OPCP_mat

This function provides a matrix that contains Pearson correlations in the lower triangle and Observed Proportions of Concordant Pairs (OPCPs) in the upper triangle between multiple variables.

Analysis of pervasiveness of effects in correlational data. The Observed Proportion (or Percentage) of Concordant Pairs (OPCP) is Kendall's Tau expressed on a 0 to 1 metric instead of the traditional -1 to 1 metric to facilitate interpretation. As its name implies, it represents the proportion of concordant pairs in a sample (with an adjustment for ties). Pairs are concordant when a participant who has a larger value on a variable than another participant also has a larger value on a second variable. The OPCP is therefore an easily interpretable indicator of monotonicity. The pervasive functions are essentially wrappers for the 'arules' package by Hahsler et al. (2025)<doi:10.32614/CRAN.package.arules> and serve to count individuals who actually display the pattern(s) suggested by a regression. For more details, see the paper "Considering approaches to pervasiveness in the context of personality psychology" now accepted at the journal Personality Science.

Denis Lajoie

pervasive

Pervasiveness Functions for Correlational Data

OPCP_mat function

<dl><dt>data</dt>
<dd>A data frame containing the variables specified in the formula.</dd>
<dt>round_digits</dt>
<dd>The number of decimals to be included in the resulting table.</dd></dl>

Arguments

Calculate Observed Proportion of Concordant Pairs (OPCP) — OPCP_mat

<dl>

<dt>data</dt>
<dd>A data frame containing the variables specified in the formula.</dd>


<dt>round_digits</dt>
<dd>The number of decimals to be included in the resulting table.</dd>

</dl>

OPCP_mat: Calculate Observed Proportion of Concordant Pairs (OPCP)

Description

Usage

Value

Arguments

Examples