recipes (version 1.1.0)

step_intercept: Add intercept (or constant) column

Description

step_intercept() creates a specification of a recipe step that will add an intercept or constant term in the first column of a data matrix. step_intercept() defaults to predictor role so that it is by default only called in the bake step. Be careful to avoid unintentional transformations when calling steps with all_predictors().

Usage

step_intercept(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  name = "intercept",
  value = 1L,
  skip = FALSE,
  id = rand_id("intercept")
)

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.

...

Argument ignored; included for consistency with other step specification functions.

role

For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.

trained

A logical to indicate if the quantities for preprocessing have been estimated. Again included only for consistency.

name

Character name for newly added column

value

A numeric constant to fill the intercept column. Defaults to 1L.

skip

A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations.

id

A character string that is unique to this step to identify it.

Tidying

When you tidy() this step, a tibble is returned with columns terms and id:

terms

character, the selectors or variables selected

id

character, id of this step

Case weights

The underlying operation does not allow for case weights.

Examples

data(biomass, package = "modeldata")

biomass_tr <- biomass[biomass$dataset == "Training", ]
biomass_te <- biomass[biomass$dataset == "Testing", ]

rec <- recipe(
  HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
  data = biomass_tr
)
rec_trans <- recipe(HHV ~ ., data = biomass_tr[, -(1:2)]) %>%
  step_intercept(value = 2) %>%
  step_scale(carbon)

rec_obj <- prep(rec_trans, training = biomass_tr)

with_intercept <- bake(rec_obj, biomass_te)
with_intercept