maketrylist               package:deal               R Documentation

_C_r_e_a_t_e_s _t_h_e _f_u_l_l _t_r_y_l_i_s_t

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

     For faster learning, a trylist is maintained as a lookup table for
     a given parent configuration of a node.

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

     maketrylist(initnw,data,prior=jointprior(network(data)),timetrace=FALSE) 

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

  initnw: an object of class 'network', from which the search is
          started. 

    data: a data frame used for learning the network, see 'network'.

   prior: a list containing parameter priors, generated by
          'jointprior'.

timetrace: a logical. If 'TRUE', prints some timing information on the
          screen.

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

     This procedure is included for illustrative purposes. For each
     node in the  network, all possible parent  configurations are
     created and learned. The result is called a trylist. To create the
     full trylist is very time-consuming, and a better choice is to
     maintain a trylist while searching and indeed this is
     automatically done. The trylist is given as output to all
     functions that call the learning procedure and can be given as an
     argument.

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

     A list with one element per node in the network. In the list,
     element _i_ is a matrix with two columns: a string with the
     indices of the parent nodes, separated by ":", and a numeric with
     the log-likelihood contribution of the node given the parent
     configuration. Whenever learning is performed of a node given a
     parent configuration, the trylist is consulted to yield faster
     learning, especially useful when using 'autosearch' or
     'heuristic'.

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

     Susanne Gammelgaard Bttcher alma@math.aau.dk, 
      Claus Dethlefsen cld@rn.dk.

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

     Further information about *Deal* can be found at:
      <URL: http://www.math.aau.dk/~dethlef/novo/deal>.

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

     'networkfamily', 'autosearch' 'heuristic'

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

     data(rats)
     rats.nw <- network(rats)
     rats.pr <- jointprior(rats.nw,12)
     rats.nw <- getnetwork(learn(rats.nw,rats,rats.pr))
     rats.tr <- maketrylist(rats.nw,rats,rats.pr)

     rats.hi <- getnetwork(heuristic(rats.nw,rats,rats.pr,trylist=rats.tr))

