\HeaderA{fdaCMA-methods}{Fisher's Linear Discriminant Analysis}{fdaCMA.Rdash.methods}
\aliasA{fdaCMA,data.frame,missing,formula-method}{fdaCMA-methods}{fdaCMA,data.frame,missing,formula.Rdash.method}
\aliasA{fdaCMA,ExpressionSet,character,missing-method}{fdaCMA-methods}{fdaCMA,ExpressionSet,character,missing.Rdash.method}
\aliasA{fdaCMA,matrix,factor,missing-method}{fdaCMA-methods}{fdaCMA,matrix,factor,missing.Rdash.method}
\aliasA{fdaCMA,matrix,numeric,missing-method}{fdaCMA-methods}{fdaCMA,matrix,numeric,missing.Rdash.method}
\keyword{multivariate}{fdaCMA-methods}
\begin{Description}\relax
Fisher's Linear Discriminant Analysis constructs a subspace of
'optimal projections' in which classification is performed.
The directions of optimal projections are computed by the
function \code{cancor} from the package \code{stats}. For
an exhaustive treatment, see e.g. Ripley (1996).
\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 references, further argument and output information, consult
\code{\LinkA{fdaCMA}{fdaCMA}}.
\end{Section}

