The R book:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Chichester
Wiley
2013
|
| Ausgabe: | 2. ed., 1. publ. |
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025203350&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Literaturangaben |
| Umfang: | XXIV, 1051 S. Ill., graph. Darst. |
| ISBN: | 9780470973929 0470973927 |
Internformat
MARC
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| 020 | |a 9780470973929 |c (hbk.) £60.00 |9 978-0-470-97392-9 | ||
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| 100 | 1 | |a Crawley, Michael J. |d 1949- |e Verfasser |0 (DE-588)114696330 |4 aut | |
| 245 | 1 | 0 | |a The R book |c Michael J. Crawley |
| 250 | |a 2. ed., 1. publ. | ||
| 264 | 1 | |a Chichester |b Wiley |c 2013 | |
| 300 | |a XXIV, 1051 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Literaturangaben | ||
| 650 | 0 | 7 | |a Zeitreihenanalyse |0 (DE-588)4067486-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a R |g Programm |0 (DE-588)4705956-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Statistik |0 (DE-588)4056995-0 |2 gnd |9 rswk-swf |
| 653 | |a R (Computer program language) | ||
| 653 | |a Mathematical statistics--Data processing. | ||
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| 689 | 0 | 1 | |a R |g Programm |0 (DE-588)4705956-4 |D s |
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| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-118-44890-8 |
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-025203350 | |
Datensatz im Suchindex
| DE-BY-TUM_call_number | 0104 MAT 620f 2009 A 2375(2) 0303 MAT 620f 2009 L 291(2) 1002 MAT 620f 2009 B 3433(2) |
|---|---|
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| DE-BY-TUM_location | 01 03 10 |
| DE-BY-TUM_media_number | 040080418260 040008539606 040008539617 040080418248 040080418237 040080418226 040080418259 040080418215 040008669205 |
| _version_ | 1821933290672619520 |
| adam_text | Titel: The R book
Autor: Crawley, Michael J
Jahr: 2013
Detailed Contents
Preface xxiii
1 Getting Started 1
1.1 How to use this book 1
1.1.1 Beginner in both computing and statistics 1
1.1.2 Student needing help with project work 2
1.1.3 Done some R and some statistics, but keen to learn more of both 2
1.1.4 Done regression and ANOVA, but want to learn more advanced statistical
modelling 2
1.1.5 Experienced in statistics, but a beginner in R 2
1.1.6 Experienced in computing, but a beginner in R 2
1.1.7 Familiar with statistics and computing, but need a friendly reference manual 3
1.2 Installing R 3
1.3 Running R 3
1.4 The Comprehensive R Archive Network 4
1.4.1 Manuals 5
1.4.2 Frequently asked questions 5
1.4.3 Contributed documentation 5
1.5 Getting help in R 6
1.5.1 Worked examples of functions 6
1.5.2 Demonstrations of R functions 7
1.6 Packages in R 7
1.6.1 Contents of packages 8
1.6.2 Installing packages 8
1.7 Command line versus scripts 9
1.8 Data editor 9
1.9 Changing the look of the R screen 10
1.10 Good housekeeping 10
1.11 Linking to other computer languages 11
2 Essentials of the R Language 12
2.1 Calculations 13
2.1.1 Complex numbers in R 13
2.1.2 Rounding 14
2.1.3 Arithmetic 16
2.1.4 Modulo and integer quotients 17
DETAILED CONTENTS
2.1.5 Variable names and assignment 18
2.1.6 Operators 19
2.1.7 Integers 19
2.1.8 Factors 20
2.2 Logical operations 22
2.2.1 TRUE and T with FALSE and F 22
2.2.2 Testing for equality with real numbers 23
2.2.3 Equality of floating point numbers using all. equal 23
2.2.4 Summarizing differences between objects using all .equal 24
2.2.5 Evaluation of combinations of TRUE and FALSE 25
2.2.6 Logical arithmetic 25
2.3 Generating sequences 27
