recall curves.