densplit {delt} | R Documentation |
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.
densplit(dendat, minobs=NULL, leaf=0, method="loglik", splitscan=0, seedf=1, suppo=NULL)
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 |
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 |
Jussi Klemela
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)