prune.quint {quint}

Pruning of a Qualitative Interaction Tree

Description

Determines the optimally pruned size of the tree by applying the one standard error rule to the results from the bias-corrected bootstrap procedure. At the end of the pruning procedure, it is checked whether the pruned tree satisfies the qualitative interaction condition. If this condition is not met, it is concluded that there is no qualitative tratment-subgroup interaction and a tree containing only the root node is returned.

Usage

## S3 method for class 'quint'
prune(tree, pp = 1, ...)

Arguments

tree

fitted tree of the class quint.

pp

pruning parameter, the constant (c) to be used in the c*standard error rule. The default value is 1.

...

optional additional arguments.

Details

The pruning algorithm of quint is explained in Dusseldorp & Van Mechelen (2014), Appendix B of the online supplementary material. It is based on the bias-corrected bootstrap pruning procedure (Le Blanc & Crowley, 1993) and the one standard error rule (Breiman, Friedman, Olshen, & Stone, 1984). The one standard error rule for quint uses the estimates of the bias-corrected criterion value (C) and its standard error for each value of L (= maximum number of leaves). The optimally pruned tree corresponds to the smallest tree with a bias-corrected C higher or equal to the maximum bias-corrected C minus its standard error.

Value

Returns an object of class quint. The number of leaves of this object is equal to the optimally pruned size of the tree.

References

Breiman L., Friedman J.H., Olshen R.A. and Stone C.J. (1984). Classification and Regression Trees. Chapman & Hall/CRC: Boca Raton.

Dusseldorp E. and Van Mechelen I. (2014). Qualitative interaction trees: a tool to identify qualitative treatment-subgroup interactions. Statistics in Medicine, 33(2), 219-237. DOI: 10.1002/sim.5933.

LeBlanc M. and Crowley J. (1993). Survival trees by goodness of split. Journal of the American Statistical Association, 88, 457-467.

See Also

quint.control, quint, quint.bootstrapCI

Examples

data(bcrp)
formula2 <- I(cesdt1-cesdt3)~cond |age+trext+uncomt1+disopt1+negsoct1
#Adjust the control parameters only to save computation time in the example;
#The default control parameters are preferred
control2 <- quint.control(maxl=5,B=2)
set.seed(2) #this enables you to repeat the results of the bootstrap procedure
quint2 <- quint(formula2, data= subset(bcrp,cond<3),control=control2)
quint2pr <- prune(quint2)
summary(quint2pr)