score                package:supclust                R Documentation

_W_i_l_c_o_x_o_n _S_c_o_r_e _f_o_r _B_i_n_a_r_y _P_r_o_b_l_e_m_s

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

     For a set of n observations grouped into two classes (for example
     n expression values of a gene), the 'score' function measures the
     separation of the classes. It can be interpreted as counting for
     each observation having response zero, the number of individuals
     of response class one that are smaller, and summing up these
     quantities.

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

     score(x, resp)

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

       x: Numeric vector of length n, for example containing gene or
          cluster expression values of n different cases.

    resp: Numeric vector of length n containing the ``binary'' class
          labels of the cases.  Must be coded by '0' and '1'.

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

     A numeric value, the 'score'. The minimal 'score' is zero, the
     maximal 'score' is the product of the number of samples in class 0
     and class 1. Values near the minimal or maximal 'score' indicate
     good separation, whereas intermediate 'score' means poor
     separation.

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

     Marcel Dettling, dettling@stat.math.ethz.ch

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

     Marcel Dettling (2002) _Supervised Clustering of Genes_, see <URL:
     http://stat.ethz.ch/~dettling/supercluster.html>

     Marcel Dettling and Peter Bhlmann (2002). Supervised Clustering
     of Genes. _Genome Biology_, *3*(12): research0069.1-0069.15.

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

     'wilma', 'margin' is the second statistic that is used there.

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

     data(leukemia, package="supclust")
     op <- par(mfrow=c(1,3))
     plot(leukemia.x[,69],leukemia.y)
     title(paste("Score = ", score(leukemia.x[,69], leukemia.y)))

     ## Sign-flipping is very important
     plot(leukemia.x[,161],leukemia.y)
     title(paste("Score = ", score(leukemia.x[,161], leukemia.y),2))
     x <- sign.flip(leukemia.x, leukemia.y)$flipped.matrix
     plot(x[,161],leukemia.y)
     title(paste("Score = ", score(x[,161], leukemia.y),2))
     par(op)

