.

0 (hodnocen0 x )

(1) Půjčeno:2x 

BK

2nd pub.

Cambridge : Cambridge University Press, 2007

xxiii, 502, [12] s. obr. příl. : il., grafy ; 26 s.

ISBN 978-0-521-86116-8 (váz.)

Cambridge series in statistical and probabilistic mathematics ; 10

Obsahuje bibliografii na s. [474]-484, bibliografické odkazy a rejstříky

Data - analýza - metody grafické - R (jazyk) - učebnice vysokošk.

000006937

Preface page xix // 1 A brief introduction to R 1 // 1.1 An overview of R 1 // 1.1.1 A short R session 1 // 1.1.2 The uses of R 5 // 1.1.3 Online help 6 // 1.1.4 Further steps in learning R 8 // 1.2 Data input, packages and the search list 8 // 1.2.1 Reading data from a file 8 // 1.2.2 R packages 9 // 1.3 Vectors, factors and univariate time series 10 // 1.3.1 Vectors in R 10 // 1.3.2 Concatenation - joining vector objects 10 // 1.3.3 Subsets of vectors 11 // 1.3.4 Patterned data 11 // 1.3.5 Missing values 12 // 1.3.6 Factors 13 // 1.3.7 Time series 14 // 1.4 Data frames and matrices 14 // 1.4.1 The attaching of data frames 16 // 1.4.2 Aggregation, stacking and unstacking 17 // 1.4.3. Data frames and matrices 17 // 1.5 Functions, operators and loops 18 // 1.5.1 Built-in functions 18 // 1.5.2 Generic functions and the class of an object 20 // 1.5.3 User-written functions 21 // 1.5.4 Relational and logical operators and operations 22 // 1.5.5 Selection and matching 23 // 1.5.6 Functions for working with missing values 23 // 1.5.7. Looping 24 // 1.6 Graphics in R 24 // 1.6.1 The function plot ( ) and allied functions 25 // 1.6.2 The use of color 27 // 1.6.3 The importance of aspect ratio 27 // 1.6.4 Dimensions and other settings for graphics devices 28 // 1.6.5 The plotting of expressions and mathematical symbols 28 // 1.6.6 Identification and location on the figure region 29 // 1.6.7 Plot methods for objects other than vectors 29 // 1.6.8 Lattice graphics versus base graphics - xyplot () versus plot () 30 // 1.6.9 Further information on graphics 30 // 1.6.10 Good and bad graphs 30 // 1.7 Lattice (trellis) graphics 31 // 1.8 Additional points on the use of R 33 // 1.9 Recap 36 // 1.10 Further reading 36 // 1.10.1 References for further reading 37 // 1.11 Exercises 37 // 2 Styles of data analysis 43 // 2.1 Revealing views of the data 43 //

2.1.1 Views of a single sample 44 // 2.1.2 Patterns in univariate time series 48 // 2.1.3 Patterns in bivariate data 50 // 2.1.4 Patterns in grouped data 52 // 2.1.5. Multiple variables and times 54 // 2.1.6 Scatterplots, broken down by multiple factors 56 // 2.1.7 What to look for in plots 58 // 2.2 Data summary 59 // 2.2.1 Counts 60 // 2.2.2 Summaries of information from data frames 63 // 2.2.3 Standard deviation and inter-quartile range 66 // 2.2.4 Correlation 68 // 2.3 Statistical analysis questions, aims and strategies 69 // 2.3.1 How relevant and how reliable are the data? 70 // 2.3.2 Helpful and unhelpful questions 70 // 2.3.3 How will results be used? 71 // 2.3.4 Formal and informal assessments 72 // 2.3.5 Statistical analysis strategies 73 // 2.3.6 Planning the formal analysis 73 // 2.3.7 Changes to the intended plan of analysis 74 // 2.4 Recap 74 // 2.5 Further reading 75 // 2.5.1 References for further reading 75 // 2.6 Exercises 75 // 3 Statistical models // 3.1 Regularities // 3.1.1 Deterministic models // 3.1.2 Models that include a random component 79 // 3.1.3 Fitting models - the model formula 82 // 3.2 Distributions: models for the random component 83 // 3.2.1 Discrete distributions 84 // 3.2.2 Continuous distributions 86 // 3.3 The uses of random numbers 88 // 3.3.1 Simulation 88 // 3.3.2 Sampling from populations 89 // 3.4 Model assumptions 90 // 3.4.1 Random sampling assumptions - independence 91 // 3.4.2 Checks for normality 92 // 3.4.3 Checking other model assumptions 95 // 3.4.4 Are non-parametric methods the answer? 95 // 3.4.5 Why models matter - adding across contingency tables 95 // 3.5 Recap 96 // 3.6 Further reading 97 // 3.6.1 References for further reading 97 // 3.7 Exercises 97 // 4 An introduction to formal inference 101 // 4.1 Basic concepts of estimation 101 // 4.1.1 Population parameters and sample statistics 101 //

