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: Patrick M. Schnell, Mark Fiecas, Bradley P. Carlin
Title: credsubs: Multiplicity-Adjusted Subset Identification
Abstract: Subset identification methods are used to select the subset of a covariate space over which the conditional distribution of a response has certain properties - for example, identifying types of patients whose conditional treatment effect is positive. An often critical requirement of subset identification methods is multiplicity control, by which the family-wise Type I error rate is controlled, rather than the Type I error rate of each covariate-determined hypothesis separately. The credible subset (or credible subgroup) method provides a multiplicity-controlled estimate of the target subset in the form of two bounding subsets: one which entirely contains the target subset, and one which is entirely contained by it. We introduce a new R package, credsubs, which constructs credible subset estimates using a sample from the joint posterior distribution of any regression model, a description of the covariate space, and a function mapping the parameters and covariates to the subset criterion. We demonstrate parametric and nonparametric applications of the package to a clinical trial dataset and a neuroimaging dataset, respectively.

Page views:: 872. Submitted: 2017-08-23. Published: 2020-09-02.
Paper: credsubs: Multiplicity-Adjusted Subset Identification     Download PDF (Downloads: 172)
Supplements:
credsubs_1.1.1.tar.gz: R source package Download (Downloads: 8; 1MB)
v94i07.R: R replication code Download (Downloads: 13; 9KB)
slice32.RData: Supplementary data (R binary format) Download (Downloads: 11; 1MB)

DOI: 10.18637/jss.v094.i07

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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.