entropy

entropy,numeric,numeric-method

entropy,Partition,numeric-method

entropy,ANY,missing-method

Optional base of the logarithm (default: $e$)

log_base

Compute the Shannon entropy
$$-\sum_{i} p_i \log_b p_i$$

Provides several measures ((dis)similarity, distance/metric,
correlation, entropy) for comparing two partitions of the same set of
objects. The different measures can be assigned to three different
classes: Pair comparison (containing the famous Jaccard and Rand
indices), set based, and information theory based.
Many of the implemented measures can be found in
Albatineh AN, Niewiadomska-Bugaj M and Mihalko D (2006)
<doi:10.1007/s00357-006-0017-z> and
Meila M (2007) <doi:10.1016/j.jmva.2006.11.013>.
Partitions are represented by vectors of class labels which allow a
straightforward integration with existing clustering algorithms
(e.g. kmeans()). The package is mostly based on the S4 object system.

Fabian Ball

partitionComparison

Implements Measures for the Comparison of Two Partitions

Andreas Geyer-Schulz

entropy function

<dl><dt>x</dt>
<dd>A probability distribution</dd>
<dt>log_base</dt>
<dd>Optional base of the logarithm (default: $e$)</dd></dl>

Arguments

<ul>
<li><code>x = Partition,log_base = numeric</code>: Entropy of a partition represented by <code>x</code></li>
</ul>

Methods (by class)

This method is used internally for measures based on information theory

Hint

Fabian Ball <a href="/link/fabian.ball%40kit.edu?package=partitionComparison&version=0.2.5" data-mini-rdoc="partitionComparison::fabian.ball@kit.edu">fabian.ball@kit.edu</a>

Author

Entropy — entropy

<dl>

<dt>x</dt>
<dd>A probability distribution</dd>


<dt>log_base</dt>
<dd>Optional base of the logarithm (default: $e$)</dd>

</dl>

This method is used internally for measures based on information theory

Fabian Ball <a href='mailto:fabian.ball@kit.edu'>fabian.ball@kit.edu</a>

entropy: Entropy

Description

Usage

Arguments

Methods (by class)

Hint

Author

Examples