plot-methods             package:flexmix             R Documentation

_R_o_o_t_o_g_r_a_m _o_f _P_o_s_t_e_r_i_o_r _P_r_o_b_a_b_i_l_i_t_i_e_s

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

     The 'plot' method for 'flexmix-class' objects gives a rootogram or
     histogram of the posterior probabilities.

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

     ## S4 method for signature 'flexmix, missing':
     plot(x, y, mark=NULL, markcol="red",
       eps=1e-4, root=TRUE, ylim=TRUE, main=NULL, mfrow=NULL, ...)

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

       x: an object of class '"flexmix"'

       y: not used

    mark: integer, mark posteriors of this component

 markcol: color used for marking components

     eps: posteriors smaller than 'eps' are ignored

    root: if 'TRUE', a rootogram of the posterior probabilities is
          drawn, otherwise a standard histogram

    ylim: A logical value or a numeric vector of length n2. If 'TRUE',
          the y axes of all rootograms are aligned to have the same
          limits, if 'FALSE' each y axis is scaled separately. If a
          numeric vector is specified it is used as usual.

    main: main title of the plot

   mfrow: layout of the plot

     ...: further graphical parameters

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

     For each mixture component a rootogram or histogram of the
     posterior probabilities of all observations is  drawn. Rootograms
     are very similar to histograms, the only difference is that the
     height of the bars correspond to square roots of counts rather
     than the counts themselves, hence low counts are more visible and
     peaks less emphasized.

     Usually in each component a lot of observations have posteriors
     close to zero, resulting in a high count for the corresponing bin
     in the rootogram which obscures the information in the other bins.
     To avoid this problem, all probabilities with a posterior below
     'eps' are ignored.

     A peak at probability one indicates that a mixture component is
     well seperated from the other components, while no peak at one
     and/or significant mass in the middle of the unit interval
     indicates overlap with other components.

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

     Friedrich Leisch

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

     Friedrich Leisch. FlexMix: A general framework for finite mixture
     models and latent class regression in R. Journal of Statistical
     Software, 11(8), 2004. http://www.jstatsoft.org/v11/i08/

     Jeremy Tantrum, Alejandro Murua and Werner Stuetzle. Assessment
     and pruning of hierarchical model based clustering. Proceedings of
     the 9th ACM SIGKDD international conference on Knowledge Discovery
     and Data Mining, pages 197-205. ACM Press, New York, NY, USA,
     2003.

     Friedrich Leisch. Exploring the structure of mixture model
     components. In Jaromir Antoch, editor, Compstat 2004 - Proceedings
     in Computational Statistics, pages 1405-1412. Physika Verlag,
     Heidelberg, Germany, 2004. ISBN 3-7908-1554-3.

