# densplit: Calculation of an overfitting histogram In delt: Estimation of Multivariate Densities Using Adaptive Partitions

## Description

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.

## Usage

 ```1 2``` ```densplit(dendat, minobs=NULL, leaf=0, method="loglik", splitscan=0, seedf=1, suppo=NULL) ```

## Arguments

 `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

## Value

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

## See Also

`prune`, `eval.pick`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```library(denpro) 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) ```

delt documentation built on May 30, 2017, 7:34 a.m.