% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DFA_class.R
\name{DFA}
\alias{DFA}
\title{Discriminant Factor Analysis}
\usage{
DFA(factor_name, number_components = 2, ...)
}
\arguments{
\item{factor_name}{(character) The name of a sample-meta column to use.}

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

\item{...}{Additional slots and values passed to \code{struct_class}.}
}
\value{
A  \code{DFA} object with the following \code{output} slots:
\tabular{ll}{
\code{scores} \tab          (DatasetExperiment)  \cr
\code{loadings} \tab          (data.frame)  \cr
\code{eigenvalues} \tab          (data.frame)  \cr
\code{that} \tab          (DatasetExperiment)  \cr
}
}
\description{
Discriminant Factor Analysis (DFA) is a supervised classification method. Using a linear combination of the input variables, DFA finds new orthogonal axes (canonical values) to minimize the variance within each given class and maximize variance between classes.
}
\section{Inheritance}{

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

\examples{
M = DFA(
      factor_name = "V1",
      number_components = 2)

D = iris_DatasetExperiment()
M = DFA(factor_name='Species')
M = model_apply(M,D)
}
\references{
Manly B (1986). \emph{Multivariate Statistical Methods: A Primer}. Chapman
and Hall, Boca Raton.
}