\HeaderA{pls\_lrCMA-methods}{Partial Least Squares followed by logistic regression}{pls.Rul.lrCMA.Rdash.methods}
\aliasA{pls\_lrCMA,data.frame,missing,formula-method}{pls\_lrCMA-methods}{pls.Rul.lrCMA,data.frame,missing,formula.Rdash.method}
\aliasA{pls\_lrCMA,ExpressionSet,character,missing-method}{pls\_lrCMA-methods}{pls.Rul.lrCMA,ExpressionSet,character,missing.Rdash.method}
\aliasA{pls\_lrCMA,matrix,factor,missing-method}{pls\_lrCMA-methods}{pls.Rul.lrCMA,matrix,factor,missing.Rdash.method}
\aliasA{pls\_lrCMA,matrix,numeric,missing-method}{pls\_lrCMA-methods}{pls.Rul.lrCMA,matrix,numeric,missing.Rdash.method}
\keyword{multivariate}{pls\_lrCMA-methods}
\begin{Description}\relax
This method constructs a classifier that extracts
Partial Least Squares components that form the the covariates
in a binary logistic regression model.
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\_lrCMA}{pls.Rul.lrCMA}}
\end{Section}

