\HeaderA{flexdaCMA-methods}{Flexible Discriminant Analysis}{flexdaCMA.Rdash.methods}
\aliasA{flexdaCMA,data.frame,missing,formula-method}{flexdaCMA-methods}{flexdaCMA,data.frame,missing,formula.Rdash.method}
\aliasA{flexdaCMA,ExpressionSet,character,missing-method}{flexdaCMA-methods}{flexdaCMA,ExpressionSet,character,missing.Rdash.method}
\aliasA{flexdaCMA,matrix,factor,missing-method}{flexdaCMA-methods}{flexdaCMA,matrix,factor,missing.Rdash.method}
\aliasA{flexdaCMA,matrix,numeric,missing-method}{flexdaCMA-methods}{flexdaCMA,matrix,numeric,missing.Rdash.method}
\keyword{multivariate}{flexdaCMA-methods}
\begin{Description}\relax
This method is experimental.

It is easy to show that, after appropriate scaling of the predictor matrix \code{X},
Fisher's Linear Discriminant Analysis is equivalent to Discriminant Analysis
in the space of the  fitted values from the linear regression of the
\code{nlearn x K} indicator matrix of the class labels on \code{X}.
This gives rise to 'nonlinear discrimant analysis' methods that expand
\code{X} in a suitable, more flexible basis. In order to avoid overfitting,
penalization is used. In the implemented version, the linear model is replaced
by a generalized additive one, using the package \code{mgcv}.
\end{Description}
\begin{Section}{Methods}
\describe{

\item[X = "matrix", y = "numeric", f = "missing"] signature 1

\item[X = "matrix", y = "factor", f = "missing"] signature 2

\item[X = "data.frame", y = "missing", f = "formula"] signature 3

\item[X = "ExpressionSet", y = "character", f = "missing"] signature 4
}
For further argument and output information, consult
\code{\LinkA{flexdaCMA}{flexdaCMA}}.
\end{Section}

