choice               package:relations               R Documentation

_R_e_l_a_t_i_o_n-_B_a_s_e_d _C_h_o_i_c_e_s

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

     Choose objects based on an ensemble of relations between these.

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

     relation_choice(x, method = "symdiff", weights = 1, control = list(), ...)

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

       x: an ensemble of endorelations.

  method: a character string specifying one of the built-in methods, or
          a function to be taken as a user-defined method. See
          *Details* for available built-in methods.

 weights: a numeric vector with non-negative case weights. Recycled to
          the number of elements in the ensemble given by 'x' if
          necessary.

 control: a list of control parameters.  See *Details*.

     ...: a list of control parameters (overruling those specified in
          'control').

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

     A social choice function is a rule for _choosing_ from a set D of
     objects, i.e., selecting suitable subsets of D. Voting rules used
     in elections are the most prominent example of such functions,
     which typically aggregate individual preferences (e.g., of
     voters).

     Choice methods '"symdiff"' and '"CKS"' (currently the only ones
     available) choose a given number k of objects (winners) by
     determining a relation R minimizing sum_b w_b d(R_b, R) over all
     relations for which winners are always strictly preferred to
     losers, without any further constraints on the relations between
     pairs of winners or pairs of losers, where d is symmetric
     difference (symdiff, Kemeny-Snell) or Cook-Kress-Seiford (CKS)
     dissimilarity, respectively, the R_b are crisp endorelations, and
     w_b is the case weight given to R_b.  (Note that this is different
     from computing consensus preference relations.)

     Available control options include:

     '_k' an integer giving the number of objects/winners to be chosen.

     '_a_l_l' a logical indicating whether all optimal choices should be
          obtained.  By default, or if 'all' is false, only a single
          optimal choice is computed.

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

     A set with the chosen objects, or a list of such sets.

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

     data("SVM_Benchmarking_Classification")
     ## Determine the three best classification learners in the above sense.
     relation_choice(SVM_Benchmarking_Classification, k = 3)

