SOMB              package:MLInterfaces              R Documentation

_A_n _i_n_t_e_r_f_a_c_e _t_o _s_e_l_f-_o_r_g_a_n_i_z_i_n_g _m_a_p _m_e_t_h_o_d_s _f_o_r _E_x_p_r_e_s_s_i_o_n_S_e_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     Self-organizing maps are covered in various machine learning
     reviews.  These can be viewed as clustering procedures analogous
     to 'kmeans' but with a constraint on the complexity of the
     subspace in which the cluster-defining prototypes are identified.

     The outputs of 'som' and 'SOM' are not easily formulated as
     'clustOutput-class' instances, so classes that extend 'list' are
     used as output containers.

     SOMB(exprObj, classifLab) somB(exprObj, classifLab)

_A_r_g_u_m_e_n_t_s:

 exprObj: An instance of the 'ExpressionSet' class. 

classifLab: A vector of class labels. 

_D_e_t_a_i_l_s:

     You may pass additional parameters, see 'SOM' or 'som' for
     details.

_V_a_l_u_e:

     An object of class 'SOMBoutput' or 'somBoutput'

_A_u_t_h_o_r(_s):

     Jess Mar, VJ Carey <stvjc@channing.harvard.edu>

_E_x_a_m_p_l_e_s:

     # access and trim an ExpressionSet
     library(golubEsets)
     data(Golub_Merge)
     smallG <- Golub_Merge[1:60,]
     # set a PRNG seed for reproducibilitiy
     set.seed(1234) # needed for nnet initialization
     # now run the classifiers
     #somB( smallG, "ALL.AML", 3)
     SOMB( smallG, "ALL.AML", 3)

