plotcluster               package:fpc               R Documentation

_D_i_s_c_r_i_m_i_n_a_n_t _p_r_o_j_e_c_t_i_o_n _p_l_o_t.

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

     Plots to distinguish given classes by ten available projection
     methods. Includes classical discriminant coordinates, methods to
     project differences in mean and covariance structure, asymmetric
     methods (separation of a homogeneous class from a heterogeneous
     one), local neighborhood-based methods and methods based on robust
     covariance matrices.

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

     plotcluster(x, clvecd, clnum=1,
                 method=ifelse(identical(range(as.integer(clvecd)),
                               as.integer(c(0,1))),"awc","dc"),
                 bw=FALSE, xlab=NULL, ylab=NULL,
                 pch=NULL, col=NULL, ...)

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

       x: the data matrix; a numerical object which can be coerced to a
          matrix.

  clvecd: vector of class numbers which can be coerced into integers;
          length must equal 'nrow(xd)'.

  method: one of

          "_d_c" usual discriminant coordinates, see 'discrcoord',

          "_b_c" Bhattacharyya coordinates, first coordinate showing mean
               differences, second showing covariance matrix
               differences, see 'batcoord',

          "_v_b_c" variance dominated Bhattacharyya coordinates, see
               'batcoord',

          "_m_v_d_c" added meana and variance differences optimizing
               coordinates, see 'mvdcoord',

          "_a_d_c" asymmetric discriminant coordinates, see 'adcoord',

          "_a_w_c" asymmetric discriminant coordinates with weighted
               observations, see 'awcoord',

          "_a_r_c" asymmetric discriminant coordinates with weighted
               observations and robust MCD-covariance matrix, see
               'awcoord',

          "_n_c" neighborhood based coordinates, see 'ncoord',

          "_w_n_c" neighborhood based coordinates with weighted
               neighborhoods, see 'ncoord',

          "_a_n_c" asymmetric neighborhood based coordinates, see
               'ancoord'.

          Note that "bc", "vbc", "adc", "awc", "arc" and "anc" assume
          that there are only two classes.

   clnum: integer. Number of the class which is attempted to plot
          homogeneously by "asymmetric methods", which are the methods
          assuming that there are only two classes, as indicated above.

      bw: logical. If 'TRUE', the classes are distinguished by symbols,
          and the default color is black/white. If 'FALSE', the classes
          are distinguished by colors, and the default symbol is
          'pch=1'.

    xlab: label for x-axis. If 'NULL', a default text is used.

    ylab: label for y-axis. If 'NULL', a default text is used.

     pch: plotting symbol, see 'par'. If 'NULL', the default is used.

     col: plotting color, see 'par'. If 'NULL', the default is used.

     ...: additional parameters passed to 'plot' or the projection
          methods.

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

     Christian Hennig chrish@stats.ucl.ac.uk <URL:
     http://www.homepages.ucl.ac.uk/~ucakche/>

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

     Hennig, C. (2003) Symmetric, asymmetric, and robust linear
     dimension reduction for classification, submitted, <URL:
     http://stat.ethz.ch/Research-Reports/108.html>.

     Seber, G. A. F. (1984). _Multivariate Observations_. New York:
     Wiley.

     Fukunaga (1990). _Introduction to Statistical Pattern Recognition_
     (2nd ed.). Boston: Academic Press.

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

     'discrcoord', 'batcoord', 'mvdcoord', 'adcoord', 'awcoord',
     'ncoord', 'ancoord'.

     'discrproj' is an interface to all these projection methods.

     'rFace' for generation of the example data used below.

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

     set.seed(4634)
     face <- rFace(600,dMoNo=2,dNoEy=0)
     grface <- as.integer(attr(face,"grouping"))
     plotcluster(face,grface)
     plotcluster(face,grface==1)

