Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Christian Thiele, Gerrit Hirschfeld
Title: cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R
Abstract: "Optimal cutpoints" for binary classification tasks are often established by testing which cutpoint yields the best discrimination, for example the Youden index, in a specific sample. This results in "optimal" cutpoints that are highly variable and systematically overestimate the out-of-sample performance. To address these concerns, the cutpointr package offers robust methods for estimating optimal cutpoints and the out-of-sample performance. The robust methods include bootstrapping and smoothing based on kernel estimation, generalized additive models, smoothing splines, and local regression. These methods can be applied to a wide range of binary-classification and cost-based metrics. cutpointr also provides mechanisms to utilize user-defined metrics and estimation methods. The package has capabilities for parallelization of the bootstrapping, including reproducible random number generation. Furthermore, it is pipe-friendly, for example for compatibility with functions from tidyverse. Various functions for plotting receiver operating characteristic curves, precision recall graphs, bootstrap results and other representations of the data are included. The package contains example data from a study on psychological characteristics and suicide attempts suitable for applying binary classification algorithms.

Page views:: 865. Submitted: 2018-04-19. Published: 2021-06-30.
Paper: cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R     Download PDF (Downloads: 288)
Supplements:
cutpointr_1.1.1.tar.gz: R source package Download (Downloads: 11; 1MB)
v98i11.R: R replication code Download (Downloads: 35; 29KB)

DOI: 10.18637/jss.v098.i11

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.