\name{rbsurv}
\alias{rbsurv}
\title{Robust likelihood-based survival modeling}
\description{
This selects survival-associated genes with microarray data.
}
\usage{
        rbsurv(time, status, x, z=NULL, alpha=1, gene.ID=NULL, method="efron", 
                   n.iter=10, n.fold=3,  n.seq=1,  seed=1234, max.n.genes=nrow(x))
}

\arguments{

\item{time}{a vector for survival times}
\item{status}{a vector for survival status, 0=censored, 1=event}
\item{x}{a matrix for expression values (genes in rows, samples in columns)}
\item{z}{a matrix for risk factors}
\item{alpha}{significance level for evaluating  risk factors; significant risk factors included with the alpha level if alpha < 1}
\item{gene.ID}{a vector for gene IDs; if NULL, row numbers are assigned.}
\item{method}{a character string specifying the method for tie handling.  Choose one of "efron", "breslow", 
        "exact". The default is "efron".  If there are no tied death times all the methods are equivalent. }
\item{n.iter}{the number of iterations for gene selection} 
\item{n.fold}{the number of partitions of samples} 
\item{n.seq}{the number of sequential runs or multiple models}
\item{seed}{a seed for sample partitioning}
\item{max.n.genes}{the maximum number of genes considered. 
If the number of the input genes is greater than the given number, it is reduced by fitting individual Cox models.}
}

\value{
   \item{model}{ survival-associated gene model}
   \item{n.genes}{number of genes}
   \item{n.samples}{number of samples}
   \item{method}{method for tie handling} 
   \item{covariates}{covariates}
   \item{n.iter}{number of iterations for gene seletion} 
   \item{n.fold}{number of partitions of samples} 
   \item{n.seq}{number of sequential runs or multiple models}
   \item{gene.list}{a list of genes included in the models}
}

\references{
Cho H et al. Robust likelihood-based survival modeling for microarray gene expression data, submitted.
 }

\author{
   HyungJun Cho, Sukwoo Kim, Soo-heang Eo, and Jaewoo Kang
}


\examples{

library(rbsurv)
data(gliomaSet)
x <- exprs(gliomaSet)
x <- log2(x)
time <- gliomaSet$Time
status <- gliomaSet$Status
z <- cbind(gliomaSet$Age, gliomaSet$Gender) 

fit <- rbsurv(time=time, status=status, x=x,  method="efron", max.n.genes=20, n.iter=10, n.fold=3,  n.seq=1)
fit$model

}

\keyword{models}