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PCovR: An R Package for Principal Covariates Regression

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

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.

Suggested Citation

  • Vervloet, Marlies & Kiers, Henk A. L. & Van den Noortgate, Wim & Ceulemans, Eva, 2015. "PCovR: An R Package for Principal Covariates Regression," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i08).
    • Handle: RePEc:jss:jstsof:v:065:i08
    DOI: http://hdl.handle.net/10.18637/jss.v065.i08
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    References listed on IDEAS

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    1. Edward Cureton & Stanley Mulaik, 1975. "The weighted varimax rotation and the promax rotation," Psychometrika, Springer;The Psychometric Society, vol. 40(2), pages 183-195, June.
    2. John Carroll, 1953. "An analytical solution for approximating simple structure in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 23-38, March.
    3. Henk Kiers, 1994. "Simplimax: Oblique rotation to an optimal target with simple structure," Psychometrika, Springer;The Psychometric Society, vol. 59(4), pages 567-579, December.
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