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Compute variance inflation factors dataframe

Source: R/vif_df.R
vif_df.Rd

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
)
#> 
#> collinear::vif_df()
#> └── collinear::validate_arg_df(): converted the following character columns to factor:
#>  - koppen_zone
#> 
#> collinear::vif_df()
#> └── collinear::cor_matrix()
#>     └── collinear::cor_df(): 2 categorical predictors have cardinality > 2 and may bias the multicollinearity analysis. Applying target encoding to convert them to numeric will solve this issue.

x
#>       vif             predictor
#> 1  0.7927           koppen_zone
#> 2 -0.0356 soil_temperature_mean
#> 3 -0.4397             soil_type
#> 4 -0.9578        topo_elevation

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