Computes the (conditional) mutual information between two variables. If 
<code>z</code> is not <code>NULL</code> then returns the conditional mutual information,
\(I(X;Y|Z)\). Otherwise, returns mutual information, \(I(X;Y)\).

State-of-the art algorithms for learning discrete Bayesian network classifiers from data, including a number of those described in Bielza & Larranaga (2014) <doi:10.1145/2576868>, with functions for prediction, model evaluation and inspection.

Mihaljevic Bojan

bnclassify

Learning Discrete Bayesian Network Classifiers from Data

Bielza Concha

Larranaga Pedro

Wickham Hadley

cmi function

<dl><dt>x</dt>
<dd>A length one character.</dd>
<dt>y</dt>
<dd>A length one character.</dd>
<dt>dataset</dt>
<dd>A data frame. Must contain x, y and, optionally, z columns.</dd>
<dt>z</dt>
<dd>A character vector.</dd>
<dt>unit</dt>
<dd>A character. Logarithm base. See <code>entropy</code> package.</dd></dl>

Arguments

Compute the (conditional) mutual information between two variables. — cmi

<dl>

<dt>x</dt>
<dd>A length one character.</dd>


<dt>y</dt>
<dd>A length one character.</dd>


<dt>dataset</dt>
<dd>A data frame. Must contain x, y and, optionally, z columns.</dd>


<dt>z</dt>
<dd>A character vector.</dd>


<dt>unit</dt>
<dd>A character. Logarithm base. See <code>entropy</code> package.</dd>

</dl>

cmi: Compute the (conditional) mutual information between two variables.

Description

Usage

Arguments

Details

Examples