Prune a fitted survival tree

Description

The prune function evaluates and prunes a survival tree that has been fitted by DStree. Different criteria can be used for evaluation (e.g. Brier Score or AIC). The result of the prune function is the optimal subtree (of class DStree) with regard to the chosen criterium, as well as various performance measures that were obtained from the subtrees during analysis. The returned performance measures are the Brier Score, the deviance, and an information criterion defined by gamma.

Usage

## S3 method for class 'DStree'
prune(tree, data, gamma = 2, which, ...)

Arguments

Details

The subtrees are the cost-minimzing subtrees in terms of deviance for given complexity parameters of the fitted tree. See Therneau et al (2013) p.12-13.

Value

prune returns one DStree object and four vectors of length equal to the number of subtrees:

• nsplit number of splits for every subtree

• CRIT value of the user defined information criterion (underlying formula: CRIT = deviance + gamma * |terminal leaves| * |time periods|).

• DEV deviance

• BRIER Integrated Brier Score, see Hothorn et al. (2004)

• pruned.fit optimal subtree regarding the choosen criterium specified in which

References

Hothorn T., Lausen B., Benner A. and Radespiel-Troeger M. (2004), Bagging Survival Trees. Statistics in medicine 23 (1), 77-91.

Therneau T. and Atkinson E., An introduction to recursive partitioning using the RPART routines, Technical Report 61, Section of Biostatistics, Mayo Clinic, Rochester.

Examples

data(cost)
## Discretize observed days to years
d.cost <- dis.cost(cost)

##Build tree
tree <- DStree(time~prevStroke+age+sex+alcohol+smoke,status="status",data=d.cost)

# Determine subtree with minimum AIC
prunedtree <- prune(tree,d.cost,which="CRIT")
prunedtree$prunedfit

# Visualize AIC/Deviance of subtrees
plot(prunedtree$nsplit,prunedtree$CRIT)
plot(prunedtree$nsplit,prunedtree$DEV)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(DStree)
Loading required package: rpart
Loading required package: pec
Loading required package: prodlim
Loading required package: Ecdat
Loading required package: Ecfun

Attaching package: 'Ecfun'

The following object is masked from 'package:base':

    sign


Attaching package: 'Ecdat'

The following object is masked from 'package:datasets':

    Orange

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/DStree/prune.DStree.Rd_%03d_medium.png", width=480, height=480)
> ### Name: prune.DStree
> ### Title: Prune a fitted survival tree
> ### Aliases: prune.DStree
> 
> ### ** Examples
> 
> data(cost)
> ## Discretize observed days to years
> d.cost <- dis.cost(cost)
> 
> ##Build tree
> tree <- DStree(time~prevStroke+age+sex+alcohol+smoke,status="status",data=d.cost)
> 
> # Determine subtree with minimum AIC
> prunedtree <- prune(tree,d.cost,which="CRIT")
> prunedtree$prunedfit
n= 518 

node), split, n, deviance, Med Surv
      * denotes terminal node

1) root 518 2303.6310 3.6  
  2) age>=74.5 275 1227.1380 2.6 *
  3) age< 74.5 243 1018.7160 5.6  
    6) age>=55.5 206  893.5323 4.7 *
    7) age< 55.5 37   89.2027  NA *
> 
> # Visualize AIC/Deviance of subtrees
> plot(prunedtree$nsplit,prunedtree$CRIT)
> plot(prunedtree$nsplit,prunedtree$DEV)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>