2.3.1 Generating repeats 28
2.3.2 Generating factor levels 29
2.4 Membership: Testing and coercing in R 30
2.5 Missing values, infinity and things that are not numbers 32
2.5.1 Missing values: NA 33
2.6 Vectors and subscripts 35
2.6.1 Extracting elements of a vector using subscripts 36
2.6.2 Classes of vector 38
2.6.3 Naming elements within vectors 38
2.6.4 Working with logical subscripts 39
2.7 Vector functions 41
2.7.1 Obtaining tables of means using tappi y 42
2.7.2 The aggregate function for grouped summary statistics 44
2.7.3 Parallel minima and maxima: pmin and pmax 45
2.7.4 Summary information from vectors by groups 46
2.7.5 Addresses within vectors 46
2.7.6 Finding closest values 47
2.7.7 Sorting, ranking and ordering 47
2.7.8 Understanding the difference between unique and duplicated 49
2.7.9 Looking for runs of numbers within vectors 50
2.7.10 Sets: union, intersect and setdiff 52
2.8 Matrices and arrays 53
2.8.1 Matrices 54
2.8.2 Naming the rows and columns of matrices 55
2.8.3 Calculations on rows or columns of the matrix 56
2.8.4 Adding rows and columns to the matrix 58
2.8.5 The sweep function 59
2.8.6 Applying functions with apply, sapply and lapply 61
2.8.7 Using the max. col function 65
2.8.8 Restructuring a multi-dimensional array using aperm 67
2.9 Random numbers, sampling and shuffling 69
2.9.1 The sample function 70
2.10 Loops and repeats 71
2.10.1 Creating the binary representation of a number 73
2.10.2 Loop avoidance 74
DETAILED CONTENTS
2.10.3 The slowness of loops 75
2.10.4 Do not grow data sets by concatenation or recursive function calls 76
2.10.5 Loops for producing time series 77
2.11 Lists 78
2.11.1 Lists and lapply 80
2.11.2 Manipulating and saving lists 82
2.12 Text, character strings and pattern matching 86
2.12.1 Pasting character strings together 87
2.12.2 Extracting parts of strings 88
2.12.3 Counting things within strings 89
2.12.4 Upper-and lower-case text 91
2.12.5 The match function and relational databases 91
2.12.6 Pattern matching 93
2.12.7 Dot. as the anything character 95
2.12.8 Substituting text within character strings 96
2.12.9 Locations of a pattern within a vector using regexpr 97
2.12.10 Using %in% and which 98
2.12.11 More on pattern matching 98
2.12.12 Perl regular expressions 100
2.12.13 Stripping patterned text out of complex strings 100
2.13 Dates and times in R 101
2.13.1 Reading time data from files 102
2.13.2 The strptime function 103
2.13.3 The diff time function 104
2.13.4 Calculations with dates and times 105
2.13.5 The diff time and as .diff time functions 105
2.13.6 Generating sequences of dates 107
2.13.7 Calculating time differences between the rows of a dataframe 109
2.13.8 Regression using dates and times 111
2.13.9 Summary of dates and times in R 113
2.14 Environments 113
2.14.1 Using with rather than attach 113
2.14.2 Using attach in this book 114
2.15 Writing R functions 115
2.15.1 Arithmetic mean of a single sample 115
2.15.2 Median of a single sample 115
2.15.3 Geometric mean 116
2.15.4 Harmonic mean 118
2.15.5 Variance 119
2.15.6 Degrees of freedom 119
2.15.7 Variance ratio test 120
2.15.8 Using variance 121
2.15.9 Deparsing: A graphics function for error bars 123
2.15.10 The switch function 125
2.15.11 The evaluation environment of a function 126
2.15.12 Scope 126
2.15.13 Optional arguments 126
DETAILED CONTENTS
2.15.14 Variable numbers of arguments (... ) 127
2.15.15 Returning values from a function 128
2.15.16 Anonymous functions 129
2.15.17 Flexible handling of arguments to functions 129
2.15.18 Structure of an object: str 130
2.16 Writing from R to file 133
2.16.1 Saving your work 133
2.16.2 Saving history 133
2.16.3 Saving graphics 134
