bestMclust               package:edci               R Documentation

_C_h_o_o_s_e '_b_e_s_t' _c_l_u_s_t_e_r_s

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

     Chooses the 'best' regression cluster(s), if the number of true
     clusters is known.

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

       bestMclust(clust, nc=1, crit="value")
       projMclust(clust, x, y)
       envMclust(clust, x, y, dist=0)

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

   clust: Cluster object returned by 'oregMclust' or 'circMclust'.

      nc: Number of 'best' clusters.

    crit: Name of the column to determine the best clusters.

     x,y: Original observations.

    dist: Maximal distance of observation from cluster center.

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

     'oregMclust' and 'circMclust' return a matrix containing not only
     the parameters of the found clusters but the value of the heights
     of the corresponding local maxima as well as how often each
     cluster is found. Both are reasonable criteria for choosing 'best'
     clusters, which can be done by 'bestMclust'. Additional criteria
     could be the number of observations projected to each cluster or
     the number of observations lying in a certain neighbourhood of the
     cluster center point.

     'projMclust' adds a column 'proj' to 'clust' which contains the
     number of points belonging to each cluster in the sense that each
     observation belongs to the cluster with shortest orthogonal
     distance. If 'clust' is comming from 'circMclust' a second column
     'projrel' is added which contains this number relativ to the
     radius of the particular circle.

     'envMclust' adds a column 'env' to 'clust' which contains the
     number of observations lying in a 'dist'-neighbourhood of each
     cluster center. If 'clust' is comming from 'circMclust' a second
     column 'envrel' is added which contains this number relativ to the
     radius of the particular circle.

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

     Both functions return a matrix of clusters.

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

     Tim Garlipp, garlipp@mathematik.uni-oldenburg.de

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

     Mller, C.H., Garlipp, T. (2003) Simple consistent cluster methods
     based on redescending M-estimators with an application to edge
     identification in images, to appear in _JMVA_.

