mvdcoord                 package:fpc                 R Documentation

_M_e_a_n/_v_a_r_i_a_n_c_e _d_i_f_f_e_r_e_n_c_e_s _d_i_s_c_r_i_m_i_n_a_n_t _c_o_o_r_d_i_n_a_t_e_s

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

     Discriminant projections as defined in Young, Marco and Odell
     (1987). The principle is to maximize the projection of a matrix
     consisting of the differences between the means of all classes and
     the first mean and the differences between the covariance matrices
     of all classes and the forst covariance matrix.

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

     mvdcoord(xd, clvecd, clnum=1, sphere="mcd", ...)

_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)'.

   clnum: integer. Number of the class to which all differences are
          computed.

  sphere: a covariance matrix or one of "mve", "mcd", "classical",
          "none". The matrix used for sphering the data. "mcd" and
          "mve" are robust covariance matrices as implemented in
          'cov.rob'. "classical" refers to the classical covariance
          matrix. "none" means no sphering and use of the raw data.

     ...: no effect

_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:

     Young, D. M., Marco, V. R. and Odell, P. L. (1987). Quadratic
     discrimination: some results on optimal low-dimensional
     representation, _Journal of Statistical Planning and Inference_,
     17, 307-319.

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

     'plotcluster' for straight forward discriminant plots. 'discrproj'
     for alternatives. 'rFace' for generation of the example data used
     below.

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

       set.seed(4634)
       face <- rFace(300,dMoNo=2,dNoEy=0,p=3)
       grface <- as.integer(attr(face,"grouping"))
       mcf <- mvdcoord(face,grface)
       plot(mcf$proj,col=grface)
       # ...done in one step by function plotcluster.