2.16.4 SavingdataproducedwithinRtodi.se 134
2.16.5 Pasting into an Excel spreadsheet 135
2.16.6 Writing an Excel readable file from R 135
2.17 Programming tips 135
3 Data Input 137
3.1 Data input from the keyboard 137
3.2 Data input from files 138
3.2.1 The working directory 138
3.2.2 Data input using read, table 139
3.2.3 Common errors when using read, table 139
3.2.4 Separators and decimal points 140
3.2.5 Data input directly from the web 140
3.3 Input from files using scan 141
3.3.1 Reading a dataframe with scan 141
3.3.2 Input from more complex file structures using scan 143
3.4 Reading data from a file using readLines 145
3.4.1 Input a dataframe using readLines 145
3.4.2 Reading non-standard files using readLines 147
3.5 Warnings when you attach the dataframe 149
3.6 Masking 150
3.7 Input and output formats 150
3.8 Checking files from the command line 151
3.9 Reading dates and times from files 151
3.10 Built-in data files 152
3.11 File paths 152
3.12 Connections 153
3.13 Reading data from an external database 154
3.13.1 Creating the DSN for your computer 155
3.13.2 Setting up R to read from the database 155
4 Dataframes 159
4.1 Subscripts and indices 164
4.2 Selecting rows from the dataframe at random 165
4.3 Sorting dataframes 166
4.4 Using logical conditions to select rows from the dataframe 169
4.5 Omitting rows containing missing values, NA 172
4.5.1 Replacing NAs with zeros 174
4.6 Using order and ¡duplicated to eliminate pseudoreplication 174
DETAILED CONTENTS
4.7 Complex ordering with mixed directions 174
4.8 A dataframe with row names instead of row numbers 176
4.9 Creating a dataframe from another kind of object 177
4.10 Eliminating duplicate rows from a dataframe 180
4.11 Dates in dataframes 180
4.12 Using the match function in dataframes 182
4.13 Merging two dataframes 183
4.14 Adding margins to a dataframe 185
4.15 Summarizing the contents of dataframes 187
S Graphics 189
5.1 Plots with two variables 189
5.2 Plotting with two continuous explanatory variables: Scatterplots 190
5.2.1 Plotting symbols: pch 195
5.2.2 Colour for symbols in plots 196
5.2.3 Adding text to scatterplots 197
5.2.4 Identifying individuals in scatterplots 198
5.2.5 Using a third variable to label a scatterplot 200
5.2.6 Joining the dots 201
5.2.7 Plotting stepped lines 202
5.3 Adding other shapes to a plot 203
5.3.1 Placing items on a plot with the cursor, using the locator function 204
5.3.2 Drawing more complex shapes with polygon 205
5.4 Drawing mathematical functions 206
5.4.1 Adding smooth parametric curves to a scatterplot 207
5.4.2 Fitting non-parametric curves through a scatterplot 209
5.5 Shape and size of the graphics window 211
5.6 Plotting with a categorical explanatory variable 212
5.6.1 Boxplots with notches to indicate significant differences 213
5.6.2 Barplots with error bars 214
5.6.3 Plots for multiple comparisons 217
5.6.4 Using colour palettes with categorical explanatory variables 219
5.7 Plots for single samples 220
5.7.1 Histograms and bar charts 220
5.7.2 Histograms 221
5.7.3 Histograms of integers 224
5.7.4 Overlaying histograms with smooth density functions 225
5.7.5 Density estimation for continuous variables 226
5.7.6 Index plots 227
5.7.7 Time series plots 228
5.7.8 Pie charts 230
5.7.9 The stripchart function 231
5.7.10 A plot to test for normality 232
5.8 Plots with multiple variables 234
5.8.1 The pairs function 234
5.8.2 The coplot function 236
5.8.3 Interaction plots 237
DETAILED CONTENTS
5.9 Special plots 238
5.9.1 Design plots 238
5.9.2 Bubble plots 239
5.9.3 Plots with many identical values 240
5.10 Saving graphics to file 242
5.11 Summary 242
6 Tables 244
