\Header{pamr.predict}{A function producing predicted information,
from a nearest shrunken centroid fit.}
\begin{Description}\relax
A function producing predicted information,
from a nearest shrunken centroid fit\end{Description}
\begin{Usage}
\begin{verbatim}
pamr.predict(fit, newx, threshold, type= c("class", "posterior", "centroid", "nonzero"), prior = object$prior, threshold.scale = object$
        threshold.scale)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{fit}] The result of a call to pamr.train 
\item[\code{newx}] Input (feature) matrix for which predictions are desired
\item[\code{threshold}] The desired threshold value
\item[\code{type}] Type of prediction desired: class predictions, posterior
probabilities, (unshrunken) class centroids, vector of  genes
surviving the threshold
\item[\code{prior}] Prior probabilities for each class. Default is that
specified in "fit"
\item[\code{threshold.scale}] Additional scaling factors to be applied
to the thresholds. Vector of length equal to the number of
classes.
Default is that
specified in "fit".
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
\code{pamr.predict} Give a cross-tabulation of true versus
predicted classes for the fit returned by pamr.train or pamr.cv,
at the specified threshold\end{Details}
\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.predict(mytrain, mydata$x , threshold=1)
 
\end{ExampleCode}
\end{Examples}

