\HeaderA{matplotProbesPDNN}{Plot the PDNN computed probe intensities}{matplotProbesPDNN}
\keyword{hplot}{matplotProbesPDNN}
\begin{Description}\relax
Plot the probe intensities as computed by 'pmcorrect.pdnn' or 'pmcorrect.pdnnpredict'
\end{Description}
\begin{Usage}
\begin{verbatim}
matplotProbesPDNN(x, type="l", ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{x}] a matrix (and attributes) as returned by \code{pmcorrect.pdnn}
or \code{pmcorrect.pdnnpredict}. 
\item[\code{type}] type of plot (same as in \code{matplot})
\item[\code{...}] optional arguments to be passed to \code{matplot} 
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
The crosses are the probe intensities which are considered `ok' by the
outlier detection part of the algorithm, while the circles are the
ones considered `outliers'
\end{Details}
\begin{Value}
Only used for its side-effect.
\end{Value}
\begin{SeeAlso}\relax
\code{\LinkA{pmcorrect.pdnn}{pmcorrect.pdnn}} and \code{\LinkA{pmcorrect.pdnnpredict}{pmcorrect.pdnnpredict}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
# see 'pmcorrect.pdnn'
\end{ExampleCode}
\end{Examples}

