som                 package:GeneSOM                 R Documentation

_F_u_n_c_t_i_o_n _t_o _t_r_a_i_n _a _S_e_l_f-_O_r_g_a_n_i_z_i_n_g _M_a_p

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

     Produces an object of class "som" which is a Self-Organizing Map
     fit of the data.

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

     som(data, xdim, ydim, init="linear", alpha=NULL, alphaType="inverse", neigh="gaussian", topol="rect", radius=NULL, rlen=NULL)

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

    data: a data frame or matrix of input data. 

    xdim: an integer specifying the x-dimension of the map. 

    ydim: an integer specifying the y-dimension of the map. 

    init: a character string specifying the initializing method. The
          following are permitted: `"sample"' uses a radom sample from
          the data; `"random"' uses random draws from N(0,1);
          `"linear"' uses the linear grids upon the first two principle
          components directin. 

   alpha: a vector of initial learning rate parameter for the two
          training phases. Decreases linearly to zero during training. 

alphaType: a character string specifying learning rate funciton type.
          Possible choices are linear function (`"linear"') and
          inverse-time type function (`"inverse"'). 

   neigh: a character string specifying the neighborhood function type.
          The following are permitted:

          `"bubble"' `"gaussian"' 

   topol: a character string specifying the topology type when
          measuring distance in the map. The following are permitted:

          `"hexa"' `"rect"' 

  radius: a vector of initial radius of the training area in
          som-algorithm for the two training phases. Decreases linearly
          to one during training. 

    rlen: a vector of running length (number of steps) in the two
          training phases. 

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

     An object of class `"som"' representing the fit.

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

     Jun Yan <jyan@stat.wisc.edu>

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

     `glm', `lm', `formula'.

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

     data(yeast)
     yeast <- yeast[, -c(1, 11)]
     yeast.f <- filtering(yeast)
     yeast.f.n <- normalize(yeast.f)
     foo <- som(yeast.f.n, xdim=5, ydim=6)
     foo <- som(yeast.f.n, xdim=5, ydim=6, topol="hexa", neigh="gaussian")

