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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/scale.R
\name{step_scale}
\alias{step_scale}
\alias{tidy.step_scale}
\title{Scaling Numeric Data}
\usage{
step_scale(
recipe,
...,
role = NA,
trained = FALSE,
sds = NULL,
factor = 1,
na_rm = TRUE,
skip = FALSE,
id = rand_id("scale")
)
\method{tidy}{step_scale}(x, ...)
}
\arguments{
\item{recipe}{A recipe object. The step will be added to the
sequence of operations for this recipe.}
\item{...}{One or more selector functions to choose which
variables are affected by the step. See \code{\link[=selections]{selections()}}
for more details. For the \code{tidy} method, these are not
currently used.}
\item{role}{Not used by this step since no new variables are
created.}
\item{trained}{A logical to indicate if the quantities for
preprocessing have been estimated.}
\item{sds}{A named numeric vector of standard deviations. This
is \code{NULL} until computed by \code{\link[=prep.recipe]{prep.recipe()}}.}
\item{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 \code{1}. More in reference below.}
\item{na_rm}{A logical value indicating whether \code{NA}
values should be removed when computing the standard deviation.}
\item{skip}{A logical. Should the step be skipped when the
recipe is baked by \code{\link[=bake.recipe]{bake.recipe()}}? While all operations are baked
when \code{\link[=prep.recipe]{prep.recipe()}} 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 \code{skip = TRUE} as it may affect
the computations for subsequent operations}
\item{id}{A character string that is unique to this step to identify it.}
\item{x}{A \code{step_scale} object.}
}
\value{
An updated version of \code{recipe} with the new step
added to the sequence of existing steps (if any). For the
\code{tidy} method, a tibble with columns \code{terms} (the
selectors or variables selected) and \code{value} (the
standard deviations).
}
\description{
\code{step_scale} creates a \emph{specification} of a recipe
step that will normalize numeric data to have a standard
deviation of one.
}
\details{
Scaling data means that the standard deviation of a
variable is divided out of the data. \code{step_scale} estimates
the variable standard deviations from the data used in the
\code{training} argument of \code{prep.recipe}.
\code{bake.recipe} then applies the scaling to new data sets
using these standard deviations.
}
\examples{
library(modeldata)
data(biomass)
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)
}
\references{
Gelman, A. (2007) "Scaling regression inputs by
dividing by two standard deviations." Unpublished. Source:
\url{http://www.stat.columbia.edu/~gelman/research/unpublished/standardizing.pdf}.
}
\concept{normalization_methods}
\concept{preprocessing}
\keyword{datagen}
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