\HeaderA{pls\_ldaCMA-methods}{Partial Least Squares combined with Linear Discriminant Analysis}{pls.Rul.ldaCMA.Rdash.methods}
\aliasA{pls\_ldaCMA,data.frame,missing,formula-method}{pls\_ldaCMA-methods}{pls.Rul.ldaCMA,data.frame,missing,formula.Rdash.method}
\aliasA{pls\_ldaCMA,ExpressionSet,character,missing-method}{pls\_ldaCMA-methods}{pls.Rul.ldaCMA,ExpressionSet,character,missing.Rdash.method}
\aliasA{pls\_ldaCMA,matrix,factor,missing-method}{pls\_ldaCMA-methods}{pls.Rul.ldaCMA,matrix,factor,missing.Rdash.method}
\aliasA{pls\_ldaCMA,matrix,numeric,missing-method}{pls\_ldaCMA-methods}{pls.Rul.ldaCMA,matrix,numeric,missing.Rdash.method}
\keyword{multivariate}{pls\_ldaCMA-methods}
\begin{Description}\relax
-This method constructs a classifier that extracts
Partial Least Squares components that are plugged into
Linear Discriminant Analysis.
The Partial Least Squares components are computed by the package
\code{plsgenomics}.
\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{pls\_ldaCMA}{pls.Rul.ldaCMA}.}
\end{Section}

