singleGeneCoxph       package:iterativeBMAsurv       R Documentation

_U_n_i_v_a_r_i_a_t_e _C_o_x _P_r_o_p_o_r_t_i_o_n_a_l _H_a_z_a_r_d_s _M_o_d_e_l _f_o_r _s_e_l_e_c_t_i_n_g _t_o_p _l_o_g-_r_a_n_k_e_d _p_r_e_d_i_c_i_t_v_e _v_a_r_i_a_b_l_e_s

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

     This is a univariate technique to rank variables by their
     predictive  relevance for use in survival analysis on microarray
     data. The  log likelihood is computed for each indiviual variable,
     where a larger  log likelihood value indicates a higher rank.

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

     singleGeneCoxph(trainData, survData, censoredData)

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

trainData: Data matrix where columns are variables and rows are
          observations. In the case of gene expression data, the
          columns (variables)  represent genes, while the rows
          (observations) represent patient  samples.

survData: Vector of survival times for the patient samples. Survival
          times  are assumed to be presented in uniform format (e.g.,
          months or  days), and the length of this vector should be
          equal to the number  of rows in trainData.

censoredData: Vector of censor data for the patient samples. In
          general, 0 = censored and 1 = uncensored. The length of this
          vector should equal the number of rows in trainData and the
          number of elements in survData.

_D_e_t_a_i_l_s:

     This function is called by 'iterateBMAsurv.train.predict.assess'.

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

     This function returns a sorted three-column matrix of the training
     data variables. The first column gives the variable names with the
     top log-ranked variable appearing first. The second column gives
     the original indexes of the variables, and the third column gives
     the rank of the variables from 1 through ncol(trainData). The
     matrix is  also written to file in the working R directory under
     the filename 'sorted_loglik.txt'.

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

     Annest, A., Yeung, K.Y., Bumgarner, R.E., and Raftery, A.E.
     (2008). Iterative Bayesian Model Averaging for Survival Analysis.
     Manuscript in Progress.

     Cox, D. (1972). Regression Models and Life Tables.  Journal of the
     Royal Statistical Society Series B 34: 187-220.

     Raftery, A.E. (1995).  Bayesian model selection in social research
     (with Discussion). Sociological Methodology 1995 (Peter V.
     Marsden, ed.), pp. 111-196, Cambridge, Mass.: Blackwells.

     Volinsky, C., Madigan, D., Raftery, A., and Kronmal, R. (1997)
     Bayesian Model Averaging in Proprtional Hazard Models: Assessing
     the Risk of a Stroke.  Applied Statistics 46: 433-448.

     Yeung, K.Y., Bumgarner, R.E. and Raftery, A.E. (2005)  Bayesian
     Model Averaging: Development of an improved multi-class, gene
     selection and classification tool for microarray data. 
     Bioinformatics 21: 2394-2402.

_S_e_e _A_l_s_o:

     'iterateBMAsurv.train.predict.assess',   'printTopGenes',
     'trainData', 'trainSurv',  'trainCens'

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

     library(BMA)
     library(iterativeBMAsurv)
     data(trainData)
     data(trainSurv)
     data(trainCens)

     sorted.genes <- singleGeneCoxph(trainData, trainSurv, trainCens)

     ## Write top 100 genes to file
     sorted.top.genes <- printTopGenes(retMatrix=sorted.genes, 100, trainData)

     ## The file, 'sorted_topCoxphGenes_100', is now in the working R directory.

