clustPlot             package:clustTool             R Documentation

_P_l_o_t_t_i_n_g _f_u_n_c_t_i_o_n _f_o_r _p_a_c_k_a_g_e _c_l_u_s_t_T_o_o_l

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

     The first plot visualises the clusters in a map. The second plot
     provides the visualisation of the cluster centres centres.

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

     clustPlot(coord, clust, k, val = "silwidths", which.plot = c(1, 2), Map = "kola.background", texth = 0.75)

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

   coord: (x,y)-coordinates of the data 

   clust: Object from function "clust" 

       k: Number of clusters 

     val: local validity measure 

which.plot: if a subset of the plots is required, specify a subset of
          the numbers '1:2' 

     Map: A map may be selected

   texth: cex of variable names in the cluster centres plot.

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

     The resulting clusters of function "clust" will be visualised in
     maps. 

     In general, not only the location of the single clustes in the map
     is of interest but also the composition of the single cluster. For
     this purpose a plot of the cluster centres is supported which is
     helpful for the interpretation of the clusters. The cluster centre
     is the element-wise mean of all observations of a cluster.
     Therefore, for each cluster all elements used for clustering are
     presented. The resulting means for all clusters are horizontally
     arranged. If the variables used for clustering were standardised
     they have the same contribution for the cluster analysis. If
     single elements show very high or low means for a cluster they are
     highly influential for this cluster.

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

     Matthias Templ

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

     'clust', 'GUIspatClust'

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

     library(mvoutlier)
     data(kola.background)
     data(humus)
     x <- prepare(humus[,c("As", "Ca", "Co", "Mo", "Ni")])
     cl1 <- clust(x, k=9, method="clara", distMethod="manhattan")
     names(cl1)
     clustPlot(coord=humus[,2:3], clust=cl1, k=cl1$k, val="median.distance", Map="kola.background")

