pair_ace

Calculates the maximal correlation coefficient from alternating conditional expectations algorithm for every 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_ace function

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

Arguments

Alternating conditional expectations correlation — pair_ace

<dl>

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


<dt>handle.na</dt>
<dd>If TRUE uses pairwise complete observations, otherwise NAs not handled.</dd>


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

</dl>

pair_ace: Alternating conditional expectations correlation

Description

Usage

Value

Arguments

Details

References

Examples