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:
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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)