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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ns.R
\name{step_ns}
\alias{step_ns}
\title{Natural Spline Basis Functions}
\usage{
step_ns(
recipe,
...,
role = "predictor",
trained = FALSE,
objects = NULL,
deg_free = 2,
options = list(),
skip = FALSE,
id = rand_id("ns")
)
}
\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 variables
for this step. See \code{\link[=selections]{selections()}} for more details.}
\item{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 \emph{predictors} in a model.}
\item{trained}{A logical to indicate if the quantities for
preprocessing have been estimated.}
\item{objects}{A list of \code{\link[splines:ns]{splines::ns()}} objects
created once the step has been trained.}
\item{deg_free}{The degrees of freedom for the natural spline. As the
degrees of freedom for a natural spline increase, more flexible and
complex curves can be generated. When a single degree of freedom is used,
the result is a rescaled version of the original data.}
\item{options}{A list of options for \code{\link[splines:ns]{splines::ns()}}
which should not include \code{x} or \code{df}.}
\item{skip}{A logical. Should the step be skipped when the
recipe is baked by \code{\link[=bake]{bake()}}? While all operations are baked
when \code{\link[=prep]{prep()}} 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.}
}
\value{
An updated version of \code{recipe} with the new step added to the
sequence of any existing operations.
}
\description{
\code{step_ns} creates a \emph{specification} of a recipe step
that will create new columns that are basis expansions of
variables using natural splines.
}
\details{
\code{step_ns} can create new features from a single variable
that enable fitting routines to model this variable in a
nonlinear manner. The extent of the possible nonlinearity is
determined by the \code{df} or \code{knot} arguments of
\code{\link[splines:ns]{splines::ns()}}. The original variables are removed
from the data and new columns are added. The naming convention
for the new variables is \code{varname_ns_1} and so on.
}
\section{Tidying}{
When you \code{\link[=tidy.recipe]{tidy()}} this step, a tibble with column
\code{terms} (the columns that will be affected) is returned.
}
\section{Case weights}{
The underlying operation does not allow for case weights.
}
\examples{
\dontshow{if (rlang::is_installed("modeldata")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
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
)
with_splines <- rec \%>\%
step_ns(carbon, hydrogen)
with_splines <- prep(with_splines, training = biomass_tr)
expanded <- bake(with_splines, biomass_te)
expanded
\dontshow{\}) # examplesIf}
}
\seealso{
Other individual transformation steps:
\code{\link{step_BoxCox}()},
\code{\link{step_YeoJohnson}()},
\code{\link{step_bs}()},
\code{\link{step_harmonic}()},
\code{\link{step_hyperbolic}()},
\code{\link{step_inverse}()},
\code{\link{step_invlogit}()},
\code{\link{step_logit}()},
\code{\link{step_log}()},
\code{\link{step_mutate}()},
\code{\link{step_percentile}()},
\code{\link{step_poly}()},
\code{\link{step_relu}()},
\code{\link{step_sqrt}()}
}
\concept{individual transformation steps}
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