PCovR: An R Package for Principal Covariates Regression
View Researcher's Other CodesDisclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).
Authors | Marlies Vervloet, Henk A. L. Kiers, Wim van den Noortgate, Eva Ceulemans |
Journal/Conference Name | Journal of Statistical Software |
Paper Category | Other |
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 |
Comment |