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Introduction

rmetrics is a R package to calculate a large variety of metrics to evaluate machine learning models and statistical models. It is primarily designed as a backend to my package revaluate, but the large number of provided metrics makes it useful as a standalone package.

The package provides classification metrics for binary, multiclass or ordered dependent variables, with the special case of binary dependent variables that are evaluated using upper and lower cutoffs and a grey area in between.

All functions in the package provide several methods based on the class of the first input variable. In most cases, the functions work with raw counts from the confusion matrix (TP, FP, TN, FN etc), a table object or a data.frame.

The package is under active development! Function names or arguments might change unannounced, methods might be added or even removed. Do not use it in a production environment (yet)!

Installation

rmetrics is not yet on CRAN. You can install the package via github:

devtools::install_github("jonas-schropp/rmetrics")

library(rmetrics)

Usage

Let’s calculate Aickin’s alpha as an example:

# Simulate some data
pred <- c(rep(1, 50), rep(2, 50), rep(3, 50))
ref <- c(rep(1, 25), rep(2, 5), rep(3, 20),
               rep(2, 40), rep(1, 5), rep(3, 5),
               rep(3, 30), rep(1, 10), rep(2, 10))

tbl <- table(pred, ref)

calc_aickin(tbl = tbl)

##     alpha        ll        ul 
## 0.4509621 0.3358289 0.5660953

We could also use a data.frame object as input and set the CI to 99%:

calc_aickin(
  data.frame(pred, ref),
  prediction = "pred",
  reference = "ref",
  ci.level = 0.99
  )

##     alpha        ll        ul 
## 0.4509621 0.2996514 0.6022728

Or not request a CI at all:

calc_aickin(tbl, ci.type = FALSE)

##     alpha        ll        ul 
## 0.4509621        NA        NA

Most functions also support raw counts for the entries of the confusion matrix as inputs:

calc_tnr(tn = 275, fp = 8, 
         ci.type = "clopper-pearson", 
         ci.level = 0.95)

## [1] 0.9717314 0.9450616 0.9877183

Available metrics

For a full list of functions, see the reference manual.

Contribute to this project

If you want to contribute to this project, please get in touch!

If there is a metric you want to see in this package that is not available, please either submit a feature request or implement it yourself and submit a pull request.

Planned features

This package is under active development. The current focus for development is 1) to implement more metrics for ordinal classifiers, 2) to add confidence interval calculations for more of the metrics and 3) to add much more documentation and references.