pair_nmi

Calculates normalized mutual information for every numeric or factor or mixed variable pair in a dataset.

We provide a tidy data structure and visualisations for multiple or grouped  variable correlations,
general association measures scagnostics and other pairwise scores  suitable for numerical, ordinal and nominal variables.
Supported measures include distance correlation, maximal information, ace correlation, Kendall's tau, and polychoric correlation.

Catherine Hurley

bullseye

Visualising Multiple Pairwise Variable Correlations and Other
Scores

Amit Chinwan

pair_nmi function

<dl><dt>d</dt>
<dd>A dataframe</dd>
<dt>handle.na</dt>
<dd>If TRUE uses pairwise complete observations to calculate normalized mutual information, otherwise NAs not handled.</dd>
<dt>...</dt>
<dd>other arguments</dd></dl>

Arguments

Normalized mutual information — pair_nmi

<dl>

<dt>d</dt>
<dd>A dataframe</dd>


<dt>handle.na</dt>
<dd>If TRUE uses pairwise complete observations to calculate normalized mutual information, otherwise NAs not handled.</dd>


<dt>...</dt>
<dd>other arguments</dd>

</dl>

pair_nmi: Normalized mutual information

Description

Usage

Value

Arguments

Details

Examples