partition                package:delt                R Documentation

_F_i_n_d_s _t_h_e _p_a_r_t_i_t_i_o_n _g_e_n_e_r_a_t_e_d _b_y _a_n _e_v_a_l_u_a_t_i_o_n _t_r_e_e

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

     Finds the partition generated by an evaluation tree. An evaluation
     tree makes a recursive partition of a rectangle. Functions
     "eval.cart", "densplit",... return evaluation trees.

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

     partition(et, grid=TRUE, zerorecs=FALSE)

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

      et: an evaluation tree; output of "eval.cart", "densplit", ...

    grid: TRUE or FALSE; whether the true coordinates or relative
          coordinates are used

zerorecs: TRUE or FALSE; whether the rectangles where the density
          vanishes are included; (evaluation trees are used by package
          "delt" to represent  density functions)

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

     Returns a list with the following elements. 

 values : vector whose lenght is equal to the number of rectangles in
          the partition; value of the function on the corresponding
          rectangle

    recs: recnum*(2*d) matrix; recnum is the number of rectangles in
          the partition and d is the dimension of the observations. The
          rows of "recs" describe the rectangles. Column (2*j-1) gives
          the lower value for the j:th interval and (2*j):th column
          gives upper value for the j:th interval, j=1,...,d.

 support: the rectangle which is partitioned

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

     Jussi Klemel

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

     'plotparti',

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

     dendat<-sim.data(n=200,seed=5,type="mulmodII")
     et<-densplit(dendat)

     pa<-partition(et)

     plotparti(pa)

