
# Precrec <img src="man/figures/logo.png" align="right" alt="" width="100" />

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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](https://cran.r-project.org/package=Rcpp).

## Documentation

- [Package website](https://evalclass.github.io/precrec/) – GitHub pages
  that contain all precrec documentation.

- [Introduction to
  precrec](https://evalclass.github.io/precrec/articles/introduction.html)
  – 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](https://evalclass.github.io/precrec/articles/introduction.html).

- [Help pages](https://evalclass.github.io/precrec/reference/) – 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](https://evalclass.github.io/precrec/reference/).

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

- Alternatively, you can install a development version of `precrec` from
  [our GitHub repository](https://github.com/evalclass/precrec/). To
  install it:

  1.  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).

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

  3.  Install `precrec` from the GitHub repository with
      `devtools::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 |
|:---|:---|:---|
| 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 |

## 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.

``` r
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.

``` r
# The ggplot2 package is required
library(ggplot2)

# Show ROC and Precision-Recall plots
autoplot(sscurves)
```

![](https://raw.githubusercontent.com/evalclass/precrec/main/README_files/figure-gfm/unnamed-chunk-2-1.png)

## 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](https://doi.org/10.1093/bioinformatics/btw570)

## External links

- [Classifier evaluation with imbalanced
  datasets](https://classeval.wordpress.com/) - 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](https://doi.org/10.1371/journal.pone.0118432) - our paper
  that summarized potential pitfalls of ROC plots with imbalanced
  datasets and advantages of using precision-recall plots instead.
