benhur-class           package:clusterStab           R Documentation

_C_l_a_s_s "_b_e_n_h_u_r", _a _c_l_a_s_s _f_o_r _e_s_t_i_m_a_t_i_n_g _c_l_u_s_t_e_r_s _i_n _m_i_c_r_o_a_r_r_a_y
_d_a_t_a, _a_n_d _m_e_t_h_o_d_s _f_o_r _v_i_s_u_a_l_i_z_i_n_g _t_h_e_m.

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

     A specialized class representation used for estimating clusters in
     microarray data.

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     Objects are usually created by a call to 'benhur', although
     technically a new object can also be created by a call to
     'new("benhur",...)'. However, this second method is usually not
     worth the work required.

_S_l_o_t_s:


     '_j_a_c_c_a_r_d_s': Object of class '"list"', containing the jaccard
          vectors; these indicate the proportion of pairwise similarity
          between clusters formed from subsets of the data.

     '_s_i_z_e': Object of class '"vector"', only used for plotting.

     '_i_t_e_r_a_t_i_o_n_s': Object of class '"vector"', containing the number of
          iterations. Defaults to 100.

     '_f_r_e_q': Object of class '"vector"', containing the proportion of
          the data used for subsampling. 

_M_e_t_h_o_d_s:


     _e_c_d_f 'signature(x = "benhur")': Plot an empirical CDF. This can be
          used to help determine the number of clusters in the data.
          The most likely (e.g., most stable number) of clusters will
          have a CDF that is concentrated at or near one. See vignette
          for more information.

     _h_i_s_t 'signature(x = "benhur")': Plot histograms for all clusters
          tested. The most likely (e.g., most stable number) of
          clusters will have a histogram in which the data are
          clustered at or near one. See vignette for more information.

     _s_h_o_w 'signature(object = "benhur")': Gives a nice summary. 

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

     James W. MacDonald <jmacdon@med.umich.edu>

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

     A. Ben-Hur, A. Elisseeff and I. Guyon. A stability based method
     for discovering structure in clustered data. Pacific Symposium on
     Biocomputing, 2002. Smolkin, M. and Ghosh, D. (2003).  Cluster
     stability scores for microarray data in cancer studies. BMC
     Bioinformatics 4, 36 - 42.

