PCovR: An R Package for Principal Covariates Regression

Marlies Vervloet, Henk A. L. Kiers, Wim Van den Noortgate and Eva Ceulemans

Journal of Statistical Software, 2015, vol. 065, issue i08

Abstract: In this article, we present PCovR, an R package for performing principal covariates regression (PCovR; De Jong and Kiers'92). 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.

Date: 2015-06-21
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Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:065:i08

DOI: 10.18637/jss.v065.i08

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