Compute summary statistics for absolute pairwise correlations

Computes the the minimum, mean, maximum, and quantiles 0.05, 0.25, median (0.5), 0.75, and 0.95 on the absolute values of the column "correlation" in the output of cor_df().

Usage

cor_stats(df = NULL, predictors = NULL, quiet = FALSE, ...)

Arguments

df

(required; dataframe, tibble, or sf) A dataframe with predictors or the output of cor_df(). Default: NULL.

predictors

(optional; character vector or NULL) Names of the predictors in df. If NULL, all columns except responses and constant/near-zero-variance columns are used. Default: NULL.

quiet

(optional; logical) If FALSE, messages are printed. Default: FALSE.

...

(optional) Internal args (e.g. function_name for validate_arg_function_name, a precomputed correlation matrix m, or cross-validation args for preference_order).

Value

dataframe with columns method (with value "correlation"), statistic and value

See also

Other multicollinearity_assessment: collinear_stats(), cor_clusters(), cor_cramer(), cor_df(), cor_matrix(), vif(), vif_df(), vif_stats()

Examples

data(
  vi_smol,
  vi_predictors_numeric
  )

## OPTIONAL: parallelization setup
## irrelevant when all predictors are numeric
## only worth it for large data with many categoricals
# future::plan(
#   future::multisession,
#   workers = future::availableCores() - 1
# )

## OPTIONAL: progress bar
# progressr::handlers(global = TRUE)

x <- cor_stats(
  df = vi_smol,
  predictors = vi_predictors_numeric
)

x
#>        method     statistic     value
#> 1 correlation             n 1081.0000
#> 2 correlation       minimum    0.0011
#> 3 correlation quantile_0.05    0.0426
#> 4 correlation quantile_0.25    0.1817
#> 5 correlation          mean    0.3980
#> 6 correlation        median    0.3610
#> 7 correlation quantile_0.75    0.6184
#> 8 correlation quantile_0.95    0.8264
#> 9 correlation       maximum    0.9893

## OPTIONAL: disable parallelization
#future::plan(future::sequential)