Preface to First Edition - lib

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CHAPTER 9Recursive Partitioning: PredictingBody Fat and Glaucoma Diagnosis9.1 IntroductionWorldwide, overweight and obesity are considered to be major health problemsbecause of their strong association with a higher risk of diseases of themetabolic syndrome, including diabetes mellitus and cardiovascular disease, aswell as with certain forms of cancer. Obesity is frequently evaluated by usingsimple indicators such as body mass index, waist circumference, or waist-tohipratio. Specificity and adequacy of these indicators are still controversial,mainly because they do not allow a precise assessment of body composition.Body fat, especially visceral fat, is suggested to be a better predictor of diseasesof the metabolic syndrome. Garcia et al. (2005) report on the developmentof a multiple linear regression model for body fat content by meansof p = 9 common anthropometric measurements which were obtained forn = 71 healthy German women. In addition, the women’s body compositionwas measured by Dual Energy X-Ray Absorptiometry (DXA). This referencemethod is very accurate in measuring body fat but finds little applicability inpractical environments, mainly because of high costs and the methodologicalefforts needed. Therefore, a simple regression model for predicting DXA measurementsof body fat is of special interest for the practitioner. The followingvariables are available (the measurements are given in Table 9.1):DEXfat: body fat measured by DXA, the response variable,age: age of the subject in years,waistcirc: waist circumference,hipcirc: hip circumference,elbowbreadth: breadth of the elbow, andkneebreadth: breadth of the knee.Table 9.1:bodyfat data (package mboost). Body fat predictionby skinfold thickness, circumferences, and bonebreadths.DEXfat age waistcirc hipcirc elbowbreadth kneebreadth41.68 57 100.0 112.0 7.1 9.443.29 65 99.5 116.5 6.5 8.9161© 2010 by Taylor and Francis Group, LLC
162 RECURSIVE PARTITIONINGTable 9.1:bodyfat data (continued).DEXfat age waistcirc hipcirc elbowbreadth kneebreadth35.41 59 96.0 108.5 6.2 8.922.79 58 72.0 96.5 6.1 9.236.42 60 89.5 100.5 7.1 10.024.13 61 83.5 97.0 6.5 8.829.83 56 81.0 103.0 6.9 8.935.96 60 89.0 105.0 6.2 8.523.69 58 80.0 97.0 6.4 8.822.71 62 79.0 93.0 7.0 8.823.42 63 79.0 99.0 6.2 8.623.24 62 72.0 94.0 6.7 8.726.25 64 81.5 95.0 6.2 8.221.94 60 65.0 90.0 5.7 8.230.13 61 79.0 107.5 5.8 8.636.31 66 98.5 109.0 6.9 9.627.72 63 79.5 101.5 7.0 9.446.99 57 117.0 116.0 7.1 10.742.01 49 100.5 112.0 6.9 9.418.63 65 82.0 91.0 6.6 8.838.65 58 101.0 107.5 6.4 8.621.20 63 80.0 96.0 6.9 8.635.40 60 89.0 101.0 6.2 9.229.63 59 89.5 99.5 6.0 8.125.16 32 73.0 99.0 7.2 8.631.75 42 87.0 102.0 6.9 10.840.58 49 90.2 110.3 7.1 9.521.69 63 80.5 97.0 5.8 8.846.60 57 102.0 124.0 6.6 11.227.62 44 86.0 102.0 6.3 8.341.30 61 102.0 122.5 6.3 10.842.76 62 103.0 125.0 7.3 11.128.84 24 81.0 100.0 6.6 9.736.88 54 85.5 113.0 6.2 9.625.09 65 75.3 101.2 5.2 9.329.73 67 81.0 104.3 5.7 8.128.92 45 85.0 106.0 6.7 10.043.80 51 102.2 118.5 6.8 10.626.74 49 78.0 99.0 6.2 9.833.79 52 93.3 109.0 6.8 9.862.02 66 106.5 126.0 6.4 11.440.01 63 102.0 117.0 6.6 10.642.72 42 111.0 109.0 6.7 9.932.49 50 102.0 108.0 6.2 9.8© 2010 by Taylor and Francis Group, LLC
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A Handbook ofStatisticalAnalysesUsi
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DedicationTo our wives, Mary-Elizab
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Preface to First EditionThis book i
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List of Figures1.1 Histograms of th
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6.8 Normal probability plot of resi
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12.2 R output of the linear mixed-e
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18.7 Within-cluster sum of squares
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4.6 Lanza data. Misoprostol randomi
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15.2 BCG data. Meta-analysis on BCG
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6 Simple and Multiple Linear Regres
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CHAPTER 1An Introduction to R1.1 Wh
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INSTALLING R 3One can change the ap
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DATA OBJECTS IN R 5http://CRAN.R-pr
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DATA OBJECTS IN R 7R> help("Forbes2
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DATA IMPORT AND EXPORT 9As a simple
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BASIC DATA MANIPULATION 11The funct
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BASIC DATA MANIPULATION 13name sale
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COMPUTING WITH DATA 15Max. : 20.960
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COMPUTING WITH DATA 17Error in quan
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COMPUTING WITH DATA 19R> layout(mat
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© 2010 by Taylor and Francis Group
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SUMMARY 23examples of these functio
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26 DATA ANALYSIS USING GRAPHICAL DI
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Table 2.2:CHFLS data. Chinese Healt
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Table 2.5:USstates data. Socio-demo
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CHAPTER 3Simple Inference: Guessing
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INTRODUCTION 47table. Here there ar
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STATISTICAL TESTS 49Table 3.4:pisto
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STATISTICAL TESTS 51assumed to have
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ANALYSIS USING R 53procedure is McN
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ANALYSIS USING R 551 R> layout(matr
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ANALYSIS USING R 57R> wilcox.test(I
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ANALYSIS USING R 59R> t.test(moorin