4.1.2 Sampling distributions 102 // 4.1.3 Assessing accuracy - the standard error 102 // 4.1.4 The standard error for the difference of means 103 // 4.1.5. The standard error of the median 104 // 4.1.6 The sampling distribution of the r-statistic 104 // 4.2 Confidence intervals and hypothesis tests 107 // 4.2.1 One-and two-sample intervals and tests for means 107 // 4.2.2 Confidence intervals and tests for proportions 113 // 4.2.3 Confidence intervals for the correlation 113 // 4.2.4 Confidence intervals versus hypothesis tests 114 // 4.3 Contingency tables 115 // 4.3.1 Rare and endangered plant species 117 // 4.3.2 Additional notes 119 // 4.4 One-way unstructured comparisons 120 // 4.4.1 Displaying means for the one-way layout 123 // 4.4.2 Multiple comparisons 124 // 4.4.3 Data with a two-way structure, that is, two factors 125 // 4.4.4 Presentation issues 126 // 4.5 Response curves 126 // 4.6 Data with a nested variation structure 127 // 4.6.1 Degrees of freedom considerations 128 // 4.6.2 General multi-way analysis of variance designs 129 // 4.7 Resampling methods for standard errors, tests and confidence // intervals 129 // 4.7.1 The one-sample permutation test 129 // 4.7.2 The two-sample permutation test 130 // 4.7.3. Estimating the standard error of the median: bootstrapping 131 // 4.7.4 Bootstrap estimates of confidence intervals 133 // 4.8. Theories of inference 134 // 4.8.1 Maximum likelihood estimation 135 // 4.8.2 Bayesian estimation 136 // 4.8.3 If there is strong prior information, use it! 136 // 4.9 Recap 137 // 4.10 Further reading 138 // 4.10.1 References for further reading 138 // 4.11 Exercises 139 // 5 Regression with a single predictor 144 // 5.1 Fitting a line to data 144 // 5.1.1 Lawn roller example 145 // 5.1.2 Calculating fitted values and residuals 146 // 5.1.3 Residual plots 147 //

5.1.4 Iron slag example: is there a pattern in the residuals? 148 // 5.1.5 The analysis of variance table 150 // 5.2 Outliers, influence and robust regression 151 // 5.3 Standard errors and confidence intervals 153 // 5.3.1 Confidence intervals and tests for the slope 153 // 5.3.2 SEs and confidence intervals for predicted values 154 // 5.3.3. Implications for design 155 // 5.4 Regression versus qualitative anova comparisons 157 // 5.4.1 Issues of power 157 // 5.4.2 The pattern of change 158 // 5.5 Assessing predictive accuracy 158 // 5.5.1 Training/test sets and cross-validation 158 // 5.5.2 Cross-validation - an example 159 // 5.5.3. Bootstrapping 161 // 5.6. A note on power transformations 164 // 5.6.1. General power transformations 164 // 5.7 Size and shape data 165 // 5.7.1 Allometric growth 166 // 5.7.2 There are two regression lines! 167 // 5.8 The model matrix in regression 168 // 5.9 Recap 169 // 5.10 Methodological references 170 // 5.11 Exercises 17 0 // 6 Multiple linear regression 173 // 6.1 Basic ideas: book weight and brain weight examples 173 // 6.1.1 Omission of the intercept term 176 // 6.1.2 Diagnostic plots 176 // 6.1.3 Example: brain weight 178 // 6.1.4 Plots that show the contribution of individual terms 180 // 6.2 Multiple regression assumptions and diagnostics 182 // 6.2.1 Influential outliers and Cook’s distance 183 // 6.2.2 Influence on the regression coefficients 184 // 6.2.3. Additional diagnostic plots 185 // 6.2.4 Robust and resistant methods 185 // 6.2.5 The uses of model diagnostics 185 // 6.3 A strategy for fitting multiple regression models 186 // 6.3.1 Preliminaries 186 // 6.3.2 Model fitting 187 // 6.3.3 An example - the Scottish hill race data 187 // 6.4 Measures for the assessment and comparison of regression // models 193 // 6.4.1 R1 and adjusted R2 193 // 6.4.2 AIC and related statistics 194 //

