Control Forest Hyper Parameters    package:party    R Documentation

_C_o_n_t_r_o_l _f_o_r _C_o_n_d_i_t_i_o_n_a_l _T_r_e_e _F_o_r_e_s_t_s

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

     Various parameters that control aspects of the `cforest' fit.

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

     cforest_control(teststat = "max",
                     testtype = "Teststatistic",
                     mincriterion = qnorm(0.9),
                     savesplitstats = FALSE,
                     ntree = 500, mtry = 5, replace = TRUE,
                     fraction = 0.632, ...)

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

teststat: a character specifying the type of the test statistic to be
          applied. 

testtype: a character specifying how to compute the distribution of the
          test statistic. 

mincriterion: the value of the test statistic or 1 - p-value that must
          be exceeded in order to implement a split. 

    mtry: number of input variables randomly sampled as candidates  at
          each node for random forest like algorithms. The default
          'mtry = 0' means that no random selection takes place.

savesplitstats: a logical determining if the process of standardized
          two-sample statistics for split point estimate is saved for
          each primary split.

   ntree: number of trees to grow in a forest.

 replace: a logical indicating whether sampling of observations is 
          done with or without replacement.

fraction: fraction of number of observations to draw without 
          replacement ('replace = TRUE').

     ...: additional arguments to be passed to  'ctree_control'.

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

     The arguments 'teststat', 'testtype' and 'mincriterion' determine
     how the global null hypothesis of independence between all input
     variables and the response is tested (see 'ctree'). The  argument
     'nresample' is the number of Monte-Carlo replications to be used
     when 'testtype = "MonteCarlo"'.

     A split is established when the sum of the weights in both
     daugther nodes is larger than 'minsplit', this avoids pathological
     splits at the borders. When 'stump = TRUE', a tree with at most
     two terminal nodes is computed.

     The argument 'mtry > 0' means that a random forest like `variable
     selection', i.e., a random selection of 'mtry' input variables, is
     performed in each node.

     It might be informative to look at scatterplots of input variables
     against the standardized two-sample split statistics, those are
     available when 'savesplitstats = TRUE'. Each node is then
     associated with a vector those length is determined by the number
     of observations in the learning sample and thus much more memory
     is required.

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

     An object of class 'ForestControl-class'.

