文章基本信息

标题：PCovR: An R Package for Principal Covariates Regression
本地全文：下载
作者：Marlies Vervloet ; Henk A. L. Kiers ; Wim Van den Noortgate 等
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2015
卷号：65
期号：1
页码：1-14
语种：English
出版社：University of California, Los Angeles
摘要：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.