6.4.3 How accurately does the equation predict? 194 // 6.5 Interpreting regression coefficients 196 // 6.5.1 Book dimensions and book weight 196 // 6.6 Problems with many explanatory variables 199 // 6.6.1 Variable selection issues 200 // 6.7 Multicollinearity 202 // 6.7.1 A contrived example 202 // 6.7.2 The variance inflation factor 206 // 6.7.3 Remedies for multicollinearity 206 // 6.8 Multiple regression models - additional points 207 // 6.8.1 Errors in x 207 // 6.8.2 Confusion between explanatory and response variables 210 // 6.8.3 Missing explanatory variables 210 // 6.8.4. The use of transformations 212 // 6.8.5. Non-linear methods - an alternative to transformation? 212 // 6.9 Recap 214 // 6.10 Further reading 214 // 6.10.1 References for further reading 215 // 6.11 Exercises 216 // 7 Exploiting the linear model framework 219 // 7.1 Levels of a factor - using indicator variables 220 // 7.1.1 Example - sugar weight 220 // 7.1.2 Different choices for the model matrix when there are factors 223 // 7.2 Block designs and balanced incomplete block designs 224 // 7.2.1 Analysis of the rice data, allowing for block effects 224 // 7.2.2 A balanced incomplete block design 226 // 7.3 Fitting multiple lines 227 // 7.4 Polynomial regression 231 // 7.4.1 Issues in the choice of model 233 // 7.5. Methods for passing smooth curves through data 234 // 7.5.1 Scatterplot smoothing - regression splines 235 // 1.5.2. Penalized splines and generalized additive models 239 // 7.5.3 Other smoothing methods 239 // 7.6 Smoothing terms in additive models 241 // 7.6. Đ“ The fitting of penalized spline terms 243 // 7.7 Further reading 243 // 7.7.1 References for further reading 243 // 7.8 Exercises 243 // 8 Generalized linear models and survival analysis 246 // 8.1 Generalized linear models 246 // 8.1.1 Transformation of the expected value on the left 246 //

