viewClusters        package:clusterGeneration        R Documentation

_P_L_O_T _A_L_L _C_L_U_S_T_E_R_S _I_N _A _2-_D _P_R_O_J_E_C_T_I_O_N _S_P_A_C_E

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

     Plot all clusters in a 2-D projection space.

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

     viewClusters(y, cl, outlierLabel=0,
       projMethod="Eigen", xlim=NULL, ylim=NULL,
       xlab="1st projection direction", 
       ylab="2nd projection direction", 
       title="Scatter plot of 2-D Projected Clusters",
       font=2, font.lab=2, cex=1.2, cex.lab=1.2) 

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

       y: Data matrix. Rows correspond to observations. Columns
          correspond to variables. 

      cl: Cluster membership vector. 

outlierLabel: Label for outliers. Outliers are not involved in
          calculating the projection directions. Outliers will be
          represented by red triangles in the plot. By default,
          'outlierLabel=0'. 

projMethod: Method to construct 2-D projection directions. 
          'projMethod="Eigen"' indicates that we project data to the 
          2-dimensional space spanned by the first two eigenvectors of
          the  between cluster distance matrix  B={2over
          k_0}sum_{i=1}^{k_0}Sigma_i+{2over
          k_0(k_0-1)}sum_{i<j}(theta_i-theta_j) (theta_i-theta_j)^T.
          'projMethod="DMS"' indicates that we project data to the 
          2-dimensional space spanned by the first two eigenvectors of
          the  between cluster distance matrix 
          B=sum_{i=2}^{k_0}sum_{j=1}^{i-1} n_i
          n_j(theta_i-theta_j)(theta_i-theta_j)^T.  "DMS" method is
          proposed by Dhillon et al. (2002). 

    xlim: Range of X axis. 

    ylim: Range of Y axis. 

    xlab: X axis label. 

    ylab: Y axis label. 

   title: Title of the plot. 

    font: An integer which specifies which font to use for text (see
          'par'). 

font.lab: The font to be used for x and y labels (see 'par'). 

     cex: A numerical value giving the amount by which plotting text
          and symbols should be scaled relative to the default (see
          'par'). 

 cex.lab: The magnification to be used for x and y labels relative to
          the current setting of 'cex' (see 'par'). 

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

       B: Between cluster distance matrix measuring the between cluster
          variation. 

       Q: Columns of 'Q' are eigenvectors of the matrix 'B'. 

    proj: Projected clusters in the 2-D space spanned by the first 2
          columns of the matrix 'Q'. 

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

     Weiliang Qiu stwxq@channing.harvard.edu
      Harry Joe harry@stat.ubc.ca

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

     Dhillon I. S., Modha, D. S. and Spangler, W. S. (2002) Class
     visualization of high-dimensional data with applications.
     _computational Statistics and Data Analysis_, *41*, 59-90.

     Qiu, W.-L. and Joe, H. (2006) Separation Index and Partial
     Membership for Clustering. _Computational Statistics and Data
     Analysis_, *50*, 585-603.

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

     'plot1DProjection' 'plot2DProjection'

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

     n1<-50
     mu1<-c(0,0)
     Sigma1<-matrix(c(2,1,1,5),2,2)
     n2<-100
     mu2<-c(10,0)
     Sigma2<-matrix(c(5,-1,-1,2),2,2)
     n3<-30
     mu3<-c(10,10)
     Sigma3<-matrix(c(3,1.5,1.5,1),2,2)
     n4<-10
     mu4<-c(0,0)
     Sigma4<-50*diag(2)

     library(MASS)
     set.seed(1234)
     y1<-mvrnorm(n1, mu1, Sigma1)
     y2<-mvrnorm(n2, mu2, Sigma2)
     y3<-mvrnorm(n3, mu3, Sigma3)
     y4<-mvrnorm(n4, mu4, Sigma4)
     y<-rbind(y1, y2, y3, y4)
     cl<-rep(c(1:3, 0), c(n1, n2, n3, n4))

     par(mfrow=c(2,1))
     viewClusters(y, cl)
     viewClusters(y, cl,projMethod="DMS")

