Preface to First Edition - lib

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110 SIMPLE AND MULTIPLE LINEAR REGRESSIONseedingno:cloudcover0.38786207seedingyes:cloudcover-0.09839285seedingno:prewetness4.10834188seedingyes:prewetness1.55127493seedingno:echomotionstationary3.15281358seedingyes:echomotionstationary2.59059513and the corresponding covariance matrix Cov( ˆβ ⋆ ) is available from the vcovmethodR> Vbetastar sqrt(diag(Vbetastar))(Intercept)2.78773403seedingyes4.44626606time0.02505286seedingno:sne0.84452994seedingyes:sne0.92837010seedingno:cloudcover0.21785501seedingyes:cloudcover0.11028981seedingno:prewetness3.60100694seedingyes:prewetness2.69287308seedingno:echomotionstationary1.93252592seedingyes:echomotionstationary1.81725973The results of the linear model fit, as shown in Figure 6.5, suggests thatrainfall can be increased by cloud seeding. Moreover, the model indicates thathigher values of the S-Ne criterion lead <strong>to</strong> less rainfall, but only on days whencloud seeding happened, i.e., the interaction of seeding with S-Ne significantlyaffects rainfall. A suitable graph will help in the interpretation of this result.We can plot the relationship between rainfall and S-Ne for seeding and nonseedingdays using the R code shown with Figure 6.6.© 2010 by Taylor and Francis Group, LLC
ANALYSIS USING R 111R> psymb plot(rainfall ~ sne, data = clouds, pch = psymb,+ xlab = "S-Ne criterion")R> abline(lm(rainfall ~ sne, data = clouds,+ subset = seeding == "no"))R> abline(lm(rainfall ~ sne, data = clouds,+ subset = seeding == "yes"), lty = 2)R> legend("<strong>to</strong>pright", legend = c("No seeding", "Seeding"),+ pch = 1:2, lty = 1:2, bty = "n")rainfall0 2 4 6 8 10 12No seedingSeeding1.5 2.0 2.5 3.0 3.5 4.0 4.5S−Ne criterionFigure 6.6Regression relationship between S-Ne criterion and rainfall with andwithout seeding.© 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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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
Page 93 and 94: ANALYSIS USING R 73R> wilcox_test(y
Page 95 and 96: ANALYSIS USING R 75For the first st
Page 97 and 98: SUMMARY 77R> mh_test(anomalies)Asym
Page 99 and 100: CHAPTER 5Analysis of Variance: Weig
Page 101 and 102: INTRODUCTION 81The data in Table 5.
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Page 107 and 108: ANALYSIS USING R 87R> plot.design(f
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Page 111 and 112: ANALYSIS USING R 91The cbind statem
Page 113 and 114: ANALYSIS USING R 93Df Roy approx F
Page 115 and 116: SUMMARY 95Table 5.4:schooldays data
Page 117 and 118: CHAPTER 6Simple and Multiple Linear
Page 119 and 120: SIMPLE LINEAR REGRESSION 99hours be
Page 121 and 122: MULTIPLE LINEAR REGRESSION 101The e
Page 123 and 124: ANALYSIS USING R 1036.3.1 Regressio
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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
Page 143 and 144: ANALYSIS USING R 123R> data("plasma
Page 145 and 146: ANALYSIS USING R 125R> summary(plas
Page 147 and 148: ANALYSIS USING R 1277.3.2 Women’s
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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 165 and 166: DENSITY ESTIMATION 1451 R> plot(xgr
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Page 169 and 170: 2.2ANALYSIS USING R 149R> library("
Page 171 and 172: ANALYSIS USING R 1510.360891 54.612
Page 173 and 174: ANALYSIS USING R 153The results are
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Page 177 and 178: SUMMARY 157Table 8.4: birthdeathrat
Page 179 and 180: 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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