Preface to First Edition - lib

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22 AN INTRODUCTION TO RR> tmp tmp$country plot(log(marketvalue) ~ country, data = tmp,+ ylab = "log(marketvalue)", varwidth = TRUE)log(marketvalue)−2 0 2 4Germany India Turkey United KingdomcountryFigure 1.4Boxplots of the logarithms of the market value for four selected countries,the width of the boxes is proportional to the square roots of thenumber of companies.© 2010 by Taylor and Francis Group, LLC
SUMMARY 23examples of these functions and those that produce more interesting graphicsin later chapters.ExercisesEx. 1.1 Calculate the median profit for the companies in the US and themedian profit for the companies in the UK, France and Germany.Ex. 1.2 Find all German companies with negative profit.Ex. 1.3 To which business category do most of the Bermuda island companiesbelong?Ex. 1.4 For the 50 companies in the Forbes data set with the highest profits,plot sales against assets (or some suitable transformation of each variable),labelling each point with the appropriate country name which may needto be abbreviated (using abbreviate) to avoid making the plot look too‘messy’.Ex. 1.5 Find the average value of sales for the companies in each countryin the Forbes data set, and find the number of companies in each countrywith profits above 5 billion US dollars.© 2010 by Taylor and Francis Group, LLC
Page 3 and 4: A Handbook ofStatisticalAnalysesUsi
Page 5 and 6: DedicationTo our wives, Mary-Elizab
Page 7 and 8: Preface to First EditionThis book i
Page 9 and 10: List of Figures1.1 Histograms of th
Page 11 and 12: 6.8 Normal probability plot of resi
Page 13 and 14: 12.2 R output of the linear mixed-e
Page 15 and 16: 18.7 Within-cluster sum of squares
Page 17 and 18: 4.6 Lanza data. Misoprostol randomi
Page 19 and 20: 15.2 BCG data. Meta-analysis on BCG
Page 21 and 22: 6 Simple and Multiple Linear Regres
Page 23 and 24: CHAPTER 1An Introduction to R1.1 Wh
Page 25 and 26: INSTALLING R 3One can change the ap
Page 27 and 28: DATA OBJECTS IN R 5http://CRAN.R-pr
Page 29 and 30: DATA OBJECTS IN R 7R> help("Forbes2
Page 31 and 32: DATA IMPORT AND EXPORT 9As a simple
Page 33 and 34: BASIC DATA MANIPULATION 11The funct
Page 35 and 36: BASIC DATA MANIPULATION 13name sale
Page 37 and 38: COMPUTING WITH DATA 15Max. : 20.960
Page 39 and 40: COMPUTING WITH DATA 17Error in quan
Page 41 and 42: COMPUTING WITH DATA 19R> layout(mat
Page 43: © 2010 by Taylor and Francis Group
Page 47 and 48: 26 DATA ANALYSIS USING GRAPHICAL DI
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Page 65 and 66: CHAPTER 3Simple Inference: Guessing
Page 67 and 68: INTRODUCTION 47table. Here there ar
Page 69 and 70: STATISTICAL TESTS 49Table 3.4:pisto
Page 71 and 72: STATISTICAL TESTS 51assumed to have
Page 73 and 74: ANALYSIS USING R 53procedure is McN
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Page 83 and 84: SUMMARY 63R> mcnemar.test(rearrests
Page 85 and 86: CHAPTER 4Conditional Inference: Gue
Page 87 and 88: INTRODUCTION 67Table 4.4:Lanza data
Page 89 and 90: CONDITIONAL TEST PROCEDURES 69and x
Page 91 and 92: ANALYSIS USING R 71R> hist(meandiff
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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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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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