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
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Authors: Niki Zumbrunnen, Lutz Dümbgen
Title: pvclass: An R Package for p Values for Classification
Abstract: Let (X, Y) be a random variable consisting of an observed feature vector X and an unobserved class label Y ∈ {1, 2, . . . , L} with unknown joint distribution. In addition, let D be a training data set consisting of n completely observed independent copies of (X, Y). Instead of providing point predictors (classifiers) for Y , we compute for each b ∈ {1, 2, . . . , L} a p value π_b (X, D) for the null hypothesis that Y = b, treating Y temporarily as a fixed parameter, i.e., we construct a prediction region for Y with a certain confidence. The advantages of this approach over more traditional ones are reviewed briefly. In principle, any reasonable classifier can be modified to yield nonparametric p values. We describe the R package pvclass which computes nonparametric p values for the potential class memberships of new observations as well as cross-validated p values for the training data. Additionally, it provides graphical displays and quantitative analyses of the p values.

Page views:: 2034. Submitted: 2014-07-04. Published: 2017-06-05.
Paper: pvclass: An R Package for p Values for Classification     Download PDF (Downloads: 1835)
pvclass_1.4.tar.gz: R source package Download (Downloads: 136; 163KB)
v78i04.R: R replication code Download (Downloads: 189; 25KB)

DOI: 10.18637/jss.v078.i04

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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.