The aim of the `precrec`

package is to provide an
integrated platform that enables robust performance evaluations of
binary classifiers. Specifically, `precrec`

offers accurate
calculations of ROC (Receiver Operator Characteristics) and
precision-recall curves. All the main calculations of
`precrec`

are implemented with C++/Rcpp.

Package website – GitHub pages that contain all precrec documentation.

Introduction to precrec – a package vignette that contains the descriptions of the functions with several useful examples. View the vignette with

`vignette("introduction", package = "precrec")`

in R. The HTML version is also available on the GitHub Pages.Help pages – all the functions including the S3 generics except for

`print`

have their own help pages with plenty of examples. View the main help page with`help(package = "precrec")`

in R. The HTML version is also available on the GitHub Pages.

`precrec`

provides accurate precision-recall curves.

- Non-linear interpolation
- Elongation to the y-axis to estimate the first point when necessary
- Use of score-wise threshold values instead of fixed bins

`precrec`

also calculates AUC scores with high
accuracy.

`precrec`

calculates curves in a matter of seconds even
for a fairly large dataset. It is much faster than most other tools that
calculate ROC and precision-recall curves.

In addition to precision-recall and ROC curves, `precrec`

offers basic evaluation measures.

- Error rate
- Accuracy
- Specificity
- Sensitivity, true positive rate (TPR), recall
- Precision, positive predictive value (PPV)
- Matthews correlation coefficient
- F-score

`precrec`

calculates confidence intervals when multiple
test sets are given. It automatically shows confidence bands about the
averaged curve in the corresponding plot.

`precrec`

calculates partial AUCs for specified x and y
ranges. It can also draw partial ROC and precision-recall curves for the
specified ranges.

`precrec`

provides several useful functions that lack in
most other evaluation tools.

- Handling multiple models and multiple test sets
- Handling tied scores and missing scores
- Pre- and post-process functions of simple data preparation and curve analysis

Install the release version of

`precrec`

from CRAN with`install.packages("precrec")`

.Alternatively, you can install a development version of

`precrec`

from our GitHub repository. To install it:Make sure you have a working development environment.

**Windows**: Install Rtools (available on the CRAN website).**Mac**: Install Xcode from the Mac App Store.**Linux**: Install a compiler and various development libraries (details vary across different flavors of Linux).

Install

`devtools`

from CRAN with`install.packages("devtools")`

.Install

`precrec`

from the GitHub repository with`devtools::install_github("evalclass/precrec")`

.

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 |

Moreover, the `precrec`

package provides nine S3 generics
for the S3 object created by the `evalmod`

function.
**N.B.** The R language specifies S3 objects and S3 generic
functions as part of the most basic object-oriented system in R.

S3 generic | Package | Description |
---|---|---|

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 |

Following two examples show the basic usage of `precrec`

functions.

The `evalmod`

function calculates ROC and Precision-Recall
curves and returns an S3 object.

```
library(precrec)
# Load a test dataset
data(P10N10)
# Calculate ROC and Precision-Recall curves
<- evalmod(scores = P10N10$scores, labels = P10N10$labels) sscurves
```

The `autoplot`

function outputs ROC and Precision-Recall
curves by using the `ggplot2`

package.

```
# The ggplot2 package is required
library(ggplot2)
# Show ROC and Precision-Recall plots
autoplot(sscurves)
```

*Precrec: fast and accurate precision-recall and ROC curve
calculations in R*

Takaya Saito; Marc Rehmsmeier

Bioinformatics 2017; 33 (1): 145-147.

doi: 10.1093/bioinformatics/btw570

Classifier evaluation with imbalanced datasets - our web site that contains several pages with useful tips for performance evaluation on binary classifiers.

The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets - our paper that summarized potential pitfalls of ROC plots with imbalanced datasets and advantages of using precision-recall plots instead.