Other packages > Find by keyword >

My.stepwise  

Stepwise Variable Selection Procedures for Regression Analysis
View on CRAN: Click here


Download and install My.stepwise package within the R console
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]
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 16 Apr. 2025.
Previous versions and publish date:
No previous versions
Other packages that cited My.stepwise R package
View My.stepwise citation profile
Other R packages that My.stepwise depends, imports, suggests or enhances
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
Downloads during the last 30 days
03/1703/1803/1903/2003/2103/2203/2303/2403/2503/2603/2703/2803/2903/3003/3104/0104/0204/0304/0404/0504/0604/0704/0804/0904/1004/1104/1204/1304/14Downloads for My.stepwise02468101214161820222426TrendBars

Today's Hot Picks in Authors and Packages

MM4LMM  
Inference of Linear Mixed Models Through MM Algorithm
The main function MMEst() performs (Restricted) Maximum Likelihood in a variance component mixed mod ...
Download / Learn more Package Citations See dependency  
apache.sedona  
R Interface for Apache Sedona
R interface for 'Apache Sedona' based on 'sparklyr' (). ...
Download / Learn more Package Citations See dependency  
hkclustering  
Ensemble Clustering using K Means and Hierarchical Clustering
Implements an ensemble algorithm for clustering combining a k-means and a hierarchical clustering ap ...
Download / Learn more Package Citations See dependency  
quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  
datadictionary  
Create a Data Dictionary
Creates a data dictionary from any dataframe or tibble in your R environment. You can opt to add va ...
Download / Learn more Package Citations See dependency  
MultiKink  
Estimation and Inference for Multi-Kink Quantile Regression
Estimation and inference for multiple kink quantile regression for longitudinal data and the i.i.d d ...
Download / Learn more Package Citations See dependency  

24,012

R Packages

207,311

Dependencies

64,993

Author Associations

24,013

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA