Calculate Accurate Precision-Recall and ROC (Receiver Operator Characteristics).
Precrec 
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.
Documentation
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
printhave their own help pages with plenty of examples. View the main help page withhelp(package = "precrec")in R. The HTML version is also available on the GitHub Pages.
Six key features of precrec
1. Accurate curve calculations
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.
2. Super fast
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.
3. Various evaluation metrics
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
4. Confidence interval band
precrec calculates confidence intervals when multiple test sets are given. It automatically shows confidence bands about the averaged curve in the corresponding plot.
5. Calculation of partial AUCs and visualization of partial curves
precrec calculates partial AUCs for specified x and y ranges. It can also draw partial ROC and precision-recall curves for the specified ranges.
6. Supporting functions
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
Installation
Install the release version of
precrecfrom CRAN withinstall.packages("precrec").Alternatively, you can install a development version of
precrecfrom 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
devtoolsfrom CRAN withinstall.packages("devtools").Install
precrecfrom the GitHub repository withdevtools::install_github("evalclass/precrec").
Functions
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 |
Examples
Following two examples show the basic usage of precrec functions.
ROC and Precision-Recall calculations
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
sscurves <- evalmod(scores = P10N10$scores, labels = P10N10$labels)
Visualization of the curves
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)
Citation
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
External links
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.