mahalconf                package:fpc                R Documentation

_M_a_h_a_l_a_n_o_b_i_s _f_i_x_e_d _p_o_i_n_t _c_l_u_s_t_e_r_s _i_n_i_t_i_a_l _c_o_n_f_i_g_u_r_a_t_i_o_n

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

     Generates an initial configuration of 'startn' points from dataset
     'x' for the 'fixmahal' fixed point iteration.

     Thought only for use within 'fixmahal'.

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

     mahalconf(x, no, startn, covall, plot)

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

       x: numerical matrix. Rows are points, columns are variables.

      no: integer between 1 and 'nrow(x)'. Number of the first point of
          the configuration.

  startn: integer between 1 and 'nrow(x)'.

  covall: covariance matrix for the computation of the first
          Mahalanobis distances.

    plot: a string. If equal to '"start"' or '"both"',the first two
          variables and the first 'ncol(x)+1' points are plotted.

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

     'mahalconf' first chooses the p (number of variables) nearest
     points to point no. 'no' in terms of the Mahalanobis distance
     w.r.t. 'covall', so that there are p+1 points. In every further
     step, the covariance matrix of the current configuration is
     computed and the nearest point in terms of the new Mahalanobis
     distance is added. 'solvecov' is used to invert singular
     covariance matrices.

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

     A logical vector of length 'nrow(x)'.

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

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

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

     'fixmahal', 'solvecov'

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

       set.seed(4634)
       face <- rFace(600,dMoNo=2,dNoEy=0,p=2)
       mahalconf(face,no=200,startn=20,covall=cov(face),plot="start")

