# prune: Prepares for pruning an overfitting evaluation tree In delt: Estimation of Multivariate Densities Using Adaptive Partitions

## Description

Finds a sequence of nodes of an overfitting evaluation tree which are candidates to be the pruning nodes. Pruning a tree means removing a branch starting from a node.

## Usage

 `1` ```prune(et) ```

## Arguments

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

## Value

A list containing the following components.

 `tree` the original tree which was given as the input `delnodes ` vector giving a sequence of nodes in the order in which we should prune the branches starting from these nodes `delend` vector whose length is the number of subtrees of the original tree. With the help of "delend" we define the subtrees. Elements of "delend" define a sequence of nodes from "delnodes" in the following way: (1:delend[1]) is the first sequence, (delend[1]+1:delend[2]) is the second sequence, and so on. Then, i:th subtree is the result of pruning branches away whose roots are the nodes which are the first delend[i] elements of delnodes. `leafs` vector whose length is the number of subtrees of the original tree; number of leafs of the subtrees `alfa` vector whose length is the number of subtrees of the original tree; value of the corresponding alfa (complexity parameter) for every subtree `loglik` vector whose length is the number of subtrees of the original tree; the value of the likelihood criterion for the subtree

## Author(s)

Jussi Klemela

`densplit`, `eval.pick`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```library(denpro) dendat<-sim.data(n=100,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) ```