rbsurv {rbsurv}R Documentation

Robust likelihood-based survival modeling

Description

This selects survival-associated genes with microarray data.

Usage

        rbsurv(time, status, x, z=NULL, alpha=0.05, gene.ID=NULL, method="efron", 
                   max.n.genes=100, n.iter=10, n.fold=3,  n.seq=1)

Arguments

time a vector for survival times
status a vector for survival status, 0=censored, 1=event
x a matrix for expression values (genes in rows, samples in columns)
z a matrix or vector for covariates
alpha significance level for covariates
gene.ID a vector for gene IDs; if NULL, row numbers are assigned.
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.
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. The default is 100.
n.iter number of iterations
n.fold number of partitions of samples
n.seq number of sequential runs

Value

model survival-associated gene model

Author(s)

HyungJun Cho and Sukwoo Kim

References

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

Examples


library(rbsurv)
data(glioma)
x <- log2(glioma.x) # do normalization if necessary
time <- glioma.y$Time
status <- glioma.y$Status

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


[Package rbsurv version 1.6.0 Index]