\HeaderA{pamr.plotcvprob}{A function to plot the cross-validated sample probabilities from
the nearest shrunken centroid
classifier}{pamr.plotcvprob}
\begin{Description}\relax
A function to plot the cross-validated sample probabilities from 
the nearest shrunken centroid
classifier
\end{Description}
\begin{Usage}
\begin{verbatim}
pamr.plotcvprob(fit, data, threshold)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{fit}] The result of a call to pamr.cv
\item[\code{data}] A list with at least two components: x- an expression
genes in the rows, samples in the columns), and y-  a vector of
the class labels for each sample. Same form as data object
used by pamr.train.
\item[\code{threshold}] Threshold value to be used
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
\code{pamr.plotcvprob} plots the cross-validated sample probabilities  the from nearest shrunken
centroid classifier, stratified by the true classses.
\end{Details}
\begin{Value}
\end{Value}
\begin{Author}\relax
Trevor Hastie,Robert Tibshirani, Balasubramanian Narasimhan, and Gilbert Chu
\end{Author}
\begin{References}\relax
\end{References}
\begin{Examples}
\begin{ExampleCode}
set.seed(120)
x <- matrix(rnorm(1000*20),ncol=20)
y <- sample(c(1:4),size=20,replace=TRUE)
mydata <- list(x=x,y=y)
mytrain <-   pamr.train(mydata)
mycv <-  pamr.cv(mytrain,mydata)
pamr.plotcvprob(mycv,mydata,threshold=1.6)


\end{ExampleCode}
\end{Examples}

