validate_data_vif: Validate Data for VIF Analysis

In collinear: Automated Multicollinearity Management

Validate Data for VIF Analysis

Description

Internal function to assess whether the input arguments df and predictors result in data dimensions suitable for a VIF analysis.

If the number of rows in df is smaller than 10 times the length of predictors, the function either issues a message and restricts predictors to a manageable number, or returns an error.

Usage

validate_data_vif(
  df = NULL,
  predictors = NULL,
  function_name = "collinear::validate_data_vif()",
  quiet = FALSE
)

Arguments

df	(required; data frame, tibble, or sf) A data frame with responses and predictors. Default: NULL.
predictors	(optional; character vector) Names of the predictors to select from df. If omitted, all numeric columns in df are used instead. If argument response is not provided, non-numeric variables are ignored. Default: NULL
function_name	(optional, character string) Name of the function performing the check. Default: "collinear::validate_data_vif()"
quiet	(optional; logical) If FALSE, messages generated during the execution of the function are printed to the console Default: FALSE

Value

character vector: predictors names

See Also

Other data_validation: validate_data_cor(), validate_df(), validate_encoding_arguments(), validate_predictors(), validate_preference_order(), validate_response()

collinear documentation built on April 12, 2025, 1:36 a.m.