Package: My.stepwise 0.1.0

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.

Authors:International-Harvard Statistical Consulting Company <[email protected]>

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# Install 'My.stepwise' in R:
install.packages('My.stepwise', repos = c('https://fu-chang.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.57 score 1 stars 37 scripts 230 downloads 3 exports 65 dependencies

Last updated 8 years agofrom:4983700d4a. Checks:8 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 08 2025
R-4.5-winOKFeb 08 2025
R-4.5-macOKFeb 08 2025
R-4.5-linuxOKFeb 08 2025
R-4.4-winOKFeb 08 2025
R-4.4-macOKFeb 08 2025
R-4.3-winOKFeb 08 2025
R-4.3-macOKFeb 08 2025

Exports:My.stepwise.coxphMy.stepwise.glmMy.stepwise.lm

Dependencies:abindbackportsbootbroomcarcarDataclicolorspacecowplotcpp11DerivdoBydplyrfansifarverFormulagenericsggplot2gluegtableisobandlabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpurrrquantregR6rbibutilsRColorBrewerRcppRcppEigenRdpackreformulasrlangscalesSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithrzoo