step_scale | R Documentation |
step_scale()
creates a specification of a recipe step that will normalize
numeric data to have a standard deviation of one.
step_scale(
recipe,
...,
role = NA,
trained = FALSE,
sds = NULL,
factor = 1,
na_rm = TRUE,
skip = FALSE,
id = rand_id("scale")
)
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. |
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.recipe
.
bake.recipe
then applies the scaling to new data sets
using these standard deviations.
An updated version of recipe
with the new step added to the
sequence of any existing operations.
When you tidy()
this step, a tibble is returned with
columns terms
, value
, and id
:
character, the selectors or variables selected
numeric, the standard deviations
character, id of this step
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
.
Gelman, A. (2007) "Scaling regression inputs by dividing by two standard deviations." Unpublished. Source: http://www.stat.columbia.edu/~gelman/research/unpublished/standardizing.pdf.
Other normalization steps:
step_center()
,
step_normalize()
,
step_range()
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)
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