order.single              package:gclus              R Documentation

_O_r_d_e_r_s _o_b_j_e_c_t_s _u_s_i_n_g _h_i_e_r_a_r_c_h_i_c_a_l _c_l_u_s_t_e_r_i_n_g

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

     Reorders objects so that  similar (or high-merit) object pairs are
     adjacent. A permutation vector is returned.

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

     order.single(merit,clusters=NULL)
     order.endlink(merit,clusters=NULL)
     order.hclust(merit, reorder=TRUE,...)

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

   merit: is either a symmetric matrix of merit or similarity score, 
          or a 'dist'.

clusters: if non-null, specifies a partial ordering. It should be a
          list whose  ith element contains  the indices the  objects in
          the ith ordered cluster.

 reorder: if TRUE, reorders the default ordering from 'hclust'.

     ...: arguments are passed to 'hclust'.

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

     'order.single' performs a variation on single-link cluster
     analysis, devised by Gruvaeus and Wainer (1972). When two ordered
     clusters are merged, the new cluster is formed by placing the most
     similar endpoints of the joining clusters adjacent to each other.
     When applied to variables, the resulting order is useful for
     scatterplot  matrices.

     'order.endlink' is another variation on single-link cluster
     analysis, where the similarity between two ordered clusters is
     defined as the minimum distance between their endpoints. When two
     ordered clusters are merged, the new cluster is formed by placing
     the most similar endpoints of the joining clusters adjacent to
     each other. When applied to variables, the resulting order is
     useful for parallel  coordinate displays.

     'order.hclust' returns the order of objects from 'hclust' if
     'reorder' is 'FALSE'. Otherwise, it reorders the objects using
     'hclust.reorder' so that when two ordered clusters are merged, the
     new cluster is formed by placing the most similar endpoints of the
     joining clusters adjacent to each other.
     'order.hclust(m,method="single")' is equivalent to  'order.single'
     when 'clusters' is 'NULL'. The default method of 'hclust' is
     "complete", see 'hclust' for other  possibilities.

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

     A permutation of the objects represented by 'merit' is returned.

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

     Catherine B. Hurley

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

     Hurley, Catherine B.  "Clustering Visualisations of
     Multidimensional  Data", Journal of Computational and Graphical
     Statistics, vol. 13, (4), pp 788-806, 2004. 

     Gruvaeus, G. and Wainer, H. (1972), "Two Additions to Hierarchical
     Cluster Analysis", British Journal of Mathematical and Statistical
     Psychology, 25, 200-206.

_S_e_e _A_l_s_o:

     'cpairs',  'cparcoord','plotcolors', 
     'reorder.hclust','order.clusters', 'hclust'.

_E_x_a_m_p_l_e_s:

     data(state)
     state.cor <- cor(state.x77)
     order.single(state.cor)
     order.endlink(state.cor)
     order.hclust(state.cor,method="average")

     # Use for plotting...

     cpairs(state.x77, panel.colors=dmat.color(state.cor), order.single(state.cor),pch=".",gap=.4)
     cparcoord(state.x77, order.endlink(state.cor),panel.colors=dmat.color(state.cor))

     # Order the states instead of the variables...

     state.d <- dist(state.x77)
     state.o <- order.single(-state.d)

     op <- par(mar=c(1,6,1,1))
     cmat <- dmat.color(as.matrix(state.d), rev(cm.colors(5)))
     plotcolors(cmat[state.o,state.o], rlabels=state.name[state.o])
     par(op)

