% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/autoscale_class.R
\name{autoscale}
\alias{autoscale}
\title{Autoscaling}
\usage{
autoscale(mode = "data", ...)
}
\arguments{
\item{mode}{(character) Mode of action. Allowed values are limited to the following: \itemize{ \item{\code{"data"}: Autoscaling is applied to the data matrix only.}\item{\code{"sample_meta"}: Autoscaling is applied to the sample_meta data only.}\item{\code{"both"}: Autoscaling is applied to both the data matrix and the meta data.}} The default is \code{"data"}.}

\item{...}{Additional slots and values passed to \code{struct_class}.}
}
\value{
A  \code{autoscale} object with the following \code{output} slots:
\tabular{ll}{
\code{autoscaled} \tab          (DatasetExperiment)  \cr
\code{mean_data} \tab          (numeric)  \cr
\code{sd_data} \tab          (numeric)  \cr
\code{mean_sample_meta} \tab          (numeric)  \cr
\code{sd_sample_meta} \tab          (numeric)  \cr
}
}
\description{
Each variable/feature is mean centred and scaled by the standard deviation. The transformed variables have zero-mean and unit-variance.
}
\section{Inheritance}{

A \code{autoscale} object inherits the following \code{struct} classes: \cr\cr
\verb{[autoscale]} >> \verb{[model]} >> \verb{[struct_class]}
}

\examples{
M = autoscale(
      mode = "data")

D = iris_DatasetExperiment()
M = autoscale()
M = model_train(M,D)
M = model_predict(M,D)
}