imageplot.iterate.bma      package:iterativeBMA      R Documentation

_A_n _i_m_a_g_e _p_l_o_t _v_i_s_u_a_l_i_z_a_t_i_o_n _t_o_o_l

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

     Create a visualization of the models and variables selected by the
     iterative BMA algorithm.

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

     imageplot.iterate.bma (bicreg.out, color="default", ...)

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

bicreg.out: An object of type 'bicreg', 'bic.glm' or 'bic.surv'

   color: The color of the plot. The value "default" uses the  current
          default R color scheme for image. The value  "blackandwhite"
          produces a black and white image.

     ...: Other parameters to be passed to the image and axis
          functions.

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

     This function is a modification of the 'imageplot.bma' function
     from the 'BMA' package. The difference is that variables (genes)
     with 'probne0' equal to 0 are removed before plotting. The
     arguments of this function is identical to those in
     'imageplot.bma'.

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

     An heatmap-style image, with the BMA selected variables on the
     vertical axis, and the BMA selected models on the horizontal axis.
     The variables (genes) are sorted in descreasing order of the
     posterior probability  that the variable is not equal to 0
     ('probne0') from top to  bottom.  The models are sorted in
     descreasing order of the model posterior probability ('postprob')
     from left to right.

_N_o_t_e:

     The 'BMA' and 'Biobase' packages are required.

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

     Clyde, M. (1999)  Bayesian Model Averaging and Model Search
     Strategies (with discussion). In Bayesian Statistics 6. J.M.
     Bernardo, A.P. Dawid, J.O. Berger, and A.F.M. Smith eds. Oxford
     University Press, pages 157-185.

     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'

_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)

     ## produce an image plot to visualize the selected genes and models
     imageplot.iterate.bma (ret.bic.glm)

