\HeaderA{dldaCMA-methods}{Diagonal Discriminant Analysis}{dldaCMA.Rdash.methods}
\aliasA{dldaCMA,data.frame,missing,formula-method}{dldaCMA-methods}{dldaCMA,data.frame,missing,formula.Rdash.method}
\aliasA{dldaCMA,ExpressionSet,character,missing-method}{dldaCMA-methods}{dldaCMA,ExpressionSet,character,missing.Rdash.method}
\aliasA{dldaCMA,matrix,factor,missing-method}{dldaCMA-methods}{dldaCMA,matrix,factor,missing.Rdash.method}
\aliasA{dldaCMA,matrix,numeric,missing-method}{dldaCMA-methods}{dldaCMA,matrix,numeric,missing.Rdash.method}
\keyword{multivariate}{dldaCMA-methods}
\begin{Description}\relax
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.
\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{dldaCMA}{dldaCMA}}.
\end{Section}

