iterativeBMA-package      package:iterativeBMA      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

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

     The iterative Bayesian Model Averaging (BMA) algorithm is a
     variable  selection and classification algorithm with an
     application of  classifying 2-class microarray samples, as
     described in Yeung,  Bumgarner and Raftery (Bioinformatics 2005,
     21: 2394-2402).

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


       Package:  iterativeBMA
       Type:     Package
       Version:  0.1.0
       Date:     2005-12-30
       License:  GPL version 2 or higher

     The function 'iterateBMAglm.train' selects relevant variables by
     iteratively applying the 'bic.glm' function from the 'BMA'
     package. The data is assumed to consist of two classes.  The
     function 'iterateBMAglm.train.predict' combines the training and
     prediction phases, and returns the predicted posterior
     probabilities that each test sample belongs to class 1. The
     function 'iterateBMAglm.train.predict.test' combines the training,
     prediction and test phases, and returns a list consisting of the
     numbers of selected genes and models using the training data, the
     number of classification errors and the Brier Score on the test
     set.

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

     Ka Yee Yeung, University of Washington, Seattle, WA, with
     contributions from Adrian Raftery and Ian Painter

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

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

     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:

     'iterateBMAglm.train.predict',  
     'iterateBMAglm.train.predict.test', 'bma.predict', 'brier.score'

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

     library (Biobase)
     library (BMA)
     library (iterativeBMA)
     data(trainData)
     data(trainClass)

     ## training phase: select relevant genes
     ret.bic.glm <- iterateBMAglm.train (train.expr.set=trainData, trainClass, p=100)

     ## get the selected genes with probne0 > 0
     ret.gene.names <- ret.bic.glm$namesx[ret.bic.glm$probne0 > 0]

     data (testData)

     ## get the subset of test data with the genes from the last iteration of bic.glm
     curr.test.dat <- t(exprs(testData)[ret.gene.names,])

     ## to compute the predicted probabilities for the test samples
     y.pred.test <- apply (curr.test.dat, 1, bma.predict, postprobArr=ret.bic.glm$postprob, mleArr=ret.bic.glm$mle)

     ## compute the Brier Score if the class labels of the test samples are known
     data (testClass)
     brier.score (y.pred.test, testClass)

