PCovR: An R Package for Principal Covariates Regression

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Authors Marlies Vervloet, Henk A. L. Kiers, Wim van den Noortgate, Eva Ceulemans
Journal/Conference Name Journal of Statistical Software
Paper Category
Paper 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 of publication 2015
Code Programming Language R

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