Preface to First Edition - lib

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64 SIMPLE INFERENCEresiduals and an adjustment:√(njk − E jk ) 2 /E jk√(1 − nj·/n)(1 − n·k /n) .When the variables forming the contingency table are independent, theadjusted residuals are approximately normally distributed with mean zeroand standard deviation one. Write a general R function to calculate bothstandardised and adjusted residuals for any r × c contingency table andapply it to the piston ring data.Ex. 3.4 For the data in table rearrests estimate the difference betweenthe probability of being rearrested after being tried in an adult court andin a juvenile court, and find a 95% confidence interval for the populationdifference.© 2010 by Taylor and Francis Group, LLC
CHAPTER 4Conditional Inference: GuessingLengths, Suicides, GastrointestinalDamage, and Newborn Infants4.1 IntroductionThere are many experimental designs or studies where the subjects are nota random sample from some well-defined population. For example, subjectsrecruited for a clinical trial are hardly ever a random sample from the setof all people suffering from a certain disease but are a selection of patientsshowing up for examination in a hospital participating in the trial. Usually,the subjects are randomly assigned to certain groups, for example a controland a treatment group, and the analysis needs to take this randomisation intoaccount. In this chapter, we discuss such test procedures usually known as(re)-randomisation or permutation tests.In the room width estimation experiment reported in Chapter 3, 40 of theestimated widths (in feet) of 69 students and 26 of the estimated widths (inmetres) of 44 students are tied. In fact, this violates one assumption of theunconditional test procedures applied in Chapter 3, namely that the measurementsare drawn from a continuous distribution. In this chapter, the data willbe reanalysed using conditional test procedures, i.e., statistical tests wherethe distribution of the test statistics under the null hypothesis is determinedconditionally on the data at hand. A number of other data sets will also beconsidered in this chapter and these will now be described.Mann (1981) reports a study carried out to investigate the causes of jeeringor baiting behaviour by a crowd when a person is threatening to commitsuicide by jumping from a high building. A hypothesis is that baiting is morelikely to occur in warm weather. Mann (1981) classified 21 accounts of threatenedsuicide by two factors, the time of year and whether or not baiting occurred.The data are given in Table 4.1 and the question is whether they giveany evidence to support the hypothesis? The data come from the northernhemisphere, so June–September are the warm months.65© 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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Page 37 and 38: COMPUTING WITH DATA 15Max. : 20.960
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Page 45 and 46: SUMMARY 23examples of these functio
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Page 67 and 68: INTRODUCTION 47table. Here there ar
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Page 87 and 88: INTRODUCTION 67Table 4.4:Lanza data
Page 89 and 90: CONDITIONAL TEST PROCEDURES 69and x
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Page 115 and 116: SUMMARY 95Table 5.4:schooldays data
Page 117 and 118: CHAPTER 6Simple and Multiple Linear
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Page 121 and 122: MULTIPLE LINEAR REGRESSION 101The e
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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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208 SURVIVAL ANALYSISR> summary(GBS
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210 SURVIVAL ANALYSISR> layout(matr
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212 SURVIVAL ANALYSISmodels includi
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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: 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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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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