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

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Documentation for package ‘WLogit’ version 2.1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

WLogit-package	Variable Selection in High-Dimensional Logistic Regression Models using a Whitening Approach
beta	True coefficients in the esample.
CalculPx	Calculate the class-conditional probabilities.
CalculWeight	Calculate the weight
Refit_glm	Refit the logistic regression with chosen variables
test	WLogit output
Thresholding	Thresholding on a vector
top	Thresholding to zero of the smallest values
top_thresh	Thresholding to a given threshold of the smallest values
WhiteningLogit	Variable selection in high-dimensional logistic regression models using a whitening approach
WLogit	Variable Selection in High-Dimensional Logistic Regression Models using a Whitening Approach
WorkingResp	Calculate the working response
X	Example of a design matrix of a logistic model
y	Example of a binary response variable of a logistic model.