iterativeBMAsurv-package  package:iterativeBMAsurv  R Documentation

_T_h_e _I_t_e_r_a_t_i_v_e _B_a_y_e_s_i_a_n _M_o_d_e_l _A_v_e_r_a_g_i_n_g (_B_M_A) _a_l_g_o_r_i_t_h_m _f_o_r _s_u_r_v_i_v_a_l _a_n_a_l_y_s_i_s

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

     The iterative Bayesian Model Averaging (BMA) algorithm for
     survival analysis is a variable selection method for applying
     survival analysis to microarray data..

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


       Package:  iterativeBMAsurv
       Type:     Package
       Version:  0.1.0
       Date:     2008-3-24
       License:  GPL version 2 or higher

     The function 'iterateBMAsurv.train' selects relevant variables by
     iteratively applying the 'bic.surv' function from the 'BMA'
     package until all variables in the training data are exhausted.
     The variables are  assumed to be pre-sorted by rank when this
     function is called. The function  'iterateBMAsurv.train.wrapper'
     acts as a wrapper for 'iterateBMAsurv.train',  returning the names
     of the selected variables and an object of class 'bic.surv'  if
     the iterations exhaust all variables in the training set (-1
     otherwise). Again,  the variables are assumed to be pre-sorted by
     rank, so calling this function  allows users to experiment with
     different univariate ranking measures. The function 
     'iterateBMAsurv.train.predict.assess' combines the training,
     prediction, and  assessment phases. It returns a list consisting
     of the numbers of selected genes  and models from the training
     phase, the predicted risk scores of the test samples,  and the
     overall survival analysis statistics indicating the difference
     between risk  groups (p-value, chi-square statistic, and variance
     matrix). It also writes a  Kaplan-Meier survival analysis curve to
     file, which serves as a pictorial nonparametric estimator of the
     difference between risk groups. The variables are not assumed to
     be  pre-sorted by rank when this function is called.
     'iterateBMAsurv.train.predict.assess'  calls 'singleGeneCoxph',
     which ranks the genes based on their log likelihood scores  using
     Cox Proportional Hazards Regression.
     'iterateBMAsurv.train.predict.assess'  calls
     'iterateBMAsurv.train.wrapper' in its training phase, so if Cox
     Proportional  Hazards Regression is the desired univariate ranking
     algorithm, then calling this  function with the training and
     testing sets is all that is necessary for a complete  survival
     analysis run. The function 'crossVal' performs k runs of n-fold
     cross validation on a training data set, where k and n are
     specified by the user.  'crossVal' calls
     'iterateBMAsurv.train.predict.assess' during each fold, so Cox
     Proportional Hazards Regression is the univariate ranking measure
     for this function.

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

     Ka Yee Yeung, University of Washington, Seattle, WA,  Amalia
     Annest, University of Washington, Tacoma, WA

     Maintainer: Ka Yee Yeung <kayee@u.washington.edu> Amalia Annest
     <amanu@u.washington.edu>

_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.

     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',   'iterateBMAsurv.train.wrapper',
     'iterateBMAsurv.train.predict.assess', 'singleGeneCoxph',
     'predictiveAssessCategory', 'crossVal', 'trainData', 'trainSurv', 
     'trainCens', 'testData', 'testSurv', 'testCens'

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

     library (BMA)
     library (iterativeBMAsurv)
     data(trainData)
     data(trainSurv)
     data(trainCens)
     data(testData)
     data(testSurv)
     data(testCens)

     ## Use p=10 genes and nbest=5 for fast computation
     ret.bma <- iterateBMAsurv.train.predict.assess (train.dat=trainData, test.dat=testData, surv.time.train=trainSurv, surv.time.test=testSurv, cens.vec.train=trainCens, cens.vec.test=testCens, p=10, nbest=5)

     ## Extract the statistics from this survival analysis run
     number.genes <- ret.bma$nvar
     number.models <- ret.bma$nmodel
     evaluate.success <- ret.bma$statistics

     ## Perform 1 run of 2-fold cross validation on the training set, using p=10 genes and nbest=5 for fast computation
     cv <- crossVal(exset=trainData, survTime=trainSurv, censor=trainCens, diseaseType="DLBCL", noFolds=2, noRuns=1, p=10, nbest=5)