8.1.2 Noise terms need not be normal 247 // 8.1.3 Log odds in contingency tables 247 // 8.1.4 Logistic regression with a continuous explanatory variable 248 // 8.2 Logistic multiple regression 251 // 8.2.1 Selection of model terms and fitting the model 253 // 8.2.2 A plot of contributions of explanatory variables 256 // 8.2.3 Cross-validation estimates of predictive accuracy 257 // 8.3 Logistic models for categorical data - an example 258 // 8.4 Poisson and quasi-Poisson regression 260 // 8.4.1 Data on aberrant crypt foci 260 // 8.4.2 Moth habitat example 263 // 8.5 Additional notes on generalized linear models 269 // 8.5.1. Residuals, and estimating the dispersion 269 // 8.5.2 Standard errors and z- or /-statistics for binomial models 270 // 8.5.3 Leverage for binomial models 270 // 8.6 Models with an ordered categorical or categorical response 271 // 8.6.1 Ordinal regression models 271 // 8.6.2. Loglinear models 274 // 8.7 Survival analysis 275 // 8.7.1 Analysis of the Aids2 data 276 // 8.7.2 Right censoring prior to the termination of the study 278 // 8.7.3 The survival curve for male homosexuals 279 // 8.7.4 Hazard rates 279 // 8.7.5 The Cox proportional hazards model 280 // 8.8 Transformations for count data 282 // 8.9 Further reading 283 // 8.9.1 References for further reading 283 // 8.10 Exercises 284 // 9 Time series models 286 // 9.1 Time series - some basic ideas 286 // 9.1.1 Preliminary graphical explorations 286 // 9.1.2 The autocorrelation function 287 // 9.1.3 Autoregressive models 288 // 9.1.4. Autoregressive moving average models - theory 290 // 9.2. Regression modeling with moving average errors 291 // 9.3. Non-linear time series 297 // 9.4 Other time series packages 298 // 9.5 Further reading 298 // 9.5.1 Spatial statistics 299 // 9.5.2 References for further reading 299 // 9.6 Exercises 299 // 10 Multi-level models and repeated measures 301 //

10.1 A one-way random effects model 302 // 10.1.1 Analysis with aov () 303 // 10.1.2 A more formal approach 306 // 10.1.3 Analysis using lmer () 308 // 10.2 Survey data, with clustering 311 // 10.2.1 Alternative models 311 // 10.2.2 Instructive, though faulty, analyses 316 // 10.2.3 Predictive accuracy 317 // 10.3 A multi-level experimental design 317 // 10.3.1 The anova table 319 // 10.3.2 Expected values of mean squares 320 // 10.3.3. The sums of squares breakdown 321 // 10.3.4 The variance components 324 // 10.3.5 The mixed model analysis 325 // 10.3.6 Predictive accuracy 327 // 10.3.7 Different sources of variance - complication or focus of interest? 327 // 10.4 Within- and between-subject effects 328 // 10.4.1 Model selection 329 // 10.4.2 Estimates of model parameters 330 // 10.5 Repeated measures in time 332 // 10.5.1 Example - random variation between profiles 334 // 10.5.2 Orthodontic measurements on children 339 // 10.6 Error structure considerations 343 // 10.6.1 Predictions from models with a complex error // structure 343 // 10.6.2 Error structure in explanatory variables 344 // 10.7 Further notes on multi-level and other models with correlated // errors 344 // 10.7.1 An historical perspective on multi-level models 344 // 10.7.2 Meta-analysis 346 // 10.7.3 Functional data analysis 346 // 10.8 Recap 346 // 10.9 Further reading 347 // 10.9.1 References for further reading 347 // 10.10 Exercises 348 // 11 Tree-based classification and regression 350 // 11.1 The uses of tree-based methods 351 // 11.1.1 Problems for which tree-based regression may be used 351 // 11.2 Detecting email spam - an example 352 // 11.2.1 Choosing the number of splits 355 // 11.3 Terminology and methodology 355 // 11.3.1 Choosing the split - regression trees 355 // 11.3.2 Within and between sums of squares 356 // 11.3.3 Choosing the split - classification trees 357 //

