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=0.05, gene.ID=NULL, method="efron", 
                        max.n.genes=100, n.iter=10, n.fold=3,  n.seq=1)

_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 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

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

   model: survival-associated gene model

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

     HyungJun Cho and Sukwoo Kim

_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(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