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ANALYSIS USING R 61R> cor.test(~ mo
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SUMMARY 63R> mcnemar.test(rearrests
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CHAPTER 4Conditional Inference: Gue
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INTRODUCTION 67Table 4.4:Lanza data
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CONDITIONAL TEST PROCEDURES 69and x
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ANALYSIS USING R 71R> hist(meandiff
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ANALYSIS USING R 73R> wilcox_test(y
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ANALYSIS USING R 75For the first st
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SUMMARY 77R> mh_test(anomalies)Asym
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CHAPTER 5Analysis of Variance: Weig
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INTRODUCTION 81The data in Table 5.
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ANALYSIS USING R 83set out in the f
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ANALYSIS USING R 85being far more t
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ANALYSIS USING R 87R> plot.design(f
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ANALYSIS USING R 89always be the ca
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ANALYSIS USING R 91The cbind statem
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ANALYSIS USING R 93Df Roy approx F
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SUMMARY 95Table 5.4:schooldays data
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CHAPTER 6Simple and Multiple Linear
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SIMPLE LINEAR REGRESSION 99hours be
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MULTIPLE LINEAR REGRESSION 101The e
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ANALYSIS USING R 1036.3.1 Regressio
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ANALYSIS USING R 105R> layout(matri
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ANALYSIS USING R 107R> data("clouds
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214 ANALYSING LONGITUDINAL DATA Iti
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216 ANALYSING LONGITUDINAL DATA ITa
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218 ANALYSING LONGITUDINAL DATA Iot
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220 ANALYSING LONGITUDINAL DATA IR>
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226 ANALYSING LONGITUDINAL DATA IKe
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228 ANALYSING LONGITUDINAL DATA Ido
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CHAPTER 13Analysing Longitudinal Da
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METHODS FOR NON-NORMAL DISTRIBUTION
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ANALYSIS USING R: GEE 239R> summary
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ANALYSIS USING R: RANDOM EFFECTS 24
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ANALYSIS USING R: RANDOM EFFECTS 24
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SUMMARY 251Table 13.3:schizophrenia
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254 SIMULTANEOUS INFERENCE AND MULT
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CHAPTER 15Meta-Analysis: Nicotine G
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SYSTEMATIC REVIEWS AND META-ANALYSI
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STATISTICS OF META-ANALYSIS 271Sele
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ANALYSIS USING R 273the parameters
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ANALYSIS USING R 275R> plot(smoking
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PUBLICATION BIAS 277R> summary(BCG_
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SUMMARY 279R> summary(BCG_mod)Call:
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SUMMARY 2811 / standard error2 4 6
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Table 15.4:aspirin data. Meta-analy
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CHAPTER 16Principal Component Analy
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PRINCIPAL COMPONENT ANALYSIS 287nie
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SUMMARY 295R> biplot(heptathlon_pca
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SUMMARY 297rainJuly: rainfall in Ju
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Table 17.1:watervoles data. Water v
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302 MULTIDIMENSIONAL SCALINGn in nu
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304 MULTIDIMENSIONAL SCALINGwill be
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306 MULTIDIMENSIONAL SCALINGR> x y
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308 MULTIDIMENSIONAL SCALINGR> libr
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310 MULTIDIMENSIONAL SCALINGR> plot
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Table 17.3:eurodist data (package d
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CHAPTER 18Cluster Analysis: Classif
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INTRODUCTION 317mass, mass), the pe
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CLUSTER ANALYSIS 319x 20 2 4 6 8 10
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CLUSTER ANALYSIS 32141031678925Figu
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CLUSTER ANALYSIS 3231. Find some in
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BibliographyAdler, D. and Murdoch,
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BIBLIOGRAPHY 337Chambers, J. M. and
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BIBLIOGRAPHY 339Everitt, B. S. and
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BIBLIOGRAPHY 341Heitjan, D. F. (199
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BIBLIOGRAPHY 343Leisch, F. and Ross
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BIBLIOGRAPHY 345Proudfoot, J., Gold
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BIBLIOGRAPHY 347Stevens, J. (2001),
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