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 |
| 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 |
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