Preface to First Edition - lib

ANALYSIS USING R 167R> plot(as.party(bodyfat_prune), tp_args = list(id = FALSE))1waistcirc2hipcirc< 88.4 >= 88.45kneebreadth< 96.25 >= 96.256hipcirc< 11.15 >= 11.15< 109.9 >= 109.9n = 17n = 23n = 13n = 15n = 3605040302010605040302010605040302010605040302010605040302010Figure 9.2Pruned regression tree for body fat data.Given this model, one can predict the (unknown, in real circumstances)body fat content based on the covariate measurements. Here, using the knownvalues of the response variable, we compare the model predictions with theactually measured body fat as shown in Figure 9.3. The three observationswith large body fat measurements in the rightmost terminal node can beidentified easily.9.3.2 Glaucoma DiagnosisWe start with a large initial tree and prune back branches according tothe cross-validation criterion. The default is to use 10 runs of 10-fold crossvalidationand we choose 100 runs of 10-fold cross-validation for reasons to beexplained later.R> data("GlaucomaM", package = "ipred")R> glaucoma_rpart glaucoma_rpart$cptableCP nsplit rel error xerror xstd1 0.65306122 0 1.0000000 1.5306122 0.060543912 0.07142857 1 0.3469388 0.3877551 0.056476303 0.01360544 2 0.2755102 0.3775510 0.055904314 0.01000000 5 0.2346939 0.4489796 0.05960655© 2010 by Taylor and Francis Group, LLC