getTree             package:randomForest             R Documentation

_E_x_t_r_a_c_t _a _s_i_n_g_l_e _t_r_e_e _f_r_o_m _a _f_o_r_e_s_t.

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

     This function extract the structure of a tree from a
     'randomForest' object.

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

     getTree(rfobj, k=1)

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

   rfobj: a 'randomForest' object.

       k: which tree to extract?

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

     For categorical predictors, the splitting point is represented by
     an integer, whose binary expansion gives the identities of the
     categories that goes to left or right.  For example, if a
     predictor has three categories, and the split point is 5.  The
     binary expansion of 5 is (1, 0, 1) (because 5 = 1*2^0 + 0*2^1 +
     1*2^2), so cases with categories 1 or 3 in this predictor get sent
     to the left, and the rest to the right.

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

     A matrix with six columns and number of rows equal to total number
     of nodes in the tree.  The six columns are: 

left daughter: the row where the left daughter node is; 0 if the node
          is terminal

right daughter: the row where the right daughter node is; 0 if the node
          is terminal

split var: which variable was used to split the node; 0 if the node is
          terminal

split point: where the best split is; see Details for categorical
          predictor

  status: is the node terminal (-1) or not (1)

prediction: the prediction for the node; 0 if the node is not terminal

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

     Andy Liaw andy_liaw@merck.com

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

     'randomForest'

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

     data(iris)
     ## Look at the third trees in the forest.
     getTree(randomForest(iris[,-5], iris[,5], ntree=10), 3)

