pair_cor

Calculates one of either pearson, spearman or kendall correlation for every numeric 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_cor function

<dl><dt>d</dt>
<dd>A dataframe</dd>
<dt>method</dt>
<dd>A character string for the correlation coefficient to be calculated. Either "pearson" (default),
"spearman", or "kendall". If the value is "all", then all three correlations are calculated.</dd>
<dt>handle.na</dt>
<dd>If TRUE uses pairwise complete observations to calculate correlation coefficient, otherwise NAs not handled.</dd>
<dt>...</dt>
<dd>other arguments</dd></dl>

Arguments

Pearson, Spearman or Kendall correlation — pair_cor

<dl>

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


<dt>method</dt>
<dd>A character string for the correlation coefficient to be calculated. Either "pearson" (default),
"spearman", or "kendall". If the value is "all", then all three correlations are calculated.</dd>


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


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

</dl>

pair_cor: Pearson, Spearman or Kendall correlation

Description

Usage

Value

Arguments

See Also

Examples