\HeaderA{pamr.predictmany}{A function giving prediction information for many threshold values,
from a nearest shrunken centroid fit.}{pamr.predictmany}
\begin{Description}\relax
A function giving prediction information for many threshold values,
from a nearest shrunken centroid fit
\end{Description}
\begin{Usage}
\begin{verbatim}
pamr.predictmany(fit, newx, threshold=fit$threshold, prior =fit$prior, threshold.scale = fit$
        threshold.scale, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{fit}] The result of a call to pamr.train 
\item[\code{newx}] Matrix of features at which predictions are to be made
\item[\code{threshold}] The desired threshold values
\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".
\item[\code{...}] Additional arguments to be passed to pamr.predict
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
\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)

pamr.predictmany(mytrain, mydata$x)
 
\end{ExampleCode}
\end{Examples}

