\HeaderA{shrinkldaCMA}{Shrinkage linear discriminant analysis}{shrinkldaCMA}
\keyword{multivariate}{shrinkldaCMA}
\begin{Description}\relax
Linear Discriminant Analysis combined with the
James-Stein-Shrinkage approach of Schaefer and Strimmer (2005)
for the covariance matrix.

Currently still an experimental version.

For \code{S4} method information, see \LinkA{shrinkldaCMA-methods}{shrinkldaCMA.Rdash.methods}
\end{Description}
\begin{Usage}
\begin{verbatim}
shrinkldaCMA(X, y, f, learnind, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{X}] Gene expression data. Can be one of the following:
\Itemize{
\item A \code{matrix}. Rows correspond to observations, columns to variables.
\item A \code{data.frame}, when \code{f} is \emph{not} missing (s. below).
\item An object of class \code{ExpressionSet}.
}

\item[\code{y}] Class labels. Can be one of the following:
\Itemize{
\item A \code{numeric} vector.
\item A \code{factor}.
\item A \code{character} if \code{X} is an \code{ExpressionSet} that
specifies the phenotype variable.
\item \code{missing}, if \code{X} is a \code{data.frame} and a
proper formula \code{f} is provided.
}
\bold{WARNING}: The class labels will be re-coded to
range from \code{0} to \code{K-1}, where \code{K} is the
total number of different classes in the learning set.

\item[\code{f}] A two-sided formula, if \code{X} is a \code{data.frame}. The
left part correspond to class labels, the right to variables.
\item[\code{learnind}] An index vector specifying the observations that
belong to the learning set. May be \code{missing};
in that case, the learning set consists of all
observations and predictions are made on the
learning set.
\item[\code{...}] Further arguments to be passed to \code{cov.shrink} from the
package \code{corpcor}
\end{ldescription}
\end{Arguments}
\begin{Value}
An object of class \code{\LinkA{cloutput}{cloutput}}.
\end{Value}
\begin{Note}\relax
This is still an experimental version.

Covariance shrinkage is performed by calling functions
from the package \code{corpcor}.

Variable selection is \emph{not} necessary.
\end{Note}
\begin{Author}\relax
Martin Slawski \email{martin.slawski@campus.lmu.de}

Anne-Laure Boulesteix \url{http://www.slcmsr.net/boulesteix}
\end{Author}
\begin{References}\relax
Schaefer, J., Strimmer, K. (2005).

A shrinkage approach to large-scale covariance estimation and implications for functional genomics.

\emph{Statististical Applications in Genetics and Molecular Biology, 4:32. }
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{compBoostCMA}{compBoostCMA}}, \code{\LinkA{dldaCMA}{dldaCMA}}, \code{\LinkA{ElasticNetCMA}{ElasticNetCMA}},
\code{\LinkA{fdaCMA}{fdaCMA}}, \code{\LinkA{flexdaCMA}{flexdaCMA}}, \code{\LinkA{gbmCMA}{gbmCMA}},
\code{\LinkA{knnCMA}{knnCMA}}, \code{\LinkA{ldaCMA}{ldaCMA}}, \code{\LinkA{LassoCMA}{LassoCMA}},
\code{\LinkA{nnetCMA}{nnetCMA}}, \code{\LinkA{pknnCMA}{pknnCMA}}, \code{\LinkA{plrCMA}{plrCMA}},
\code{\LinkA{pls\_ldaCMA}{pls.Rul.ldaCMA}}, \code{\LinkA{pls\_lrCMA}{pls.Rul.lrCMA}}, \code{\LinkA{pls\_rfCMA}{pls.Rul.rfCMA}},
\code{\LinkA{pnnCMA}{pnnCMA}}, \code{\LinkA{qdaCMA}{qdaCMA}}, \code{\LinkA{rfCMA}{rfCMA}},
\code{\LinkA{scdaCMA}{scdaCMA}}, \code{\LinkA{svmCMA}{svmCMA}}.
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
### load Golub AML/ALL data
data(golub)
### extract class labels
golubY <- golub[,1]
### extract gene expression
golubX <- as.matrix(golub[,-1])
### select learningset
ratio <- 2/3
set.seed(111)
learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
### run  shrinkage-LDA
result <- shrinkldaCMA(X=golubX, y=golubY, learnind=learnind)
### show results
show(result)
ftable(result)
plot(result)\end{ExampleCode}
\end{Examples}

