% Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/PCA_class.R

 \name{PCA}

 \alias{PCA}

 \title{Principal Component Analysis (PCA)}

 \usage{

 PCA(number_components = 2, ...)

 }

 \arguments{

 \item{number_components}{(numeric, integer) The number of Principal Components calculated. The default is \code{2}.\cr}

 \item{...}{Additional slots and values passed to \code{struct_class}.}

 }

 \value{

 A  \code{PCA} object with the following \code{output} slots:
 \tabular{ll}{
 \code{scores} \tab          (DatasetExperiment) A matrix of PCA scores where each column corresponds to a Principal Component. \cr
 \code{loadings} \tab          (data.frame)  \cr
 \code{eigenvalues} \tab          (data.frame)  \cr
 \code{ssx} \tab          (numeric)  \cr
 \code{correlation} \tab          (data.frame)  \cr
 \code{that} \tab          (DatasetExperiment)  \cr
 }

 }

 \description{

 PCA is a multivariate data reduction technique. It summarises the data in a smaller number of Principal Components that maximise variance.

 }

 \section{Inheritance}{
 
 A \code{PCA} object inherits the following \code{struct} classes: \cr\cr

 \verb{[PCA]} >> \verb{[model]} >> \verb{[struct_class]}

 }
 

 \examples{

 M = PCA(
       number_components = 2)
 

 }