\HeaderA{pnnCMA}{Probabilistic Neural Networks}{pnnCMA}
\keyword{multivariate}{pnnCMA}
\begin{Description}\relax
Probabilistic Neural Networks is the term Specht (1990) used
for a Gaussian kernel estimator for the conditional class
densities.

For \code{S4} method information, see \LinkA{pnnCMA-methods}{pnnCMA.Rdash.methods}.
\end{Description}
\begin{Usage}
\begin{verbatim}
pnnCMA(X, y, f, learnind, sigma = 1)
\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}.

Each variable (gene) will be scaled for unit variance and zero mean.
}
\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. For this method, this
must \emph{not} be \code{missing}.
\item[\code{sigma}] Standard deviation of the Gaussian Kernel used.

This hyperparameter should be tuned, s. \code{\LinkA{tune}{tune}}.
The default is \code{1}, but this generally  does not
lead to good results. Actually, this method reacts
very sensitively to the value of sigma. Take care
if warnings appear related to the particular choice.
\end{ldescription}
\end{Arguments}
\begin{Value}
An object of class \code{\LinkA{cloutput}{cloutput}}.
\end{Value}
\begin{Note}\relax
There is actually no strong relation of this method to Feed-Forward
Neural Networks, s. \code{\LinkA{nnetCMA}{nnetCMA}}.
\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
Specht, D.F. (1990).

Probabilistic Neural Networks.
\emph{Neural Networks, 3, 109-118}.
\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{qdaCMA}{qdaCMA}}, \code{\LinkA{rfCMA}{rfCMA}},
\code{\LinkA{scdaCMA}{scdaCMA}}, \code{\LinkA{shrinkldaCMA}{shrinkldaCMA}}, \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 from first 10 genes
golubX <- as.matrix(golub[,2:11])
### select learningset
ratio <- 2/3
set.seed(111)
learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
### run PNN
pnnresult <- pnnCMA(X=golubX, y=golubY, learnind=learnind, sigma = 3)
### show results
show(pnnresult)
ftable(pnnresult)
plot(pnnresult)
\end{ExampleCode}
\end{Examples}

