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: Marlies Vervloet, Henk A. L. Kiers, Wim Van den Noortgate, Eva Ceulemans
Title: PCovR: An R Package for Principal Covariates Regression
Abstract: In this article, we present PCovR, an R package for performing principal covariates regression (PCovR; De Jong and Kiers 1992). PCovR was developed for analyzing regression data with many and/or highly collinear predictor variables. The method simultaneously reduces the predictor variables to a limited number of components and regresses the criterion variables on these components. The flexibility, interpretational advantages, and computational simplicity of PCovR make the method stand out between many other regression methods. The PCovR package offers data preprocessing options, new model selection procedures, and several component rotation strategies, some of which were not available in R up till now. The use and usefulness of the package is illustrated with a real dataset, called psychiatrists.

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Paper: PCovR: An R Package for Principal Covariates Regression     Download PDF (Downloads: 2793)
PCovR_2.6.tar.gz: R source package Download (Downloads: 240; 18KB)
v65i08.R: R example code from the paper Download (Downloads: 348; 1KB)

DOI: 10.18637/jss.v065.i08

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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.