vif_df: Compute variance inflation factors dataframe

In collinear: Automated Multicollinearity Management

Compute variance inflation factors dataframe

Description

Computes the pairwise correlation matrix between all pairs of predictors via cor_df() and cor_matrix(), applies vif() to the resulting matrix to compute Variance Inflation Factors, and returns the result as a dataframe.

Usage

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

Arguments

df	(required; dataframe, tibble, or sf) A dataframe with responses (optional) and predictors. Must have at least 10 rows for pairwise correlation analysis, and 10 * (length(predictors) - 1) for VIF. 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:

• predictor: Character, predictor name.

• vif: Numeric, variance inflation factor

Variance Inflation Factors

VIF for predictor a is computed as 1/(1-R^2), where R^2 is the multiple R-squared from regressing a on the other predictors. Recommended maximums commonly used are 2.5, 5, and 10.

References

• David A. Belsley, D.A., Kuh, E., Welsch, R.E. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. John Wiley & Sons. DOI: 10.1002/0471725153.

See Also

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

Examples

data(vi_smol)

# ## 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)

#predictors
predictors = c(
  "koppen_zone", #character
  "soil_type", #factor
  "topo_elevation", #numeric
  "soil_temperature_mean" #numeric
)

x <- vif_df(
  df = vi_smol,
  predictors = predictors
)

x

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

collinear documentation built on Dec. 8, 2025, 5:06 p.m.