% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/YeoJohnson.R
\name{step_YeoJohnson}
\alias{step_YeoJohnson}
\title{Yeo-Johnson Transformation}
\usage{
step_YeoJohnson(recipe, ..., role = NA, trained = FALSE, lambdas = NULL,
  limits = c(-5, 5), nunique = 5)
}
\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}} for more details.}

\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{lambdas}{A numeric vector of transformation values. This is
\code{NULL} until computed by \code{\link{prep.recipe}}.}

\item{limits}{A length 2 numeric vector defining the range to compute the
transformation parameter lambda.}

\item{nunique}{An integer where data that have less possible values will
not be evaluate for a transformation}
}
\value{
An updated version of \code{recipe} with the
  new step added to the sequence of existing steps (if any).
}
\description{
\code{step_YeoJohnson} creates a \emph{specification} of a recipe step that
  will transform data using a simple Yeo-Johnson transformation.
}
\details{
The Yeo-Johnson transformation is very similar to the Box-Cox but
  does not require the input variables to be strictly positive. In the
  package, the partial log-likelihood function is directly optimized within
  a reasonable set of transformation values (which can be changed by the
  user).

This transformation is typically done on the outcome variable using the
  residuals for a statistical model (such as ordinary least squares). Here,
  a simple null model (intercept only) is used to apply the transformation
  to the \emph{predictor} variables individually. This can have the effect
  of making the variable distributions more symmetric.

If the transformation parameters are estimated to be very closed to the
  bounds, or if the optimization fails, a value of \code{NA} is used and
  no transformation is applied.
}
\examples{

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)

yj_trans <- step_YeoJohnson(rec,  all_numeric())

yj_estimates <- prep(yj_trans, training = biomass_tr)

yj_te <- bake(yj_estimates, biomass_te)

plot(density(biomass_te$sulfur), main = "before")
plot(density(yj_te$sulfur), main = "after")
}
\references{
Yeo, I. K., and Johnson, R. A. (2000). A new family of power
  transformations to improve normality or symmetry. \emph{Biometrika}.
}
\seealso{
\code{\link{step_BoxCox}} \code{\link{recipe}}
  \code{\link{prep.recipe}} \code{\link{bake.recipe}}
}
\concept{
preprocessing transformation_methods
}
\keyword{datagen}
