% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ns.R
\name{step_ns}
\alias{step_ns}
\alias{tidy.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")
)

\method{tidy}{step_ns}(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}{For model terms created by this step, what analysis
role should they be assigned?. By default, the function assumes
that the new columns created from the original variables will be
used as 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.}

\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.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_ns} 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 and \code{holiday}.
}
\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.
}
\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)

with_splines <- rec \%>\%
  step_ns(carbon, hydrogen)
with_splines <- prep(with_splines, training = biomass_tr)

expanded <- bake(with_splines, biomass_te)
expanded
}
\seealso{
\code{\link[=step_poly]{step_poly()}} \code{\link[=recipe]{recipe()}}
\code{\link[=prep.recipe]{prep.recipe()}} \code{\link[=bake.recipe]{bake.recipe()}}
}
\concept{basis_expansion}
\concept{preprocessing}
\keyword{datagen}
