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.
|License:||GPL (>= 2)|
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:
Marlies Vervloet ([email protected])
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/.
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