final.tree: Finding the Final Tree After Bootstrap

In TimeVTree: Survival Analysis of Time Varying Coefficients Using a Tree-Based Approach

Description

final.tree uses bias-corrected costs obtained from bootstrap function and the predetermined penalty parameter to find the optimal tree from the set of subtrees.

Usage

final.tree(nodetree=nodetree, subtrees=subtrees, omega, alphac=2)

Arguments

nodetree	Fully grown tree from the original data. Output from output.coxphout
subtrees	Pruned subtrees from the original data. Output from prune
omega	Bias (i.e. third index of the output) from bootstrap. Look at the value section of bootstrap for more information.
alphac	Predetermined penalty parameter

Details

final.tree is part of the bootstrap function but can be used to try different penalty parameters without re-running bootstrap.

Value

subtree	output from prune with an additional column 'cost' that contains bootstrap estimate of each subtree
final	A tree with lowest cost value after applying predetermined penalty

References

Xu, R. and Adak, S. (2002), Survival Analysis with Time-Varying Regression Effects Using a Tree-Based Approach. Biometrics, 58: 305-315.

Examples

## Not run: 
data('alcohol')
require(survival)

coxtree <- coxph.tree(alcohol[,'time'], alcohol[,'event'], 
                      x = alcohol[,'alc', drop = FALSE], D = 4)
nodetree <- output.coxphout(coxtree)

subtrees <- prune(nodetree)

store.mult.cont <- bootstrap(B=20, nodetree, subtrees, alcohol[,'time'],
                                alcohol[,'event'], x = alcohol[,'alc', drop = FALSE], 
                                D=4,minfail=20, alphac=2)
                                
Balph <- 0.5 * 2 * log(nrow(alcohol))                                
final.tree <- final.tree(nodetree, subtrees, store.mult.cont[[3]], alphac= Balph)

## End(Not run)

TimeVTree documentation built on May 2, 2019, 2:17 a.m.