CRAN

mobForest 1.3.0

Model Based Random Forest Analysis

Released Jan 3, 2018 by Kasey Jones

Dependencies

party 1.3-0 sandwich 2.4-0 zoo 1.8-1 strucchange 1.5-1 modeltools 0.2-21

Functions to implements random forest method for model based recursive partitioning. The mob() function, developed by Zeileis et al. (2008), within 'party' package, is modified to construct model-based decision trees based on random forests methodology. The main input function mobforest.analysis() takes all input parameters to construct trees, compute out-of-bag errors, predictions, and overall accuracy of forest. The algorithm performs parallel computation using cluster functions within 'parallel' package.

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>mobForest</artifactId>
    <version>1.3.0-b5</version>
  </dependency>
</dependencies>
<repositories>
  <repository>
    <id>bedatadriven</id>
    <name>bedatadriven public repo</name>
    <url>https://nexus.bedatadriven.com/content/groups/public/</url>
  </repository>
</repositories>

View build log

Renjin CLI

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

library('org.renjin.cran:mobForest')

Test Results

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

• compute.acc-examples
• compute.mse-examples
• compute.r2-examples
• logistic.acc-examples
• mobforest.analysis-examples
• mobforest.control-class-examples
• mobforest.control-examples
• string.formula-examples
• testthat
• tree.predictions-examples

Source

View GitHub Mirror

Release History

• 1.3.0
• 1.2
• 1.1
• 1.0