rpart.control             package:rpart             R Documentation

_C_o_n_t_r_o_l _f_o_r _R_p_a_r_t _M_o_d_e_l_s

_D_e_s_c_r_i_p_t_i_o_n:

     Various parameters that control aspects of the 'rpart' fit.

_U_s_a_g_e:

     rpart.control(minsplit=20, minbucket=round(minsplit/3), cp=0.01, 
                   maxcompete=4, maxsurrogate=5, usesurrogate=2, xval=10,
                   surrogatestyle=0, maxdepth=30, ...)

_A_r_g_u_m_e_n_t_s:

minsplit: the minimum number of observations that must exist in a node,
          in order for  a split to be attempted.  

minbucket: the minimum number of observations in any terminal '<leaf>'
          node. If only one of 'minbucket' or 'minsplit' is specified,
          the code either sets 'minsplit' to 'minbucket*3' or
          'minbucket' to 'minsplit/3', as appropriate. 

      cp: complexity parameter.  Any split that does not decrease the
          overall lack of fit by a factor of 'cp' is not attempted. 
          For instance, with 'anova' splitting, this means that the
          overall Rsquare must increase by 'cp' at each step. The main
          role of this parameter is to save computing time by pruning
          off splits that are obviously not worthwhile. Essentially,the
          user informs the program that any split which does not
          improve the fit by 'cp' will likely be pruned off by
          cross-validation, and that hence the program need not pursue
          it. 

maxcompete: the number of competitor splits retained in the output.  It
          is useful to know not just which split was chosen, but which
          variable came in second, third, etc. 

maxsurrogate: the number of surrogate splits retained in the output. 
          If this is set to zero the compute time will be shortened,
          since approximately half of the computational time (other
          than setup) is used in the search for surrogate splits. 

usesurrogate: how to use surrogates in the splitting process.  0=
          display only; an observation with a missing value for the
          primary split rule is not sent further down the tree.  1= use
          surrogates, in order, to split subjects missing the primary
          variable; if all surrogates are missing the observation is
          not split.  2= if all surrogates are missing, then send the
          observation in the majority direction.  A value of 0
          corresponds to the action of 'tree', and 2 to the
          recommendations of Breiman, et.al. 

    xval: number of cross-validations  

surrogatestyle: controls the selection of a best surrogate. If set to 0
          (default) the program uses the total number of correct
          classification for a potential surrogate variable, if set to
          1 it uses the percent correct, calculated over the
          non-missing values of the surrogate. The first option more
          severely penalizes covariates with a large number of missing
          values. 

maxdepth: Set the maximum depth of any node of the final tree, with the
          root node counted as depth 0 (past 30 'rpart' will give
          nonsense results on 32-bit machines). 

     ...: mop up other arguments. 

_V_a_l_u_e:

     a list containing the options.

_S_e_e _A_l_s_o:

     'rpart'