Preface to First Edition - lib

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150 DENSITY ESTIMATIONR> persp(x = CYGOB1d$x1, y = CYGOB1d$x2, z = CYGOB1d$fhat,+ xlab = "log surface temperature",+ ylab = "log light intensity",+ zlab = "estimated density",+ theta = -35, axes = TRUE, box = TRUE)estimated densitylog light intensitylog surface temperatureFigure 8.6The bivariate density estimate of the CYGOB1 data, here shown in athree-dimensional fashion using the persp function.R> startparam opp opp$parp mu1 sd1 mu2 sd2© 2010 by Taylor and Francis Group, LLC
ANALYSIS USING R 1510.360891 54.612125 5.872379 80.093414 5.867288$value[1] 1034.002$countsfunction gradient55 55$convergence[1] 0Of course, optimising the appropriate likelihood ‘by hand’ is not very convenient.In fact, (at least) two packages offer high-level functionality for estimatingmixture models. The first one is package mclust (Fraley et al., 2009)implementing the methodology described in Fraley and Raftery (2002). Here,a Bayesian information criterion (BIC) is applied to choose the form of themixture model:R> library("mclust")R> mc mcbest model: equal variance with 2 componentsand the estimated means areR> mc$parameters$mean1 254.61911 80.09384with estimated standard deviation (found to be equal within both groups)R> sqrt(mc$parameters$variance$sigmasq)[1] 5.86848The proportion is ˆp = 0.36. The second package is called flexmix whose functionalityis described by Leisch (2004). A mixture of two normals can be fittedusingR> library("flexmix")R> fl parameters(fl, component = 1)Comp.1coef.(Intercept) 54.628701sigma 5.895234R> parameters(fl, component = 2)Comp.2coef.(Intercept) 80.098582sigma 5.871749© 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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Page 121 and 122: MULTIPLE LINEAR REGRESSION 101The e
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Page 135 and 136: SUMMARY 115R> plot(clouds_lm)Cook's
Page 137 and 138: CHAPTER 7Logistic Regression and Ge
Page 139 and 140: INTRODUCTION 119Table 7.2:womensrol
Page 141 and 142: LOGISTIC REGRESSION AND GENERALISED
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Page 157 and 158: SUMMARY 137Table 7.5:bladdercancer
Page 159 and 160: CHAPTER 8Density Estimation: Erupti
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Page 175 and 176: SUMMARY 155R> layout(matrix(1:2, nc
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 189 and 190: 170 RECURSIVE PARTITIONINGOne way o
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Page 197 and 198: INTRODUCTION 179Table 10.2:USairpol
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Page 215 and 216: 198 SURVIVAL ANALYSISTable 11.1:gli
Page 217 and 218: 200 SURVIVAL ANALYSISspect to time
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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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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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