plot                   package:CMA                   R Documentation

_B_a_r_p_l_o_t _o_f _v_a_r_i_a_b_l_e _i_m_p_o_r_t_a_n_c_e

_D_e_s_c_r_i_p_t_i_o_n:

     This method can be seen as a visual pendant to 'toplist'. The plot
     visualizes variable importance by a barplot. The height of the
     barplots correspond to variable importance. What variable
     importance exactly means depends on the method chosen when calling
     'GeneSelection', s. 'genesel'.

_A_r_g_u_m_e_n_t_s:

       x: An object of class 'genesel'

     top: Number of top genes whose variable importance should be
          displayed. Defaults to 10.

    iter: Iteration number ('learningset') for which variable
          importance should be displayed.

     ...: Further graphical options passed to 'barplot'.

_V_a_l_u_e:

     No return.

_N_o_t_e:

     Note the following

        *  If 'scheme = "multiclass"', only one plot will be made.
           Otherwise, one plot will be made for each binary scenario
           (depending on whether '"scheme"' is '"one-vs-all"' or
           '"pairwise"').

        *  Variable importance do not make sense for variable selection
           (ranking) methods that are essentially discrete, such as the
           Wilcoxon-Rank sum statistic or the Kruskal-Wallis statistic.

        *  For the methods '"lasso", "elasticnet", "boosting"' the
           number of nonzero coefficients can be very small, resulting
           in bars of height zero if 'top' has been chosen too large.

_A_u_t_h_o_r(_s):

     Martin Slawski martin.slawski@campus.lmu.de

     Anne-Laure Boulesteix <URL: http://www.slcmsr.net/boulesteix>

_S_e_e _A_l_s_o:

     'genesel', 'GeneSelection', 'toplist'

