Preface to First Edition - lib

More documents

Recommendations

Info

ANALYSIS USING R 309R> x y plot(x, y, xlab = "Coordinate 1", ylab = "Coordinate 2",+ xlim = range(voting_mds$points[,1])*1.2, type = "n")R> text(x, y, labels = colnames(voting))R> voting_sh
310 MULTIDIMENSIONAL SCALINGR> plot(voting_sh, pch = ".", xlab = "Dissimilarity",+ ylab = "Distance", xlim = range(voting_sh$x),+ ylim = range(voting_sh$x))R> lines(voting_sh$x, voting_sh$yf, type = "S")Distance5 10 155 10 15DissimilarityFigure 17.4The Shepard diagram for the voting data shows some discrepanciesbetween the original dissimilarities and the multidimensional scalingsolution.17.4 SummaryMultidimensional scaling provides a powerful approach <strong>to</strong> extracting the structurein observed proximity matrices. Uncovering the pattern in this type ofdata may be important for a number of reasons, in particular for discoveringthe dimensions on which similarity judgements have been made.© 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 and 44:
© 2010 by Taylor and Francis Group
Page 45 and 46:
SUMMARY 23examples of these functio
Page 47 and 48:
26 DATA ANALYSIS USING GRAPHICAL DI
Page 49 and 50:
Table 2.2:CHFLS data. Chinese Healt
Page 51 and 52:
Page 53 and 54:
Page 55 and 56:
Page 57 and 58:
Page 59 and 60:
Page 61 and 62:
Page 63 and 64:
Table 2.5:USstates data. Socio-demo
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
Page 75 and 76:
ANALYSIS USING R 551 R> layout(matr
Page 77 and 78:
ANALYSIS USING R 57R> wilcox.test(I
Page 79 and 80:
ANALYSIS USING R 59R> t.test(moorin
Page 81 and 82:
ANALYSIS USING R 61R> cor.test(~ mo
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.
Page 103 and 104:
ANALYSIS USING R 83set out in the f
Page 105 and 106:
ANALYSIS USING R 85being far more t
Page 107 and 108:
ANALYSIS USING R 87R> plot.design(f
Page 109 and 110:
ANALYSIS USING R 89always be the ca
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
Page 125 and 126:
ANALYSIS USING R 105R> layout(matri
Page 127 and 128:
ANALYSIS USING R 107R> data("clouds
Page 129 and 130:
ANALYSIS USING R 109R> summary(clou
Page 131 and 132:
ANALYSIS USING R 111R> psymb plot(
Page 133 and 134:
SUMMARY 113R> plot(clouds_fitted, c
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
Page 149 and 150:
ANALYSIS USING R 129R> myplot(role.
Page 151 and 152:
ANALYSIS USING R 131R> role.fitted2
Page 153 and 154:
ANALYSIS USING R 133R> summary(poly
Page 155 and 156:
ANALYSIS USING R 1357.3.4 Driving a
Page 157 and 158:
SUMMARY 137Table 7.5:bladdercancer
Page 159 and 160:
CHAPTER 8Density Estimation: Erupti
Page 161 and 162:
DENSITY ESTIMATION 141The Hertzspru
Page 163 and 164:
DENSITY ESTIMATION 143rectangular:
Page 165 and 166:
DENSITY ESTIMATION 1451 R> plot(xgr
Page 167 and 168:
ANALYSIS USING R 1478.3 Analysis Us
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
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
Page 185 and 186:
166 RECURSIVE PARTITIONINGR> librar
Page 187 and 188:
168 RECURSIVE PARTITIONINGR> DEXfat
Page 189 and 190:
170 RECURSIVE PARTITIONINGOne way o
Page 191 and 192:
172 RECURSIVE PARTITIONINGR> librar
Page 193 and 194:
174 RECURSIVE PARTITIONINGR> plot(g
Page 195 and 196:
CHAPTER 10Scatterplot Smoothers and
Page 197 and 198:
INTRODUCTION 179Table 10.2:USairpol
Page 199 and 200:
SMOOTHERS AND GENERALISED ADDITIVE
Page 201 and 202:
Page 203 and 204:
Page 205 and 206:
ANALYSIS USING R 187R> plot(time ~
Page 207 and 208:
ANALYSIS USING R 189R> x y men150
Page 209 and 210:
ANALYSIS USING R 191R> USair_gam l
Page 211 and 212:
ANALYSIS USING R 193R> layout(matri
