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=1, gene.ID=NULL, method="efron", 
                   n.iter=10, n.fold=3,  n.seq=1,  seed=1234, max.n.genes=nrow(x))

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 for risk factors
alpha significance level for evaluating risk factors; significant risk factors included with the alpha level if alpha < 1
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.
n.iter the number of iterations for gene selection
n.fold the number of partitions of samples
n.seq the number of sequential runs or multiple models
seed a seed for sample partitioning
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

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

Author(s)

HyungJun Cho, Sukwoo Kim, Soo-heang Eo, and Jaewoo Kang

References

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

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


[Package rbsurv version 1.10.0 Index]