validate_preference_order: Validate Argument preference_order

In collinear: Automated Multicollinearity Management

Validate Argument preference_order

Description

Internal function to validate the argument preference_order.

Usage

validate_preference_order(
  predictors = NULL,
  preference_order = NULL,
  preference_order_auto = NULL,
  function_name = "collinear::validate_preference_order()",
  quiet = FALSE
)

Arguments

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
preference_order	(optional; string, character vector, output of preference_order()). Defines a priority order, from first to last, to preserve predictors during the selection process. Accepted inputs are: "auto" (default): if response is not NULL, calls preference_order() for internal computation. character vector: predictor names in a custom preference order. data frame: output of preference_order() from response of length one. named list: output of preference_order() from response of length two or more. NULL: disabled. . Default: "auto"
preference_order_auto	(required, character vector) names of the predictors in the automated preference order returned by vif_select() or cor_select()
function_name	(optional, character string) Name of the function performing the check. Default: "collinear::validate_preference_order()"
quiet	(optional; logical) If FALSE, messages generated during the execution of the function are printed to the console Default: FALSE

Value

character vector: ranked variable names

See Also

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

Examples

data(
  vi,
  vi_predictors
  )

#validating example data frame
vi <- validate_df(
  df = vi
)

#validating example predictors
vi_predictors <- validate_predictors(
  df = vi,
  predictors = vi_predictors
)

#tagged as validated
attributes(vi_predictors)$validated

#validate preference order
my_order <- c(
  "swi_max",
  "swi_min",
  "swi_deviance" #wrong one
)

my_order <- validate_preference_order(
  predictors = vi_predictors,
  preference_order = my_order,
  preference_order_auto = vi_predictors
)

#has my_order first
#excludes wrong names
#all other variables ordered according to preference_order_auto
my_order

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