.effect_correlations: 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
)

Value

A tibble with correlation results.

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.