Preface to First Edition - lib

ANALYSIS USING R 173R> plot(bodyfat_ctree)1hipcircp < 0.0012waistcircp < 0.001≤ 108 > 1087kneebreadthp = 0.003≤ 76.5 > 76.54hipcircp < 0.001≤ 10 > 10≤ 99 > 99Node 3 (n = 17)Node 5 (n = 11)Node 6 (n = 17)Node 8 (n = 17)Node 9 (n = 9)605040302010605040302010605040302010605040302010605040302010Figure 9.6Conditional inference tree with the distribution of body fat contentshown for each terminal leaf.9.3.3 Trees RevisitedAnother approach to recursive partitioning, making a connection to classicalstatistical test problems such as those discussed in Chapter 4, is implementedin the party package (Hothorn et al., 2006b, 2009c). In each node of thosetrees, a significance test on independence between any of the covariates andthe response is performed and a split is established when the p-value, possiblyadjusted for multiple comparisons, is smaller than a pre-specified nominal levelα. This approach has the advantage that one does not need to prune backlarge initial trees since we have a statistically motivated stopping criterion –the p-value – at hand.For the body fat data, such a conditional inference tree can be computedusing the ctree functionR> library("party")R> bodyfat_ctree glaucoma_ctree