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My.stepwise  

Stepwise Variable Selection Procedures for Regression Analysis
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Install from CRAN:
install.packages("My.stepwise")

Install from Github:
library("remotes")
install_github("cran/My.stepwise")

Install by package version:
library("remotes")
install_version("My.stepwise", "0.1.0")



Attach the package and use:
library("My.stepwise")
Maintained by
Fu-Chang Hu
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-06-29
Latest Update: 2017-06-29
Description:
The stepwise variable selection procedure (with iterations between the 'forward' and 'backward' steps) can be used to obtain the best candidate final regression model in regression analysis. All the relevant covariates are put on the 'variable list' to be selected. The significance levels for entry (SLE) and for stay (SLS) are usually set to 0.15 (or larger) for being conservative. Then, with the aid of substantive knowledge, the best candidate final regression model is identified manually by dropping the covariates with p value > 0.05 one at a time until all regression coefficients are significantly different from 0 at the chosen alpha level of 0.05.
How to cite:
Fu-Chang Hu (2017). My.stepwise: Stepwise Variable Selection Procedures for Regression Analysis. R package version 0.1.0, https://cran.r-project.org/web/packages/My.stepwise. Accessed 15 Jul. 2026.
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Complete documentation for My.stepwise
Functions, R codes and Examples using the My.stepwise R package
Some associated functions: My.stepwise.coxph . My.stepwise.glm . My.stepwise.lm . 
Some associated R codes: Full My.stepwise package functions and examples
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