collinear (version 3.0.0)

validate_arg_preference_order: Check and complete argument preference_order

Description

Internal function to validate the argument preference_order in cor_select(), vif_select(), collinear_select(), collinear(), and collinear(). Predictors not in preference_order are ranked from lower to higher sum of Pearson correlations with all other predictors.

Usage

validate_arg_preference_order(
  df = NULL,
  response = NULL,
  predictors = NULL,
  preference_order = NULL,
  quiet = FALSE,
  function_name = NULL,
  ...
)

Value

character vector: ranked variable names

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.

response

(optional, character string) Name of a numeric response variable in df. 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.

preference_order

(optional; character vector, dataframe from preference_order, or NULL) Prioritizes predictors to preserve.

quiet

(optional; logical) If FALSE, messages are printed. Default: FALSE.

function_name

(optional, character string) Name of the function performing the argument check. Default: NULL

...

(optional) Internal args (e.g. function_name for validate_arg_function_name, a precomputed correlation matrix m, or cross-validation args for preference_order).

See Also

Other argument_validation: drop_geometry_column(), validate_arg_df(), validate_arg_df_not_null(), validate_arg_encoding_method(), validate_arg_f(), validate_arg_function_name(), validate_arg_max_cor(), validate_arg_max_vif(), validate_arg_predictors(), validate_arg_quiet(), validate_arg_responses()

Examples

data(
  vi_smol,
  vi_predictors_numeric
  )

#input arguments must be validated first
df <- validate_arg_df(
  df = vi_smol,
  response = "vi_numeric",
  predictors = vi_predictors_numeric,
  quiet = TRUE
)

response <- validate_arg_responses(
  df = df,
  responses = "vi_numeric"
)

predictors <- validate_arg_predictors(
  df = df,
  response = response,
  predictors = vi_predictors_numeric[1:10]
)



#no preference order
#no response
#ranks predictor from lower to higher multicollinearity
y <- validate_arg_preference_order(
  df = df,
  predictors = predictors,
  preference_order = NULL
)

y
attributes(y)$validated


#validate character vector
y <- validate_arg_preference_order(
  df = df,
  predictors = predictors,
  preference_order = c(
    "swi_max",
    "swi_min",
    "swi_deviance" #does not exist
  )
)

y
attributes(y)$validated

#validate output of preference order
x <- preference_order(
  df = df,
  responses = response,
  predictors = predictors
)

x

y <- validate_arg_preference_order(
  df = df,
  response = response,
  predictors = predictors,
  preference_order = x
)

y
attributes(y)$validated