6.1 Tables of counts 244
6.2 Summary tables 245
6.3 Expanding a table into a dataframe 250
6.4 Converting from a dataframe to a table 252
6.5 Calculating tables of proportions with prop, table 253
6.6 The scale function 254
6.7 The expand, grid function 254
6.8 The model. matrix function 255
6.9 Comparing table and tabulate 256
7 Mathematics 258
7.1 Mathematical functions 258
7.1.1 Logarithmic functions 259
7.1.2 Trigonometric functions 260
7.1.3 Power laws 261
7.1.4 Polynomial functions 262
7.1.5 Gamma function 264
7.1.6 Asymptotic functions 265
7.1.7 Parameter estimation in asymptotic functions 266
7.1.8 Sigmoid (S-shaped) functions 267
7.1.9 B¡exponential model 269
7.1.10 Transformations of the response and explanatory variables 270
7.2 Probability functions 271
7.3 Continuous probability distributions 272
7.3.1 Normal distribution 274
7.3.2 The central limit theorem 278
7.3.3 Maximum likelihood with the normal distribution 282
7.3.4 Generating random numbers with exact mean and standard deviation 284
7.3.5 Comparing data with a normal distribution 285
7.3.6 Other distributions used in hypothesis testing 286
7.3.7 The chi-squared distribution 287
7.3.8 Fisher s F distribution 289
7.3.9 Student s t distribution 291
7.3.10 The gamma distribution 293
7.3.11 The exponential distribution 296
7.3.12 The beta distribution 296
7.3.13 The Cauchy distribution 298
7.3.14 The lognormal distribution 299
7.3.15 The logistic distribution 300
7.3.16 The log-logistic distribution 301
DETAILED CONTENTS
7.3.17 The Weibull distribution 301
7.3.18 Multivariate normal distribution 303
7.3.19 The uniform distribution 304
7.3.20 Plotting empirical cumulative distribution functions 306
7.4 Discrete probability distributions 307
7.4.1 The Bernoulli distribution 307
7.4.2 The binomial distribution 308
7.4.3 The geometric distribution 311
7.4.4 The hypergeometric distribution 312
7.4.5 The multinomial distribution 313
7.4.6 The Poisson distribution 314
7.4.7 The negative binomial distribution 315
7.4.8 The Wilcoxon rank-sum statistic 322
7.5 Matrix algebra 322
7.5.1 Matrix multiplication 323
7.5.2 Diagonals of matrices 324
7.5.3 Determinant 325
7.5.4 Inverse of a matrix 327
7.5.5 Eigenvalues and eigenvectors 328
7.5.6 Matrices in statistical models 331
7.5.7 Statistical models in matrix notation 334
7.6 Solving systems of linear equations using matrices 338
7.7 Calculus 339
7.7.1 Derivatives 339
7.7.2 Integrals 339
7.7.3 Differential equations 340
Classical Tests 344
8.1 Single samples 344
8.1.1 Data summary 345
8.1.2 Plots for testing normality 346
8.1.3 Testing for normality 347
8.1.4 An example of single-sample data 348
8.2 Bootstrap in hypothesis testing 349
8.3 Skew and kurtosis 350
8.3.1 Skew 350
8.3.2 Kurtosis 352
8.4 Two samples 353
8.4.1 Comparing two variances 354
8.4.2 Comparing two means 358
8.4.3 Student s t test 358
8.4.4 Wilcoxon rank-sum test 361
8.5 Tests on paired samples 362
8.6 The sign test 364
8.7 Binomial test to compare two proportions 365
8.8 Chi-squared contingency tables 365
8.8.1 Pearson s chi-squared 367
8.8.2 G test of contingency 369
DETAILED CONTENTS
8.8.3 Unequal probabilities in the null hypothesis 370
8.8.4 Chi-squared tests on table objects 370
8.8.5 Contingency tables with small expected frequencies: Fisher s exact test 371
8.9 Correlation and covariance 373
8.9.1 Data dredging 375
8.9.2 Partial correlation 375
8.9.3 Correlation and the variance of differences between variables 376
8.9.4 Scale-dependent correlations 377
8.10 Kolmogorov-Smirnov test 379
8.11 Power analysis 382
8.12 Bootstrap 385
9 Statistical Modelling 388
9.1 First things first 389
9.2 Maximum likelihood 390