11.3.4 Tree-based regression versus loess regression smoothing 358 // 11.4 Predictive accuracy and the cost-complexity tradeoff 360 // 11.4.1 Cross-validation 361 // 11.4.2 The cost-complexity parameter 361 // 11.4.3 Prediction error versus tree size 362 // 11.5 Data for female heart attack patients 363 // 11.5.1 The one-standard-deviation rule 365 // 11.5.2 Printed information on each split 365 // 11.6 Detecting email spam - the optimal tree 366 // 11.7 The randomForest package 368 // 11.8 Additional notes on tree-based methods 371 // 11.8.1 The combining of tree-based methods with other approaches 371 // 11.8.2 Models with a complex error structure 372 // 11.8.3 Pruning as variable selection 372 // 11.8.4 Other types of tree 372 // 11.8.5 Factors as predictors 372 // 11.8.6 Summary of pluses and minuses of tree-based methods 372 // 11.9 Further reading 373 // 11.9.1 References for further reading 373 // 11.10 Exercises 374 // 12 Multivariate data exploration and discrimination 375 // 12.1 Multivariate exploratory data analysis 376 // 12.1.1 Scatterplot matrices 376 // 12.1.2 Principal components analysis 377 // 12.1.3 Multi-dimensional scaling 383 // 12.2 Discriminant analysis 384 // 12.2.1 Example - plant architecture 384 // 12.2.2 Logistic discriminant analysis 386 // 12.2.3 Linear discriminant analysis 387 // 12.2.4 An example with more than two groups 388 // 12.3. High-dimensional data, classification and plots 390 // 12.3.1 Classifications and associated graphs 392 // 12.3.2 Flawed graphs 393 // 12.3.3 Accuracies and scores for test data 397 // 12.3.4 Graphs derived from the cross-validation process 403 // 12.4 Further reading 405 // 12.4.1 References for further reading 406 // 12.5 Exercises 406 // 13 Regression on principal component or discriminant scores 408 // 13.1 Principal component scores in regression 408 //

13.2. Propensity scores in regression comparisons - labor // training data 412 // 13.2.1 Regression analysis, using all covariates 415 // 13.2.2 The use of propensity scores 417 // 13.3 Further reading 419 // 13.3.1 References for further reading 419 // 13.4 Exercises 420 // 14 The R system - additional topics 421 // 14.1 Working directories, workspaces and the search list 421 // 14.1.1. The search path 421 // 14.1.2 Workspace management 421 // 14.1.3 Utility functions 423 // 14.2 Data input and output 423 // 14.2.1 Input of data 424 // 14.2.2 Data output 428 // 14.3 Functions and operators - some further details 429 // 14.3.1 Function arguments 430 // 14.3.2 Character string and vector functions 431 // 14.3.3 Anonymous functions 431 // 14.3.4 Functions for working with dates (and times) 432 // 14.3.5 Creating groups 433 // 14.3.6 Logical operators 434 // 14.4 Factors 434 // 14.5 Missing values 437 // 14.6. Matrices and arrays 439 // 14.6.1 Matrix arithmetic 440 // 14.6.2 Outer products 441 // 14.6.3 Arrays 442 // 14.7 Manipulations with lists, data frames and matrices 443 // 14.7.1 Lists - an extension of the notion of "vector" 443 // 14.7.2 Changing the shape of data frames 445 // 14.7.3. Merging data frames - merge () 445 // 14.7.4 Joining data frames, matrices and vectors - cbind () 446 // 14.7.5 The apply family of functions 446 // 14.7.6 Splitting vectors and data frames into lists - split () 448 // 14.7.7 Multivariate time series 448 // 14.8 Classes and methods 449 // 14.8.1 Printing and summarizing model objects 449 // 14.8.2 Extracting information from model objects 450 // 14.8.3 S4 classes and methods 450 // 14.9 Manipulation of language constructs 451 // 14.9.1 Model and graphics formulae 451 // 14.9.2 The use of a list to pass parameter values 452 // 14.9.3 Expressions 453 // 14.9.4 Environments 453 //

14.9.5 Function environments and lazy evaluation 455 // 14.10 Document preparation - Sweave () 456 // 14.11 Graphs in R 457 // 14.11.1 Hardcopy graphics devices 457 // 14.11.2 Multiple graphs on a single graphics page 457 // 14.11.3 Plotting characters, symbols, line types and colors 457 // 14.12 Lattice graphics and the grid package 462 // 14.12.1 Interaction with plots 464 // 14.12.2. Use of grid, text () to label points 464 // 14.12.3. Multiple lattice graphs on a graphics page 465 // 14.13 Further reading 466 // 14.13.1 Vignettes 466 // 14.13.2 References for further reading 466 // 14.14 Exercises 467 // Epilogue - models 470 // References 474 // Index of R Symbols and Functions 485 // Index of Terms 491 // Index of Authors 501 // Color Plates after Page 502