rbsurv {rbsurv} | R Documentation |
This selects survival-associated genes with microarray data.
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))
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. |
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 |
HyungJun Cho, Sukwoo Kim, Soo-heang Eo, and Jaewoo Kang
Cho H et al. Robust likelihood-based survival modeling for microarray gene expression data, submitted.
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