9.3 The principle of parsimony (Occam s razor) 390
9.4 Types of statistical model 391
9.5 Steps involved in model simplification 393
9.5.1 Caveats 393
9.5.2 Order of deletion 394
9.6 Model formulae in R 395
9.6.1 Interactions between explanatory variables 396
9.6.2 Creating formula objects 397
9.7 Multiple error terms 398
9.8 The intercept as parameter 1 398
9.9 The update function in model simplification 399
9.10 Model formulae for regression 399
9.11 Box-Cox transformations 401
9.12 Model criticism 403
9.13 Model checking 404
9.13.1 Heteroscedasticity 404
9.13.2 Non-normality of errors 405
9.14 Influence 408
9.15 Summary of statistical models in R 411
9.16 Optional arguments in model-fitting functions 412
9.16.1 Subsets 413
9.16.2 Weights 413
9.16.3 Missing values 414
9.16.4 Offsets 415
9.16.5 Dataframes containing the same variable names 415
9.17 Akaike s information criterion 415
9.17.1 AIC as a measure of the fit of a model 416
9.18 Leverage 417
9.19 Misspecified model 418
9.20 Model checking in R 418
9.21 Extracting information from model objects 420
9.21.1 Extracting information by name 421
9.21.2 Extracting information by list subscripts 421
DETAILED CONTENTS
9.21.3 Extracting components of the model using $ 425
9.21.4 Using lists with models 425
9.22 The summary tables for continuous and categorical explanatory variables 426
9.23 Contrasts 430
9.23.1 Contrast coefficients 431
9.23.2 An example of contrasts in R 432
9.23.3 A priori contrasts 433
9.24 Model simplification by stepwise deletion 437
9.25 Comparison of the three kinds of contrasts 440
9.25.1 Treatment contrasts 440
9.25.2 Helmert contrasts 440
9.25.3 Sum contrasts 442
9.26 Aliasing 443
9.27 Orthogonal polynomial contrasts: contr. poly 443
9.28 Summary of statistical modelling 448
10 Regression 449
10.1 Linear regression 450
10.1.1 The famous five in R 453
10.1.2 Corrected sums of squares and sums of products 453
10.1.3 Degree of scatter 456
10.1.4 Analysis of variance in regression: SSY = SSR + SSE 458
10.1.5 Unreliability estimates for the parameters 460
10.1.6 Prediction using the fitted model 462
10.1.7 Model checking 463
10.2 Polynomial approximations to elementary functions 465
10.3 Polynomial regression 466
10.4 Fitting a mechanistic model to data 468
10.5 Linear regression after transformation 469
10.6 Prediction following regression 472
10.7 Testing for lack of fit in a regression 475
10.8 Bootstrap with regression 478
10.9 Jackknife with regression 481
10.10 Jackknife after bootstrap 483
10.11 Serial correlation in the residuals 484
10.12 Piecewise regression 485
10.13 Multiple regression 489
10.13.1 The multiple regression model 490
10.13.2 Common problems arising in multiple regression 497
11 Analysis of Variance 498
11.1 One-way ANOVA 498
11.1.1 Calculations in one-way ANOVA 502
11.1.2 Assumptions of ANOVA 503
11.1.3 A worked example of one-way ANOVA 503
11.1.4 Effect sizes 509
11.1.5 Plots for interpreting one-way ANOVA 511
11.2 Factorial experiments 516
11.3 Pseudoreplication: Nested designs and split plots 519
DETAILED CONTENTS
11.3.1 Split-plot experiments 519
11.3.2 Mixed-effects models 522
11.3.3 Fixed effect or random effect? 523
11.3.4 Removing the pseudoreplication 523
11.3.5 Derived variable analysis 524
11.4 Variance components analysis 524
11.5 Effect sizes in ANOVA: aov or lm? 527
11.6 Multiple comparisons 531
11.7 Multivariate analysis of variance 535
12 Analysis of Covariance 537
12.1 Analysis of covariance in R 538
12.2 ANCOVA and experimental design 548
12.3 ANCOVA with two factors and one continuous covariate 548
12.4 Contrasts and the parameters of ANCOVA models 551
12.5 Order matters in summary, aov 554
