collinear (version 3.0.0)

step_collinear: Tidymodels recipe step for multicollinearity filtering

Description

Adds a step to a recipe created by recipes::recipe()]+ to apply multicollinearity filtering via collinear().

This function requires the recipes package to be installed.

Unlike collinear(), this wrapper does not perform target encoding, and the default value for the argument quiet is TRUE.

Usage

step_collinear(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  options = list(),
  selected = NULL,
  skip = FALSE,
  keep_original_cols = FALSE,
  id = recipes::rand_id("collinear")
)
# S3 method for step_collinear
prep(x = NULL, training = NULL, info = NULL, ...)
# S3 method for step_collinear
bake(object = NULL, new_data = NULL, ...)

Value

Updated recipe with new step.

Arguments

recipe

(required, recipe) A recipe object to which this step will be added.

...

(optional) Additional arguments (currently ignored).

role

(optional, character) Not used by this step since no new variables are created. Default: NA

trained

(optional, logical) Indicates if the step has been trained. Default: FALSE

options

(optional, list) Named list of arguments passed to collinear(). Common options include:

• max_cor: Maximum correlation threshold. If NULL (default), automatically set based on median correlation of predictors.

• max_vif: Maximum VIF threshold. If NULL (default), automatically set to match the auto-computed max_cor.

• preference_order: Vector of predictor names in priority order.

• f: Function to compute preference order (default: f_auto)

• quiet: Suppress messages (default: TRUE)

Note: encoding_method is not supported in this step. The automatic threshold selection adapts to each dataset's correlation structure.

selected

(character vector) Predictors retained after filtering. Populated during training and used during baking. Default: NULL

skip

(optional, logical) Trigger to skip this step when baking. Default: FALSE.

keep_original_cols

(optional, logical) Whether to keep original columns. Default: FALSE.

id

(optional, character) Unique identifier for this step.

x

(required, step_collinear object) The step to be trained. Default: NULL

training

(required, data.frame) The training dataset used to estimate quantities.

info

(optional, data.frame) Preprocessed information about variables in training. Default: NULL

object

(required, step_collinear object) The trained step. Default: NULL

new_data

(required, data.frame) New data to apply the step to. Default: NULL

See Also

Other multicollinearity_filtering: collinear(), collinear_select(), cor_select(), vif_select()

Examples

if (FALSE) {
if(requireNamespace("recipes", quietly = TRUE) &&
    requireNamespace("parsnip", quietly = TRUE) &&
    requireNamespace("workflows", quietly = TRUE)
    ){

data(
  vi_smol,
  vi_predictors_numeric
  )

# model formula
vi_formula <- collinear::model_formula(
  df = vi_smol,
  response = "vi_numeric",
  predictors = vi_predictors_numeric
)

# recipe
vi_recipe <- recipes::recipe(
  formula = vi_formula,
  data = vi_smol
  ) |>
  #multicollinearity filtering
  collinear::step_collinear(
    recipes::all_predictors(),
    options = list(
      max_cor = 0.7,
      max_vif = 5,
      f = collinear::f_numeric_glm
      )
  ) |>
  #normalization
  recipes::step_normalize(
    recipes::all_predictors()
    )

# define linear regression model
vi_model <- parsnip::linear_reg() |>
  parsnip::set_engine("lm")

# create and fit workflow
vi_workflow <- workflows::workflow() |>
  workflows::add_recipe(vi_recipe) |>
  workflows::add_model(vi_model) |>
  workflows::fit(data = vi_smol)

vi_workflow

}
}