Page 213 and 214:
ANALYSIS USING R 195ter 9) where th
Page 215 and 216:
198 SURVIVAL ANALYSISTable 11.1:gli
Page 217 and 218:
200 SURVIVAL ANALYSISspect to time
Page 219 and 220:
202 SURVIVAL ANALYSISHazard0.00 0.0
Page 221 and 222:
204 SURVIVAL ANALYSISIn the Cox mod
Page 223 and 224:
206 SURVIVAL ANALYSISExact Logrank
Page 225 and 226:
208 SURVIVAL ANALYSISR> summary(GBS
Page 227 and 228:
210 SURVIVAL ANALYSISR> layout(matr
Page 229 and 230:
212 SURVIVAL ANALYSISmodels includi
Page 231 and 232:
214 ANALYSING LONGITUDINAL DATA Iti
Page 233 and 234:
216 ANALYSING LONGITUDINAL DATA ITa
Page 235 and 236:
218 ANALYSING LONGITUDINAL DATA Iot
Page 237 and 238:
220 ANALYSING LONGITUDINAL DATA IR>
Page 239 and 240:
Page 241 and 242:
Page 243 and 244:
226 ANALYSING LONGITUDINAL DATA IKe
Page 245 and 246:
228 ANALYSING LONGITUDINAL DATA Ido
Page 247 and 248:
CHAPTER 13Analysing Longitudinal Da
Page 249 and 250:
METHODS FOR NON-NORMAL DISTRIBUTION
Page 251 and 252:
Page 253 and 254:
Page 255 and 256:
ANALYSIS USING R: GEE 239R> summary
Page 257 and 258:
Page 259 and 260:
Page 261 and 262:
ANALYSIS USING R: GEE 245R> layout(
Page 263 and 264:
ANALYSIS USING R: RANDOM EFFECTS 24
Page 265 and 266:
ANALYSIS USING R: RANDOM EFFECTS 24
Page 267 and 268:
SUMMARY 251Table 13.3:schizophrenia
Page 269 and 270:
254 SIMULTANEOUS INFERENCE AND MULT
Page 271 and 272: 256 SIMULTANEOUS INFERENCE AND MULT
Page 281 and 282: CHAPTER 15Meta-Analysis: Nicotine G
Page 283 and 284: SYSTEMATIC REVIEWS AND META-ANALYSI
Page 285 and 286: STATISTICS OF META-ANALYSIS 271Sele
Page 287 and 288: ANALYSIS USING R 273the parameters
Page 289 and 290: ANALYSIS USING R 275R> plot(smoking
Page 291 and 292: PUBLICATION BIAS 277R> summary(BCG_
Page 293 and 294: SUMMARY 279R> summary(BCG_mod)Call:
Page 295 and 296: SUMMARY 2811 / standard error2 4 6
Page 297 and 298: Table 15.4:aspirin data. Meta-analy
Page 299 and 300: CHAPTER 16Principal Component Analy
Page 301 and 302: PRINCIPAL COMPONENT ANALYSIS 287nie
Page 303 and 304: ANALYSIS USING R 289R> score plot(
Page 305 and 306: ANALYSIS USING R 291R> score plot(
Page 307 and 308: ANALYSIS USING R 293Yuping (CHN) Ha
Page 309 and 310: SUMMARY 295R> biplot(heptathlon_pca
Page 311 and 312: SUMMARY 297rainJuly: rainfall in Ju
Page 313 and 314: Table 17.1:watervoles data. Water v
Page 315 and 316: 302 MULTIDIMENSIONAL SCALINGn in nu
Page 317 and 318: 304 MULTIDIMENSIONAL SCALINGwill be
Page 319 and 320: 306 MULTIDIMENSIONAL SCALINGR> x y
Page 321: 308 MULTIDIMENSIONAL SCALINGR> libr
Page 325 and 326: Table 17.3:eurodist data (package d
Page 327 and 328: CHAPTER 18Cluster Analysis: Classif
Page 329 and 330: INTRODUCTION 317mass, mass), the pe
Page 331 and 332: CLUSTER ANALYSIS 319x 20 2 4 6 8 10
Page 333 and 334: CLUSTER ANALYSIS 32141031678925Figu
Page 335 and 336: CLUSTER ANALYSIS 3231. Find some in
Page 337 and 338: ANALYSIS USING R 32518.3 Analysis U
Page 339 and 340: ANALYSIS USING R 327R> pottery_sing
Page 341 and 342: ANALYSIS USING R 329R> rge planet.
Page 343 and 344: ANALYSIS USING R 331R> plot(planet_
Page 345 and 346: ANALYSIS USING R 333R> scatterplot3
Page 347 and 348: BibliographyAdler, D. and Murdoch,
Page 349 and 350: BIBLIOGRAPHY 337Chambers, J. M. and
Page 351 and 352: BIBLIOGRAPHY 339Everitt, B. S. and
Page 353 and 354: BIBLIOGRAPHY 341Heitjan, D. F. (199
Page 355 and 356: BIBLIOGRAPHY 343Leisch, F. and Ross
Page 357 and 358: BIBLIOGRAPHY 345Proudfoot, J., Gold
Page 359 and 360: BIBLIOGRAPHY 347Stevens, J. (2001),
show all

Preface to First Edition - lib

Create successful ePaper yourself

Delete template?

Save as template?