13 Generalized Linear Models 557
13.1 Error structure 558
13.2 Linear predictor 559
13.3 Link function 559
13.3.1 Canonical link functions 560
13.4 Proportion data and binomial errors 560
13.5 Count data and Poisson errors 561
13.6 Deviance: Measuring the goodness of fit of a GLM 562
13.7 Quasi-likelihood 562
13.8 The quasi family of models 563
13.9 Generalized additive models 565
13.10 Offsets 566
13.11 Residuals 568
13.11.1 Misspecified error structure 569
13.11.2 Misspecified link function 569
13.12 Overdispersion 570
13.13 Bootstrapping a GLM 570
13.14 Binomial GLM with ordered categorical variables 574
14 Count Data 579
14.1 A regression with Poisson errors 579
14.2 Analysis of deviance with count data 581
14.3 Analysis of covariance with count data 586
14.4 Frequency distributions 588
14.5 Overdispersion in log-linear models 592
14.6 Negative binomial errors 595
15 Count Data in Tables 599
15.1 A two-class table of counts 599
15.2 Sample size for count data 600
15.3 A four-class table of counts 600
15.4 Two-by-two contingency tables 601
15.5 Using log-linear models for simple contingency tables 602
DETAILED CONTENTS
15.6 The danger of contingency tables 604
15.7 Quasi-Poisson and negative binomial models compared 606
15.8 A contingency table of intermediate complexity 608
15.9 Schoener s lizards: A complex contingency table 610
15.10 Plot methods for contingency tables 616
15.11 Graphics for count data: Spine plots and spinograms 621
16 Proportion Data 628
16.1 Analyses of data on one and two proportions 629
16.2 Count data on proportions 629
16.3 Odds 630
16.4 Overdispersion and hypothesis testing 631
16.5 Applications 632
16.5.1 Logistic regression with binomial errors 633
16.5.2 Estimating LD50 and LD90 from bioassay data 635
16.5.3 Proportion data with categorical explanatory variables 636
16.6 Averaging proportions 639
16.7 Summary of modelling with proportion count data 640
16.8 Analysis of covariance with binomial data 640
16.9 Converting complex contingency tables to proportions 643
16.9.1 Analysing Schoener s lizards as proportion data 645
17 Binary Response Variables 650
17.1 Incidence functions 652
17.2 Graphical tests of the fit of the logistic to data 653
17.3 ANCOVA with a binary response variable 655
17.4 Binary response with pseudoreplication 660
18 Generalized Additive Models 666
18.1 Non-parametric smoothers 667
18.2 Generalized additive models 669
18.2.1 Technical aspects 672
18.3 An example with strongly humped data 675
18.4 Generalized additive models with binary data 677
18.5 Three-dimensional graphic output from gam 679
19 Mixed-Effects Models 681
19.1 Replication and pseudoreplication 683
19.2 The Ime and lmer functions 684
19.2.1 Ime 684
19.2.2 lmer 685
19.3 Best linear unbiased predictors 685
19.4 Designed experiments with different spatial scales: Split plots 685
19.5 Hierarchical sampling and variance components analysis 691
19.6 Mixed-effects models with temporal pseudoreplication 695
19.7 Time series analysis in mixed-effects models 699
19.8 Random effects in designed experiments 703
19.9 Regression in mixed-effects models 704
19.10 Generalized linear mixed models 710
19.10.1 Hierarchically structured count data 710
DETAILED CONTENTS
20 Non-Linear Regression 715
20.1 Comparing Michaelis-Menten and asymptotic exponential 719
20.2 Generalized additive models 720
20.3 Grouped data for non-linear estimation 721
20.4 Non-linear time series models (temporal pseudoreplication) 726
20.5 Self-starting functions 728
20.5.1 Self-starting Michaelis-Menten model 729
20.5.2 Self-starting asymptotic exponential model 730
20.5.3 Self-starting logistic 730
