WCCSOM                package:wccsom                R Documentation

_M_a_p_p_i_n_g _s_p_e_c_t_r_a _w_i_t_h _s_e_l_f-_o_r_g_a_n_i_s_i_n_g _m_a_p_s

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

     Self-organising maps for mapping high-dimensional spectra or
     patterns to 2D; instead of Euclidean distance, the weighted cross
     correlation (WCC) similarity measure is used. Modelled after the
     SOM function in package 'class'. WCCSOM takes 'continous'
     patterns, i.e. datapoints are equidistant.

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

     WCCSOM(data, grid=somgrid(), rlen = 100, alpha = c(0.05, 0.01),
            radius = quantile(nhbrdist, 0.7), init, nhbrdist, trwidth = 20,
            toroidal = FALSE, FineTune = TRUE, keep.data = TRUE)

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

    data: Spectra or patterns to be mapped: a matrix, with each row
          representing a compound.

    grid: A grid for the representatives: see 'somgrid'.

    rlen: the number of times the complete data set will be presented
          to the network.

   alpha: a vector of two numbers indicating the amount of change.
          Default is to decline linearly from 0.05 to 0.01 over 'rlen'
          updates.

  radius: the initial radius of the neighbourhood to be used for each
          update: the decrease is exponential over 'rlen' updates in
          such a way that after one-third of the updates only the
          winning unit is updated. The default is to start with a value
          that covers 2/3 of all units.

    init: the initial representatives, represented as a matrix.  If
          missing, chosen (without replacement) randomly from 'data'.

nhbrdist: optionally, the distance matrix for the units.

 trwidth: width of the triangle function used in the WCC measure, given
          in the number of data points.

toroidal: if TRUE, then the edges of the map are joined. Note that in a
          toroidal hexagonal map, the number of rows must be even.

FineTune: apply kmeans for fine-tuning the codebook vectors.

keep.data: store training data and their mapping in the network.

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

     an object of class '"WCCSOM"' with components 

    grid: the grid, an object of class '"somgrid"'.

 changes: vector of mean average deviations from code vectors

   codes: a matrix of code vectors.

  trwdth: the triangle width used for the WCC measure

   acors: autocorrelations of the code vectors.

toroidal: setting of parameter 'toroidal'.

FineTune: setting of parameter 'FineTune'.

unit.classif: mapping of training data: a vector of unit numbers. Only
          if 'keep.data' equals TRUE.

    wccs: WCC values of all training data, compared to the best
          matching codebook vector. Only if 'keep.data' equals TRUE.

data.acors: WAC values for training data. Only if 'keep.data' equals
          TRUE.

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

     Ron Wehrens

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

     R. Wehrens, W.J. Melssen, L.M.C. Buydens and R. de Gelder.
     Representing Structural Databases in a Self-Organising Map. Acta
     Cryst. B61, 548-557, 2005.

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

     'SOM', 'plot.WCCSOM', 'wcc'

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

     data(cepha)
     gr <- somgrid(3,3, "hexagonal")
     set.seed(7)
     x <- WCCSOM(cepha$patterns, grid=gr, trwidth=20, rlen=100)

