discrcoord                package:fpc                R Documentation

_D_i_s_c_r_i_m_i_n_a_n_t _c_o_o_r_d_i_n_a_t_e_s/_c_a_n_o_n_i_c_a_l _v_a_r_i_a_t_e_s

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

     Computes discriminant coordinates, sometimes referred to as
     "canonical variates" as described in Seber (1984).

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

     discrcoord(xd, clvecd, pool = "n", ...)

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

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

  clvecd: integer vector of class numbers; length must equal
          'nrow(xd)'.

    pool: string. Determines how the within classes covariance is
          pooled. "n" means that the class covariances are weighted
          corresponding to the number of points in each class
          (default). "equal" means that all classes get equal weight.

     ...: no effect

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

     The matrix T (see Seber (1984), p. 270) is inverted by use of
     'tdecomp', which can be expected to give reasonable results for
     singular within-class covariance matrices.

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

     List with the following components 

      ev: eigenvalues in descending order.

   units: columns are coordinates of projection basis vectors. New
          points 'x' can be projected onto the projection basis vectors
          by 'x %*% units'

    proj: projections of 'xd' onto 'units'.

_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:

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

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

     'plotcluster' for straight forward discriminant plots.

     'batcoord' for discriminating projections for two classes, so that
     also the differences in variance are shown ('discrcoord' is based
     only on differences in mean).

     '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"))
       dcf <- discrcoord(face,grface)
       plot(dcf$proj,col=grface)
       # ...done in one step by function plotcluster.

