PScv                 package:GolubRR                 R Documentation

_C_o_m_p_u_t_e _t_h_e _W_e_i_g_h_t_e_d _V_o_t_i_n_g _S_t_a_t_i_s_t_i_c_s _U_s_i_n_g _C_r_o_s_s _V_a_l_i_d_a_t_i_o_n.

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

     The weighted voting statistics described in Golub et al are
     computed using leave one out cross-validation.

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

     PScv(eset, cov)

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

    eset: An object of class 'exprSet' that contains the expression
          data to be analysed. 

     cov: A vector indicating which of two classes each sample belongs
          to. 

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

     Each sample is left out in turn and an object of class 'vstruct'
     is computed using the remaining samples. This object is then used,
     together with the left out sample to obtain the votes and PS for
     the left out sample. The function returns a list with one entry
     for each sample.

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

     A list with one entry for each sample. The entries are the output
     of 'dovote' and are described there.

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

     R. Gentleman

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

     Molecular Classification of Cancer: Class Discovery and Class
     Prediction by  Gene Expression Monitoring, Science, 531-537, 1999,
     T. R. Golub and D. K. Slonim and P. Tamayo and C. Huard and M.
     Gaasenbeek and J. P. Mesirov and H. Coller and M.L. Loh and J. R.
     Downing and M. A. Caligiuri and C. D. Bloomfield and E. S. Lander

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

     'dovote'

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

        library(golubEsets)
        ans <- PScv(golubTrain[1:20,], golubTrain$ALL)

