correlate_variables: Correlate variables in the data set.

In PiotrTymoszuk/ExDA: Toolset for Explorative Data Analysis

Correlate variables in the data set.

Description

Correlates two variables in the provided data sets by Pearson, Spearman or Kendall method.

Usage

correlate_variables(
  ...,
  variables,
  what = c("correlation", "covariance"),
  type = c("pearson", "spearman", "kendall", "kappa"),
  ci = TRUE,
  boot_method = "percentile",
  pub_styled = FALSE,
  signif_digits = 2,
  adj_method = "none",
  simplify_p = TRUE
)

Arguments

...	data sets.
variables	a vector with variable names. If more than two provided, only the first two are analyzed.
what	the requested analysis: 'correlation' or 'covariance'. Defaults to 'correlation'.
type	the type of correlation/covariance: Pearson, Spearman, Kendall or Kohen's kappa. Defaults to Pearson. See: correlate and covariance for details.
ci	logical, should confidence intervals for the test effect size be returned?
boot_method	indicates how the bootstrap confidence intervals are calculated. Can be any of 'percentile', 'bca', or 'normality', defaults to 'percentile'.
pub_styled	logical, should the output be publication-ready formatted?
signif_digits	significant digits used for rounding in the publication-style output.
adj_method	the method for adjusting p values for multiple testing, as defined for p.adjust, defaults to 'none'. The adjusted p value is summarized in the 'significance' column, when pub_style output is chosen.
simplify_p	logical, should p_values < 0.001 be presented in a p < 0.001 form?

PiotrTymoszuk/ExDA documentation built on Nov. 17, 2024, 5:46 p.m.