Internal function to assess whether the input arguments df
and predictors
result in data dimensions suitable for pairwise correlation analysis.
If the number of rows in df
is smaller than 10, an error is issued.
Usage
validate_data_cor(
df = NULL,
predictors = NULL,
function_name = "collinear::validate_data_cor()",
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_cor()"
- 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_vif()
,
validate_df()
,
validate_encoding_arguments()
,
validate_predictors()
,
validate_preference_order()
,
validate_response()