20.5.4 Self-starting four-parameter logistic 731
20.5.5 Self-starting Weibull growth function 733
20.5.6 Self-starting first-order compartment function 734
20.6 Bootstrapping a family of non-linear regressions 735
21 Meta-Analysis 740
21.1 Effect size 741
21.2 Weights 741
21.3 Fixed versus random effects 741
21.3.1 Fixed-effect meta-analysis of scaled differences 742
21.3.2 Random effects with a scaled mean difference 746
21.4 Random-effects meta-analysis of binary data 748
22 Bayesian Statistics 752
22.1 Background 754
22.2 A continuous response variable 755
22.3 Normal prior and normal likelihood 755
22.4 Priors 756
22.4.1 Conjugate priors 757
22.5 Bayesian statistics for realistically complicated models 757
22.6 Practical considerations 758
22.7 Writing BUGS models 758
22.8 Packages in R for carrying out Bayesian analysis 758
22.9 Installing JAGS on your computer 759
22.10 Running JAGS in R 759
22.11 MCMC for a simple linear regression 760
22.12 MCMC for a model with temporal pseudoreplication 763
22.13 MCMC for a model with binomial errors 766
23 Tree Models 768
23.1 Background 769
23.2 Regression trees 77I
23.3 Using rpart to fit tree models 772
23.4 Tree models as regressions 775
23.5 Model simplification 776
23.6 Classification trees with categorical explanatory variables 778
23.7 Classification trees for replicated data 780
23.8 Testing for the existence of humps 783
24 Time Series Analysis 785
24.1 Nicholson s blowflies 785
DETAILED CONTENTS
24.2 Moving average 792
24.3 Seasonal data 793
24.3.1 Pattern in the monthly means 796
24.4 Built-in time series functions 797
24.5 Decompositions 797
24.6 Testing for a trend in the time series 798
24.7 Spectral analysis 800
24.8 Multiple time series 801
24.9 Simulated time series 803
24.10 Time series models 805
25 Multivariate Statistics 809
25.1 Principal components analysis 809
25.2 Factor analysis 813
25.3 Cluster analysis 816
25.3.1 Partitioning 816
25.3.2 Taxonomic use of kmeans 817
25.4 Hierarchical cluster analysis 819
25.5 Discriminant analysis 821
25.6 Neural networks 824
26 Spatial Statistics 825
26.1 Point processes 825
26.1.1 Random points in a circle 826
26.2 Nearest neighbours 829
26.2.1 Tessellation 833
26.3 Tests for spatial randomness 834
26.3.1 Ripley s A: 834
26.3.2 Quadrat-based methods 838
26.3.3 Aggregated pattern and quadrat count data 839
26.3.4 Counting things on maps 842
26.4 Packages for spatial statistics 844
26.4.1 The spatstat package 845
26.4.2 The spdep package 849
26.4.3 Polygon lists 854
26.5 Geostatistical data 856
26.6 Regression models with spatially correlated errors: Generalized least squares 860
26.7 Creating a dot-distribution map from a relational database 867
27 Survival Analysis 869
27.1 A Monte Carlo experiment 869
27.2 Background 872
27.3 The survivor function 873
27.4 The density function 873
27.5 The hazard function 874
27.6 The exponential distribution 874
27.6.1 Density function 874
27.6.2 Survivor function 874
27.6.3 Hazard function 874
DETAILED CONTENTS
27.7 Kaplan-Meier survival distributions 875
27.8 Age-specific hazard models 876
27.9 Survival analysis in R 878
27.9.1 Parametric models 878
27.9.2 Cox proportional hazards model 878
27.9.3 Cox s proportional hazard or a parametric model? 879
27.10 Parametric analysis 879
27.11 Cox s proportional hazards 882
27.12 Models with censoring 883
27.12.1 Parametric models 884
27.12.2 Comparing coxph and survreg survival analysis 887
28 Simulation Models 893
28.1 Temporal dynamics: Chaotic dynamics in population size 893
28.1.1 Investigating the route to chaos 895
28.2 Temporal and spatial dynamics: A simulated random walk in two dimensions 896
