Preface to First Edition - lib

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ANALYSIS USING R 261R> par(mai = par("mai") * c(1, 2.1, 1, 0.5))R> layout(matrix(1:2, ncol = 2))R> ci1 ci2 ox sx plot(ci1, xlim = c(-0.6, 2.6), main = ox,+ xlab = "Difference", ylim = c(0.5, 3.5))R> plot(ci2, xlim = c(-0.6, 2.6), main = sx,+ xlab = "Difference", ylim = c(0.5, 3.5))Tukey (ordinary S n )Tukey (sandwich S n )intr − shrt (●)intr − shrt (●)long − shrt(●)long − shrt(●)long − intr(●)long − intr(●)−0.5 0.5 1.5 2.5Difference−0.5 0.5 1.5 2.5DifferenceFigure 14.2Simultaneous confidence intervals for the alpha data based on theordinary covariance matrix (left) and a sandwich estimator (right).and therefore the results obtained from function TukeyHSD might be less accurate.14.3.2 Deer BrowsingSince we have to take the spatial structure of the deer browsing data intoaccount, we cannot simply use a logistic regression model as introduced inChapter 7. One possibility is to apply a mixed logistic regression model (usingpackage lme4, Bates and Sarkar, 2008) with random intercept accounting forthe spatial variation of the trees. These models have already been discussed inChapter 13. For each plot nested within a set of five plots oriented on a 100mtransect (the location of the transect is determined by a predefined equallyspaced lattice of the area under test), a random intercept is included in themodel. Essentially, trees that are close to each other are handled like repeatedmeasurements in a longitudinal analysis. We are interested in probability estimatesand confidence intervals for each tree species. Each of the six fixedparameters of the model corresponds to one species (in absence of a global© 2010 by Taylor and Francis Group, LLC
262 SIMULTANEOUS INFERENCE AND MULTIPLE COMPARISONSintercept term); therefore, K = diag(6) is the linear function we are interestedin:R> mmod K K[,1] [,2] [,3] [,4] [,5][1,] 1 0 0 0 0[2,] 0 1 0 0 0[3,] 0 0 1 0 0[4,] 0 0 0 1 0[5,] 0 0 0 0 1In order to help interpretation, the names of the tree species and the correspondingsample sizes (computed via table) are added to K as row names;this information will carry through all subsequent steps of our analysis:R> colnames(K) ci ci$confint ci$confint[,2:3]
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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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CHAPTER 8Density Estimation: Erupti
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DENSITY ESTIMATION 141The Hertzspru
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DENSITY ESTIMATION 143rectangular:
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DENSITY ESTIMATION 1451 R> plot(xgr
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ANALYSIS USING R 1478.3 Analysis Us
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2.2ANALYSIS USING R 149R> library("
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ANALYSIS USING R 1510.360891 54.612
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ANALYSIS USING R 153The results are
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SUMMARY 155R> layout(matrix(1:2, nc
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SUMMARY 157Table 8.4: birthdeathrat
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SUMMARY 159Table 8.5:schizophrenia
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162 RECURSIVE PARTITIONINGTable 9.1
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164 RECURSIVE PARTITIONINGBoth sets
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166 RECURSIVE PARTITIONINGR> librar
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168 RECURSIVE PARTITIONINGR> DEXfat
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170 RECURSIVE PARTITIONINGOne way o
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172 RECURSIVE PARTITIONINGR> librar
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174 RECURSIVE PARTITIONINGR> plot(g
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CHAPTER 10Scatterplot Smoothers and
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INTRODUCTION 179Table 10.2:USairpol
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SMOOTHERS AND GENERALISED ADDITIVE
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ANALYSIS USING R 187R> plot(time ~
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ANALYSIS USING R 189R> x y men150
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ANALYSIS USING R 191R> USair_gam l
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ANALYSIS USING R 193R> layout(matri
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ANALYSIS USING R 195ter 9) where th
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198 SURVIVAL ANALYSISTable 11.1:gli
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200 SURVIVAL ANALYSISspect to time
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202 SURVIVAL ANALYSISHazard0.00 0.0
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204 SURVIVAL ANALYSISIn the Cox mod
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206 SURVIVAL ANALYSISExact Logrank
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Page 247 and 248: CHAPTER 13Analysing Longitudinal Da
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Page 267 and 268: SUMMARY 251Table 13.3:schizophrenia
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Page 281 and 282: CHAPTER 15Meta-Analysis: Nicotine G
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Page 295 and 296: SUMMARY 2811 / standard error2 4 6
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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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