densplit                package:delt                R Documentation

_C_a_l_c_u_l_a_t_i_o_n _o_f _a_n _o_v_e_r_f_i_t_t_i_n_g _h_i_s_t_o_g_r_a_m

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

     The function returns an overfitting histogram when a data matrix
     is given as an input. The output is an evaluation tree which is
     grown with greedy growing. The evaluation tree defines a partition
     of the sample space. The evaluation tree may be pruned to get a
     density estimate.

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

     densplit(dendat, minobs=NULL, leaf=0, method="loglik", 
     splitscan=0, seedf=1, suppo=NULL)

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

  dendat: n*d data matrix

  minobs: non-negative integer; splitting of a bin will be continued if
          the bin containes "minobs" or more observations

    leaf: internal (maximal number of leafs in the evaluation tree)

  method: "loglik" or "projec"; the contrast function 

splitscan: internal (random selection of splits)

   seedf: internal

   suppo: 2*d vector of real numbers;  the rectangle to be splitted; 
          the rectangle has to contain the data

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

     Returns an evaluation tree as a list of vectors.  

  direc : integer in 1,...,d; variable which is splitted 

  split : real number; splitting point

   mean : nonnegative number; value of the histogram on the rectangle
          corresponding to the node

  nelem : nonnegative integer; number of observations in the rectangle
          corresponding to the node

    ssr : real number; value of the likelihood criterion

 volume : non-negative number; volume of the rectangle corresponding to
          the node 

   left : non-negative integer; link to the left child, 0 if terminal
          node 

  right : non-negative integer; link to the right child, 0 if terminal
          node

    low : the lower vertice of the rectangles

    upp : the upper vertice of the rectangles

      N : the number of grid points at each direction

 support: the support of the histogram

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

     Jussi Klemel

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

     'prune', 'eval.pick'

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

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

     treeseq<-prune(et)
     treeseq$leafs
     len<-length(treeseq$leafs)

     leaf<-treeseq$leafs[len-10]
     leaf
     etsub<-eval.pick(treeseq,leaf=leaf)

     dp<-draw.pcf(etsub)
     persp(dp$x,dp$y,dp$z,phi=25,theta=-120)

