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

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

See Also

densplit, eval.pick

Examples

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)

delt documentation built on May 2, 2019, 3:42 p.m.