High-Dimensional Regression and CAR Score Variable Selection

The "care" package implements the regression approach of Zuber and Strimmer (2011). CAR scores measure the correlation between the response and the Mahalanobis-decorrelated predictors. The squared CAR score is a natural measure of variable importance and provides a canonical ordering of variables. This package provides functions for estimating CAR scores, for variable selection using CAR scores, and for estimating corresponding regression coefficients. Both shrinkage as well as empirical estimators are available.

Current Version: 1.1.2

Authors: Verena Zuber and Korbinian Strimmer.

Documentation and Installation:

• Manual (pdf file) and release history.
• Download of care version 1.1.2 source package.
• Archive of previous versions of care.
• Licensed under the GNU GPL version 3 (or any later version).

Methodology Paper with R Code:

• V. Zuber and K. Strimmer. 2011. High-dimensional regression and variable selection using CAR scores. Statist. Appl. Genet. Mol. Biol. 10: 34. (reprint, arXiv:1007.5516).
• R code for reproducing the simulations described in the 2011 methodology paper.
• CAR regression models on diabetes data (click on image to enlarge and see here for the R code):
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• Comparison of regression models based CAR scores and marginal correlations for brain data (click on image to enlarge and see here for the R code):
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GAW17 Case Study: Simultaneous SNP Selection with CAR Scores

• V. Zuber, A. P. Duarte Silva, and K. Strimmer. 2012. An efficient approach to simultaneous SNP selection: A case study on GAW17 data.
  (arXiv:1203.3082 )
• Comparison of SNP rankings for phenotype Q1 (click on image to enlarge):
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• R code for reproducing the results in the GAW17 SNP analysis paper.

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