Preface to First Edition - lib

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SUMMARY 227R> bdi plot(1:4, rep(-0.5, 4), type = "n", axes = FALSE,+ ylim = c(0, 50), xlab = "Months", ylab = "BDI")R> axis(1, at = 1:4, labels = c(0, 2, 3, 5))R> axis(2)R> for (i in 1:4) {+ dropout
228 ANALYSING LONGITUDINAL DATA Idom effects models can also be applied to non-normal responses, for examplebinary variables – see, for example, Everitt (2002b).The lack of independence of repeated measures data is what makes themodelling of such data a challenge. But even when only a single measurementof a response is involved, correlation can, in some circumstances, occur betweenthe response values of different individuals and cause similar problems.As an example consider a randomised clinical trial in which subjects are recruitedat multiple study centres. The multicentre design can help to provideadequate sample sizes and enhance the generalisability of the results. Howeverfactors that vary by centre, including patient characteristics and medical practisepatterns, may exert a sufficiently powerful effect to make inferences thatignore the ‘clustering’ seriously misleading. Consequently it may be necessaryto incorporate random effects for centres into the analysis.ExercisesEx. 12.1 Use the lm function to fit a model to the Beat the Blues data thatassumes that the repeated measurements are independent. Compare theresults to those from fitting the random intercept model BtheB_lmer1.Ex. 12.2 Investigate whether there is any evidence of an interaction betweentreatment and time for the Beat the Blues data.Ex. 12.3 Construct a plot of the mean profiles of both groups in the Beat theBlues data, showing also standard deviation bars at each time point.Ex. 12.4 The phosphate data given in Table 12.2 show the plasma inorganicphosphate levels for 33 subjects, 20 of whom are controls and 13 of whomhave been classified as obese (Davis, 2002). Produce separate plots of theprofiles of the individuals in each group, and guided by these plots fit whatyou think might be sensible linear mixed effects models.Table 12.2:phosphate data. Plasma inorganic phosphate levelsfor various time points after glucose challenge.group t0 t0.5 t1 t1.5 t2 t3 t4 t5control 4.3 3.3 3.0 2.6 2.2 2.5 3.4 4.4control 3.7 2.6 2.6 1.9 2.9 3.2 3.1 3.9control 4.0 4.1 3.1 2.3 2.9 3.1 3.9 4.0control 3.6 3.0 2.2 2.8 2.9 3.9 3.8 4.0control 4.1 3.8 2.1 3.0 3.6 3.4 3.6 3.7control 3.8 2.2 2.0 2.6 3.8 3.6 3.0 3.5control 3.8 3.0 2.4 2.5 3.1 3.4 3.5 3.7control 4.4 3.9 2.8 2.1 3.6 3.8 4.0 3.9control 5.0 4.0 3.4 3.4 3.3 3.6 4.0 4.3control 3.7 3.1 2.9 2.2 1.5 2.3 2.7 2.8control 3.7 2.6 2.6 2.3 2.9 2.2 3.1 3.9control 4.4 3.7 3.1 3.2 3.7 4.3 3.9 4.8© 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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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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Page 225 and 226: 208 SURVIVAL ANALYSISR> summary(GBS
Page 227 and 228: 210 SURVIVAL ANALYSISR> layout(matr
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Page 231 and 232: 214 ANALYSING LONGITUDINAL DATA Iti
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Page 249 and 250: METHODS FOR NON-NORMAL DISTRIBUTION
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Page 267 and 268: SUMMARY 251Table 13.3:schizophrenia
Page 269 and 270: 254 SIMULTANEOUS INFERENCE AND MULT
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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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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 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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