boundary             package:exactmaxsel             R Documentation

_C_o_m_p_u_t_e_s _t_h_e _c_o_o_r_d_i_n_a_t_e_s _o_f _t_h_e _b_o_u_n_d_a_r_i_e_s

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

     The function 'boundary' is an internal function that computes the
     greatest (and also the smallest if 'lower=TRUE')  number of
     observations from class Y=1 in the left node that lead to an
     association criterion <= c.  Remark: in general, the numbers
     output by 'boundary' are not integers.

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

     boundary(x, n0, n1, c, statistic, lower=TRUE)

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

       x: the number of observations in the left node

      n0: the number of observations in class Y=0

      n1: the number of observations in class Y=1

       c: the value of the criterion that should not be exceeded.

statistic: the association measure. Currently, only 'statistic="chi2"'
          (chi-square statistic) and 'statistic="gini"' (the Gini-gain
          from machine learning) are implemented.

   lower: Should the lower boundary also be computed? 

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

     This function should not be called by the user in practice.

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

     a list with 

   upper: the upper boundary (greatest allowed value).

   lower: the lower boundary (smallest allowed value).

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

     Anne-Laure Boulesteix (<URL:
     http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/>).

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

     A.-L. Boulesteix (2006), Maximally selected chi-square statistics
     for ordinal variables, Biometrical Journal 48:451-462.

     A.-L. Boulesteix (2006), Maximally selected chi-square statistics
     and binary splits of nominal variables, Biometrical Journal
     48:838-848.

     C. Strobl, A.-L. Boulesteix and T. Augustin (2007), Unbiased split
     selection  for classification trees based on the Gini index,
     Computational Statistics and Data Analysis 52:483-501.

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

     'Ford', 'Fcat'.

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

     # load exactmaxsel library
     library(exactmaxsel)

     boundary(10,30,30,c=3,statistic="chi2",lower=TRUE)

