suppl                 package:made4                 R Documentation

_P_r_o_j_e_c_t_i_o_n _o_f _s_u_p_p_l_e_m_e_n_t_a_r_y _d_a_t_a _o_n_t_o _a_x_e_s _f_r_o_m _a _b_e_t_w_e_e_n _g_r_o_u_p _a_n_a_l_y_s_i_s

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

     Projection and class prediction of supplementary points onto  axes
     from a between group analysis, 'bga'.

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

     suppl(dudi.bga, supdata, supvec = NULL, assign=TRUE, ...)

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

dudi.bga: An object returned by 'bga' 

 supdata: Test or blind dataset. Accepted formats are a 'matrix', 
          'data.frame', 'exprSet' or  'marrayRaw' 

  supvec: A factor or vector which describes the classes in the
          training dataset

  assign: A logical indicating whether class assignment should be
          calculated using the method described by Culhane et al.,
          2002. The default value is TRUE

     ...: further arguments passed to or from other methods 

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

     After performing a between group analysis on a training dataset
     using 'bga',  a test dataset can be then projected onto 'bga' axes
     using 'suppl'.  

     'suppl' returns the projected coordinates and assignment of each
     test case (array).

     The test dataset must contain the same number of variables (genes)
     as the training dataset.  The input format of both the training
     dataset and test dataset are verified using 'array2ade4'. Use
     'plot.bga' to plot results from bga.

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

     A list containing: 

   suppl: An object returned by 'suppl'

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

     Aedin Culhane

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

     Culhane AC, et al., 2002 Between-group analysis of microarray
     data. Bioinformatics. 18(12):1600-8.

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

     See Also  'bga', 'between', 'plot.bga',  'bga.jackknife'

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

     data(khan)
     #khan.bga<-bga(khan$train, khan$train.classes)
     if (require(ade4, quiet = TRUE)) {
     khan.bga<-bga.suppl(khan$train, supdata=khan$test, classvec=khan$train.classes, 
               supvec=khan$test.classes)

     khan.bga
     plot.bga(khan.bga, genelabels=khan$annotation$Symbol)
     khan.bga$suppl
     }

