\name{pamr.cv}
\alias{pamr.cv}
\title{ A function to cross-validate the nearest shrunken centroid
  classifier}
\description{A function to cross-validate the nearest shrunken centroid
  classifier produced by pamr.train}

\usage{
pamr.cv(fit, data,  nfold = NULL, folds = NULL,...)
}

\arguments{
  \item{fit}{The result of a call to pamr.train}
  \item{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{nfold}{Number of cross-validation folds. Default is the
    smallest
    class size}
  \item{folds}{A list with nfold components, each component a vector of
    indices of the samples in that fold. By default a (random) balanced
    cross-validation is used}.
  \item{...}{Any additional arguments that are to be passed to pamr.train}
}    
\details{
  \code{pamr.cv} carries out cross-validation for a nearest shrunken
  centroid classifier.
}
\value{
  A list with components
  \item{threshold}{A vector of the thresholds tried in the shrinkage}
  \item{errors}{The number of cross-validation  errors for each threshold value}
  \item{loglik}{The cross-validated multinomial log-likelihood value
    for each threshold value}
  \item{size}{A vector of the number of genes that survived the
    thresholding, for each threshold value tried.}.
  \item{yhat}{A matrix of size n by nthreshold, containing the
    cross-validated
    class predictions for each threshold value,  in each column}
  \item{prob}{A matrix of size n by nthreshold, containing the
    cross-validated
    class probabilities for each threshold value,  in each column}
  \item{folds}{ The cross-validation folds used}
  \item{cv.objects}{Train objects (output of pamr.train),  from each of the CV folds}
  \item{call}{The calling sequence used}
  
}

\references{}


\author{ Trevor Hastie,Robert Tibshirani, Balasubramanian Narasimhan, and Gilbert Chu  }

\examples{
set.seed(120)
x <- matrix(rnorm(1000*20),ncol=20)
y <- sample(c(1:4),size=20,replace=TRUE)

mydata <- list(x=x, y=factor(y), geneid=as.character(1:nrow(x)),
               genenames=paste("g", as.character(1:nrow(x)), sep=""))

mytrain <-   pamr.train(mydata)
mycv <- pamr.cv(mytrain, mydata)
}

\keyword{ }