rbsurv                package:rbsurv                R Documentation

_R_o_b_u_s_t _l_i_k_e_l_i_h_o_o_d-_b_a_s_e_d _s_u_r_v_i_v_a_l _m_o_d_e_l_i_n_g

_D_e_s_c_r_i_p_t_i_o_n:

     This selects survival-associated genes with microarray data.

_U_s_a_g_e:

             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))

_A_r_g_u_m_e_n_t_s:

    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.

_V_a_l_u_e:

   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

_A_u_t_h_o_r(_s):

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

_R_e_f_e_r_e_n_c_e_s:

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

_E_x_a_m_p_l_e_s:

     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

