WLogit-package {WLogit}R Documentation

Variable Selection in High-Dimensional Logistic Regression Models using a Whitening Approach

Description

It proposes a novel variable selection approach in classification problem that takes into account the correlations that may exist between the predictors of the design matrix in a high-dimensional logistic model. Our approach consists in rewriting the initial high-dimensional logistic model to remove the correlation between the predictors and in applying the generalized Lasso criterion.

Details

The DESCRIPTION file: This package was not yet installed at build time.
Index: This package was not yet installed at build time.
This package consists of functions: "WhiteningLogit", "CalculPx", "CalculWeight", "Refit_glm", "top", "top_thresh", "WorkingResp", and "Thresholding". For further information on how to use these functions, we refer the reader to the vignette of the package.

Author(s)

Wencan Zhu

Maintainer: Wencan Zhu <wencan.zhu@yahoo.com>

References

W. Zhu, C. Levy-Leduc, N. Ternes. "Variable selection in high-dimensional logistic regression models using a whitening approach". (2022)


[Package WLogit version 2.1 Index]