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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/spatialsign.R
\name{step_spatialsign}
\alias{step_spatialsign}
\alias{tidy.step_spatialsign}
\title{Spatial Sign Preprocessing}
\usage{
step_spatialsign(
recipe,
...,
role = "predictor",
na_rm = TRUE,
trained = FALSE,
columns = NULL,
skip = FALSE,
id = rand_id("spatialsign")
)
\method{tidy}{step_spatialsign}(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 will be used for the normalization. See
\code{\link[=selections]{selections()}} for more details. For the \code{tidy}
method, these are not currently used.}
\item{role}{For model terms created by this step, what analysis
role should they be assigned?}
\item{na_rm}{A logical: should missing data be removed from the
norm computation?}
\item{trained}{A logical to indicate if the quantities for
preprocessing have been estimated.}
\item{columns}{A character string of variable names that will
be populated (eventually) by the \code{terms} argument.}
\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_spatialsign} 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} which
is the columns that will be affected.
}
\description{
\code{step_spatialsign} is a \emph{specification} of a recipe
step that will convert numeric data into a projection on to a
unit sphere.
}
\details{
The spatial sign transformation projects the variables
onto a unit sphere and is related to global contrast
normalization. The spatial sign of a vector \code{w} is
\code{w/norm(w)}.
The variables should be centered and scaled prior to the
computations.
}
\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)
ss_trans <- rec \%>\%
step_center(carbon, hydrogen) \%>\%
step_scale(carbon, hydrogen) \%>\%
step_spatialsign(carbon, hydrogen)
ss_obj <- prep(ss_trans, training = biomass_tr)
transformed_te <- bake(ss_obj, biomass_te)
plot(biomass_te$carbon, biomass_te$hydrogen)
plot(transformed_te$carbon, transformed_te$hydrogen)
tidy(ss_trans, number = 3)
tidy(ss_obj, number = 3)
}
\references{
Serneels, S., De Nolf, E., and Van Espen, P.
(2006). Spatial sign preprocessing: a simple way to impart
moderate robustness to multivariate estimators. \emph{Journal of
Chemical Information and Modeling}, 46(3), 1402-1409.
}
\concept{preprocessing}
\concept{projection_methods}
\keyword{datagen}
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