CRAN

VSURF 1.0.4

Variable Selection Using Random Forests

Released Apr 10, 2018 by Robin Genuer

This package can be loaded by Renjin but 1 out 6 tests failed. An older version of this package is more compatible with Renjin.

Dependencies

randomForest 4.6-14 foreach 1.4.4 doParallel 1.0.11 rpart 4.1-13

Three steps variable selection procedure based on random forests. Initially developed to handle high dimensional data (for which number of variables largely exceeds number of observations), the package is very versatile and can treat most dimensions of data, for regression and supervised classification problems. First step is dedicated to eliminate irrelevant variables from the dataset. Second step aims to select all variables related to the response for interpretation purpose. Third step refines the selection by eliminating redundancy in the set of variables selected by the second step, for prediction purpose. Genuer, R. and Poggi, J.M. and Tuleau-Malot, C. (2015) .

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>VSURF</artifactId>
    <version>1.0.4-b3</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:VSURF')

Test Results

This package was last tested against Renjin 0.9.2644 on Jun 2, 2018.

Source

R

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Release History