discrproj                package:fpc                R Documentation

_L_i_n_e_a_r _d_i_m_e_n_s_i_o_n _r_e_d_u_c_t_i_o_n _f_o_r _c_l_a_s_s_i_f_i_c_a_t_i_o_n

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

     An interface for ten methods of linear dimension reduction in
     order to separate the groups optimally in the projected data.
     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:

      discrproj(x, clvecd, method="awc", clnum=1, ...)

_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.

     ...: additional parameters passed to the projection methods.

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

     'discrproj' returns the output of the chosen projection method,
     which is a list with at least the components 'ev, units, proj'.
     For detailed informations see the help pages of the projection
     methods. 

      ev: eigenvalues in descending order, usually indicating portion
          of information in the corresponding direction.

   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:

     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'.

     '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"))
     discrproj(face,grface, method="nc")$units
     discrproj(face,grface, method="wnc")$units
     discrproj(face,grface==1, method="arc")$units

