step_intercept {recipes} | R Documentation |
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")
)
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
|
skip |
A logical. Should the step be skipped when the
recipe is baked by |
id |
A character string that is unique to this step to identify it. |
Value
An updated version of recipe
with the new step added to the
sequence of any existing operations.
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