PCovR-package: Principal Covariates Regression

Description

Analyzing regression data with many and/or highly collinear predictor variables, by simultaneously reducing the predictor variables to a limited number of components and regressing the criterion variables on these components. Several rotation options are provided in this package, as well as model selection options.

Arguments

Author

Marlies Vervloet (marlies.vervloet@ppw.kuleuven.be)

Details

This package contains the function pcovr, which runs a full PCovR analysis of a data set and provides several preprocessing, model selection, and rotation options. This function calls the function pcovr_est, which estimates the PCovR parameters given a specific weigthing parameter value and a particular number of components. This function was originally written in MATLAB by De Jong & Kiers (1992). Two illustrative data sets are included: alexithymia and psychiatrists.

References

S. de Jong, H.A.L. Kiers, Principal covariates regression: Part I. Theory, Chemom. intell. lab. syst 14 (1992) 155-164.

Marlies Vervloet, Henk A. Kiers, Wim Van den Noortgate, Eva Ceulemans (2015). PCovR: An R Package for Principal Covariates Regression. Journal of Statistical Software, 65(8), 1-14. URL http://www.jstatsoft.org/v65/i08/.

Examples

Run this code

data(alexithymia)
results <- pcovr(alexithymia$X, alexithymia$Y)
summary(results)
plot(results)