Correlate the original variables with a chosen method and correction

Usage

correlate_vars(
  df,
  method = "pearson",
  partial = TRUE,
  correction = "bonferroni"
)

Arguments

df: A data frame containing the variables to be correlated, usually study_data.
method: A string indicating the correlation method to use. Default is "pearson". Other options include "spearman" and "kendall". See ?correlation::correlation() for more details.
partial: Logical. Whether to compute partial correlations. Default is TRUE. If FALSE, computes simple correlations.
correction: A string indicating the p-value correction method to use. Default is "bonferroni". Other options include "holm", "fdr", or "none". See ?correlation::correlation() for more details.

Value

A data frame containing the correlations between the variables, with the parameters renamed for better readability.

Examples

simple_correlations <- correlate_vars(study_data, partial = FALSE)
format(head(simple_correlations))
#>   Parameter1      Parameter2     r         95% CI t(94)         p
#> 1       VVIQ   OSIVQ\nObject  0.88 [ 0.82,  0.92] 17.55 < .001***
#> 2       VVIQ  OSIVQ\nSpatial  0.11 [-0.09,  0.30]  1.07 > .999   
#> 3       VVIQ   OSIVQ\nVerbal -0.23 [-0.41, -0.03] -2.32 > .999   
#> 4       VVIQ   Psi-Q\nVisual  0.94 [ 0.92,  0.96] 27.69 < .001***
#> 5       VVIQ Psi-Q\nAudition  0.80 [ 0.72,  0.86] 13.05 < .001***
#> 6       VVIQ    Psi-Q\nSmell  0.81 [ 0.73,  0.87] 13.30 < .001***