pvclass: An R Package for p Values for Classification

Zumbrunnen, Niki; Dümbgen, Lutz (2017). pvclass: An R Package for p Values for Classification. Journal of statistical software, 78(4), pp. 1-19. UCLA Statistics 10.18637/jss.v078.i04

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Let (X,Y) be a random variable consisting of an observed feature vector X and an unobserved class label Y ∈ {12,...,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 ∈ {12,...,L} ap value πb (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 pvalues. We describe the R package pvclass which computes nonparametric p values for the potential class memberships of new observations as well as cross-validated pvalues for the training data. Additionally, it provides graphical displays and quantitative analyses of the p values.

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