collinear (version 3.0.1)

collinear_stats: Compute summary statistics for correlation and VIF

Description

Computes the the minimum, mean, maximum, and quantiles 0.05, 0.25, median (0.5), 0.75, and 0.95 of the correlations and variance inflation factors in a given dataframe. Wraps the functions cor_stats() and vif_stats()

Usage

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

Value

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

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).

See Also

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

Examples

data(vi_smol, package = "spatialData")
data(vi_predictors, package = "spatialData")
vi_predictors_numeric <- identify_numeric_variables(
  df = vi_smol,
  predictors = vi_predictors
)$valid

## 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 <- collinear_stats(
  df = vi_smol,
  predictors = vi_predictors_numeric
)

x

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