SignifReg-package {SignifReg}

Consistent Significance Controlled Variable Selection in Generalized Linear Regression

Description

Provides significance controlled variable selection algorithms with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, PRESS, or p-value). The algorithm selects a final model with only significant variables defined as those with significant p-values after multiple testing correction such as Bonferroni, False Discovery Rate, etc. See Zambom and Kim (2018) <doi:10.1002/sta4.210>.

Details

The DESCRIPTION file:

Package:	SignifReg
Type:	Package
Title:	Consistent Significance Controlled Variable Selection in Generalized Linear Regression
Version:	4.3
Date:	2022-03-21
Imports:	car
Author:	Jongwook Kim, Adriano Zanin Zambom
Maintainer:	Adriano Zanin Zambom <[e-mail]>
Description:	Provides significance controlled variable selection algorithms with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, PRESS, or p-value). The algorithm selects a final model with only significant variables defined as those with significant p-values after multiple testing correction such as Bonferroni, False Discovery Rate, etc. See Zambom and Kim (2018) <doi:10.1002/sta4.210>.
License:	GPL (>=2)

Author(s)

Jongwook Kim, Adriano Zanin Zambom

Maintainer: Adriano Zanin Zambom <[e-mail]>

References

Zambom A Z, Kim J. Consistent significance controlled variable selection in high-dimensional regression. Stat.2018;7:e210. https://doi.org/10.1002/sta4.210