My.stepwise: Stepwise Variable Selection Procedures for Regression Analysis

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.

Version:	0.1.0
Depends:	R (≥ 3.3.3)
Imports:	car, lmtest, survival, stats
Published:	2017-06-29
DOI:	10.32614/CRAN.package.My.stepwise
Author:	International-Harvard Statistical Consulting Company
Maintainer:	Fu-Chang Hu <fuchang.hu at gmail.com>
License:	GPL (≥ 3)
NeedsCompilation:	no
CRAN checks:	My.stepwise results

Documentation:

Reference manual:

My.stepwise.pdf

Downloads:

Package source:	My.stepwise_0.1.0.tar.gz
Windows binaries:	r-devel: My.stepwise_0.1.0.zip, r-release: My.stepwise_0.1.0.zip, r-oldrel: My.stepwise_0.1.0.zip
macOS binaries:	r-release (arm64): My.stepwise_0.1.0.tgz, r-oldrel (arm64): My.stepwise_0.1.0.tgz, r-release (x86_64): My.stepwise_0.1.0.tgz, r-oldrel (x86_64): My.stepwise_0.1.0.tgz

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