CRAN

auto.pca 0.3

Automatic Variable Reduction Using Principal Component Analysis

Released Sep 12, 2017 by Navinkumar Nedunchezhian

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

Dependencies

PCA done by eigenvalue decomposition of a data correlation matrix, here it automatically determines the number of factors by eigenvalue greater than 1 and it gives the uncorrelated variables based on the rotated component scores, Such that in each principal component variable which has the high variance are selected. It will be useful for non-statisticians in selection of variables. For more information, see the web page.

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>auto.pca</artifactId>
    <version>0.3-b17</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:auto.pca')

Test Results

This package was last tested against Renjin 0.9.2689 on Aug 26, 2018.

auto.pca-examples

Source

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