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 indf
are used instead. If argumentresponse
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
See also
Other data_validation:
validate_data_cor()
,
validate_df()
,
validate_encoding_arguments()
,
validate_predictors()
,
validate_preference_order()
,
validate_response()