step_scale {recipes} | R Documentation |
Scaling numeric data
Description
step_scale()
creates a specification of a recipe step that will normalize
numeric data to have a standard deviation of one.
Usage
step_scale(
recipe,
...,
role = NA,
trained = FALSE,
sds = NULL,
factor = 1,
na_rm = TRUE,
skip = FALSE,
id = rand_id("scale")
)
Arguments
recipe |
A recipe object. The step will be added to the sequence of operations for this recipe. |
... |
One or more selector functions to choose variables for this step.
See |
role |
Not used by this step since no new variables are created. |
trained |
A logical to indicate if the quantities for preprocessing have been estimated. |
sds |
A named numeric vector of standard deviations. This is |
factor |
A numeric value of either 1 or 2 that scales the numeric inputs
by one or two standard deviations. By dividing by two standard deviations,
the coefficients attached to continuous predictors can be interpreted the
same way as with binary inputs. Defaults to |
na_rm |
A logical value indicating whether |
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. |
Details
Scaling data means that the standard deviation of a variable is divided out
of the data. step_scale()
estimates the variable standard deviations from
the data used in the training
argument of prep()
. bake()
then applies
the scaling to new data sets using these standard deviations.
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
, value
, and id
:
- terms
character, the selectors or variables selected
- value
numeric, the standard deviations
- id
character, id of this step
Sparse data
This step can be applied to sparse_data such that it is preserved. Nothing needs to be done for this to happen as it is done automatically.
Case weights
This step performs an unsupervised operation that can utilize case weights.
As a result, case weights are only used with frequency weights. For more
information, see the documentation in case_weights and the examples on
tidymodels.org
.
References
Gelman, A. (2007) "Scaling regression inputs by
dividing by two standard deviations." Unpublished. Source:
https://sites.stat.columbia.edu/gelman/research/unpublished/standardizing.pdf
.
See Also
Other normalization steps:
step_center()
,
step_normalize()
,
step_range()
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
)
scaled_trans <- rec |>
step_scale(carbon, hydrogen)
scaled_obj <- prep(scaled_trans, training = biomass_tr)
transformed_te <- bake(scaled_obj, biomass_te)
biomass_te[1:10, names(transformed_te)]
transformed_te
tidy(scaled_trans, number = 1)
tidy(scaled_obj, number = 1)