\HeaderA{stat.diag.da}{Diagonal Discriminant Analysis}{stat.diag.da}
\keyword{models}{stat.diag.da}
\begin{Description}\relax
This function implements a simple Gaussian maximum likelihood
discriminant rule, for diagonal class covariance matrices.
\end{Description}
\begin{Usage}
\begin{verbatim}
stat.diag.da(ls, cll, ts, pool=1)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{ls}] learning set data matrix, with rows corresponding to
cases (i.e., mRNA samples) and columns to predictor variables 
(i.e., genes).
\item[\code{cll}] class labels for learning set, must be consecutive integers.
\item[\code{ts}] test set data matrix, with rows corresponding to cases
and columns to predictor variables.
\item[\code{pool}] logical flag. If \code{pool=1}, the covariance matrices
are assumed to be constant across classes and the discriminant rule
is linear in the data. If \code{pool=0}, the covariance matrices may
vary across classes and the discriminant rule is quadratic in the
data.
\end{ldescription}
\end{Arguments}
\begin{Value}
List containing the following components 
\begin{ldescription}
\item[\code{pred}] vector of class predictions for the test set.
\end{ldescription}
\end{Value}
\begin{Author}\relax
Sandrine Dudoit, \email{sandrine@stat.berkeley.edu} \\
Jane Fridlyand, \email{janef@stat.berkeley.edu}
\end{Author}
\begin{References}\relax
S. Dudoit, J. Fridlyand, and T. P. Speed. Comparison of
Discrimination Methods for the Classification of Tumors Using Gene 
Expression Data. June 2000. (Statistics, UC Berkeley, Tech Report \#576).
\end{References}

