Preface to First Edition - lib

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192 SMOOTHERS AND GENERALISED ADDITIVE MODELSR> SO2hat SO2 plot(SO2hat, SO2 - SO2hat, type = "n", xlim = c(0, 110))R> text(SO2hat, SO2 - SO2hat, labels = rownames(USairpollution),+ adj = 0)R> abline(h = 0, lty = 2, col = "grey")ProvidenceSO2 − SO2hat−10 0 10 20 30 40 50St. Pittsburgh LouisClevelandHartfordBaltimorePhiladelphiaAlbanyPhoenix Salt NorfolkLake CityAtlanta Richmond Washington Louisville WilmingtonWichitaSanJacksonvilleFranciscoNew Albuquerque CharlestonDallas Miami Little Denver Orleans MinneapolisNashville Rock SeattleCincinnati ColumbusMemphis Houston IndianapolisBuffalo Omaha Des Milwaukee Moines DetroitKansas CityChicago0 20 40 60 80 100SO2hatFigure 10.7Residual plot of SO 2 concentration.to the model, we aren’t able to select a smaller subset of the covariates formodelling and thus fitting a model using gam is still complicated (and will notadd much knowledge anyway).10.3.3 Risk Factors for KyphosisBefore modelling the relationship between kyphosis and the three exploratoryvariables age, starting vertebral level of the surgery and number of vertebrae© 2010 by Taylor and Francis Group, LLC
ANALYSIS USING R 193R> layout(matrix(1:3, nrow = 1))R> spineplot(Kyphosis ~ Age, data = kyphosis,+ ylevels = c("present", "absent"))R> spineplot(Kyphosis ~ Number, data = kyphosis,+ ylevels = c("present", "absent"))R> spineplot(Kyphosis ~ Start, data = kyphosis,+ ylevels = c("present", "absent"))Kyphosispresent absent0 20 80 120 1600.0 0.2 0.4 0.6 0.8 1.0Kyphosispresent absent2 3 4 5 70.0 0.2 0.4 0.6 0.8 1.0Kyphosispresent absent0 4 8 12 14 160.0 0.2 0.4 0.6 0.8 1.0AgeNumberStartFigure 10.8Spinograms of the three exploratory variables and response variablekyphosis.involved, we investigate the partial associations by so-called spinograms, asintroduced in Chapter 2. The numeric exploratory covariates are discretisedand their empirical relative frequencies are plotted against the conditionalfrequency of kyphosis in the corresponding group. Figure 10.8 shows thatkyphosis is absent in very young or very old children, children with a smallstarting vertebral level and high number of vertebrae involved.The logistic additive model needed to describe the conditional probabilityof kyphosis given the exploratory variables can be fitted using function gam.Here, the dimension of the basis (k) has to be modified for Number and Startsince these variables are heavily tied. As for generalised linear models, thefamily argument determines the type of model to be fitted, a logistic modelin our case:R> kyphosis_gam kyphosis_gamFamily: binomialLink function: logit© 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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ANALYSIS USING R 109R> summary(clou
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ANALYSIS USING R 111R> psymb plot(
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SUMMARY 113R> plot(clouds_fitted, c
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SUMMARY 115R> plot(clouds_lm)Cook's
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CHAPTER 7Logistic Regression and Ge
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INTRODUCTION 119Table 7.2:womensrol
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LOGISTIC REGRESSION AND GENERALISED
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ANALYSIS USING R 123R> data("plasma
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ANALYSIS USING R 125R> summary(plas
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ANALYSIS USING R 1277.3.2 Women’s
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ANALYSIS USING R 129R> myplot(role.
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ANALYSIS USING R 131R> role.fitted2
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ANALYSIS USING R 133R> summary(poly
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ANALYSIS USING R 1357.3.4 Driving a
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SUMMARY 137Table 7.5:bladdercancer
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Page 177 and 178: SUMMARY 157Table 8.4: birthdeathrat
Page 179 and 180: SUMMARY 159Table 8.5:schizophrenia
Page 181 and 182: 162 RECURSIVE PARTITIONINGTable 9.1
Page 183 and 184: 164 RECURSIVE PARTITIONINGBoth sets
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Page 221 and 222: 204 SURVIVAL ANALYSISIn the Cox mod
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Page 225 and 226: 208 SURVIVAL ANALYSISR> summary(GBS
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Page 231 and 232: 214 ANALYSING LONGITUDINAL DATA Iti
Page 233 and 234: 216 ANALYSING LONGITUDINAL DATA ITa
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Page 247 and 248: CHAPTER 13Analysing Longitudinal Da
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ANALYSIS USING R: GEE 245R> layout(
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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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ANALYSIS USING R 289R> score plot(
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ANALYSIS USING R 291R> score plot(
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ANALYSIS USING R 293Yuping (CHN) Ha
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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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ANALYSIS USING R 32518.3 Analysis U
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ANALYSIS USING R 327R> pottery_sing
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ANALYSIS USING R 329R> rge planet.
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ANALYSIS USING R 331R> plot(planet_
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ANALYSIS USING R 333R> scatterplot3
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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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