CRAN

My.stepwise 0.1.0

Stepwise Variable Selection Procedures for Regression Analysis

Released Jun 29, 2017 by Fu-Chang Hu

This package can be loaded by Renjin but all tests failed.

Dependencies

lmtest 0.9-35 survival 2.41-3 car 2.1-6

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.

Installation

Maven

This package can be included as a dependency from a Java or Scala project by including the following your project's pom.xml file. Read more about embedding Renjin in JVM-based projects.

<dependencies>
  <dependency>
    <groupId>org.renjin.cran</groupId>
    <artifactId>My.stepwise</artifactId>
    <version>0.1.0-b11</version>
  </dependency>
</dependencies>
<repositories>
  <repository>
    <id>bedatadriven</id>
    <name>bedatadriven public repo</name>
    <url>https://nexus.bedatadriven.com/content/groups/public/</url>
  </repository>
</repositories>

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Renjin CLI

If you're using Renjin from the command line, you load this library by invoking:

library('org.renjin.cran:My.stepwise')

Test Results

This package was last tested against Renjin 0.8.2561 on Dec 22, 2017.

Source

R

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Release History