dot-effect_correlations: Calculate correlation and cooccurrence coefficients and test...

In volker: High-Level Functions for Tabulating, Charting and Reporting Survey Data

Calculate correlation and cooccurrence coefficients and test whether they are different from zero

Description

This function is used to calculate coefficients for all pairwise items by calling get_correlation() on each combination of the items in the cols- by cross-parameter.

Usage

.effect_correlations(
  data,
  cols,
  cross,
  method = "pearson",
  category = NULL,
  test = TRUE,
  adjust = "fdr",
  labels = TRUE
)

Arguments

data	A tibble.
cols	The columns holding metric values.
cross	The columns holding metric values to correlate.
method	The output metrics, pearson = Pearson's R, spearman = Spearman's rho, cramer = Cramer's V, npmi = Normalized Pointwise Mutual Information. The reported R square value is simply the square of Spearman's rho respective Pearson's r.
category	Calculating NPMI for multiple items requires a focus category. By default, for logical column types, only TRUE values are counted. For other column types, the first category is counted. Accepts both character and numeric values to override default counting behavior.
test	Boolean, whether to perform significance tests (default = TRUE).
adjust	Performing multiple significance tests inflates the alpha error. Thus, p values need to be adjusted according to the number of tests. Set a method supported by stats::p.adjust, e.g. "fdr" (the default) or "bonferroni". Disable adjustment with FALSE.
labels	If TRUE (default) extracts labels from the attributes, see codebook.

Value

A tibble with correlation results.

volker documentation built on Nov. 5, 2025, 5:21 p.m.