\name{dldaCMA}
\alias{dldaCMA}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Diagonal Discriminant Analysis}
\description{
Performs a diagonal discriminant analysis under the assumption
of a multivariate normal distribution in each classes (with equal,
diagonally structured) covariance matrices.
The method is also known under the name 'naive Bayes' classifier.

For \code{S4} method information, see \link{dldaCMA-methods}.
}
\usage{
dldaCMA(X, y, f, learnind, models=FALSE, ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
 \item{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{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{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{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{models}{a logical value indicating whether the model object shall be returned }
  \item{\dots}{Currently unused argument.}
}
\note{As opposed to linear or quadratic discriminant analysis, variable
      selection is not strictly necessary.}
\value{An object of class \code{\link{cloutput}}.}
\references{McLachlan, G.J. (1992).

           Discriminant Analysis and Statistical Pattern Recognition.

           \emph{Wiley, New York}}

\author{Martin Slawski \email{ms@cs.uni-sb.de}

        Anne-Laure Boulesteix \email{boulesteix@ibe.med.uni-muenchen.de}}

\seealso{\code{\link{compBoostCMA}}, \code{\link{ElasticNetCMA}},
         \code{\link{fdaCMA}}, \code{\link{flexdaCMA}}, \code{\link{gbmCMA}},
         \code{\link{knnCMA}}, \code{\link{ldaCMA}}, \code{\link{LassoCMA}},
         \code{\link{nnetCMA}}, \code{\link{pknnCMA}}, \code{\link{plrCMA}},
         \code{\link{pls_ldaCMA}}, \code{\link{pls_lrCMA}}, \code{\link{pls_rfCMA}},
         \code{\link{pnnCMA}}, \code{\link{qdaCMA}}, \code{\link{rfCMA}},
         \code{\link{scdaCMA}}, \code{\link{shrinkldaCMA}}, \code{\link{svmCMA}}}
\examples{
### 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 DLDA
dldaresult <- dldaCMA(X=golubX, y=golubY, learnind=learnind)
### show results
show(dldaresult)
ftable(dldaresult)
plot(dldaresult)
### multiclass example:
### load Khan data
data(khan)
### extract class labels
khanY <- khan[,1]
### extract gene expression
khanX <- as.matrix(khan[,-1])
### select learningset
set.seed(111)
learnind <- sample(length(khanY), size=floor(ratio*length(khanY)))
### run LDA
ldaresult <- dldaCMA(X=khanX, y=khanY, learnind=learnind)
### show results
show(dldaresult)
ftable(dldaresult)
plot(dldaresult)
}
\keyword{multivariate}