28.3 Spatial simulation models 897
28.3.1 Metapopulation dynamics 898
28.3.2 Coexistence resulting from spatially explicit (local) density dependence 900
28.4 Pattern generation resulting from dynamic interactions 903
29 Changing the Look of Graphics 907
29.1 Graphs for publication 907
29.2 Colour 908
29.2.1 Palettes for groups of colours 910
29.2.2 The RColorBrewer package 913
29.2.3 Coloured plotting symbols with contrasting margins 914
29.2.4 Colour in legends 915
29.2.5 Background colours 916
29.2.6 Foreground colours 917
29.2.7 Different colours and font styles for different parts of the graph 917
29.2.8 Full control of colours in plots 918
29.3 Cross-hatching 920
29.4 Grey scale 921
29.5 Coloured convex hulls and other polygons 921
29.6 Logarithmic axes 922
29.7 Different font families for text 923
29.8 Mathematical and other symbols on plots 924
29.9 Phase planes 928
29.10 Fat arrows 929
29.11 Three-dimensional plots 930
29.12 Complex 3D plots with wireframe 933
29.13 An alphabetical tour of the graphics parameters 935
29.13.1 Text justification, ad j 935
29.13.2 Annotation of graphs, ann 935
29.13.3 Delay moving on to the next in a series of plots, ask 935
29.13.4 Control over the axes, axis 938
29.13.5 Backgroundcolourforplots.bg 939
DETAILED CONTENTS
29.13.6 Boxes around plots, bty 939
29.13.7 Size of plotting symbols using the character expansion function, cex 940
29.13.8 Changing the shape of the plotting region, pit 941
29.13.9 Locating multiple graphs in non-standard layouts using fig 942
29.13.10 Two graphs with a common x scale but different v scales using f ig 942
29.13.11 The layout function 943
29.13.12 Creating and controlling multiple screens on a single device 945
29.13.13 Orientation of numbers on the tick marks, las 947
29.13.14 Shapes for the ends and joins of lines, lend and 1 join 947
29.13.15 Line types, lty 948
29.13.16 Line widths, lwd 949
29.13.17 Several graphs on the same page, mf row and mf col 950
29.13.18 Margins around the plotting area, mar 950
29.13.19 Plotting more than one graph on the same axes, new 951
29.13.20 Two graphs on the same plot with different scales for their v axes 951
29.13.21 Outer margins, orna 952
29.13.22 Packing graphs closer together 954
29.13.23 Square plotting region, pty 955
29.13.24 Character rotation, srt 955
29.13.25 Rotating the axis labels 955
29.13.26 Tick marks on the axes 956
29.13.27 Axis styles 957
29.14 Trellis graphics 957
29.14.1 Panel box-and-whisker plots 959
29.14.2 Panel scatterplots 960
29.14.3 Panel barplots 965
29.14.4 Panels for conditioning plots 966
29.14.5 Panel histograms 967
29.14.6 Effect sizes 968
29.14.7 More panel functions 969
References and Further Reading 971
Index 977
|
| any_adam_object | 1 |
| author | Crawley, Michael J. 1949- |
| author_GND | (DE-588)114696330 |
| author_facet | Crawley, Michael J. 1949- |
| author_role | aut |
| author_sort | Crawley, Michael J. 1949- |
| author_variant | m j c mj mjc |
| building | Verbundindex |
| bvnumber | BV040349275 |
| classification_rvk | QH 237 SK 850 ST 250 ST 601 WC 7000 |
| classification_tum | MAT 634f DAT 368f MAT 620f |
| ctrlnum | (OCoLC)798968363 (DE-599)OBVAC08939551 |
| dewey-full | 519.502855133 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.502855133 |
| dewey-search | 519.502855133 |
| dewey-sort | 3519.502855133 |
| dewey-tens | 510 - Mathematics |
| discipline | Biologie Informatik Mathematik Wirtschaftswissenschaften |
| edition | 2. ed., 1. publ. |
| format | Book |
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| id | DE-604.BV040349275 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T16:13:07Z |
| institution | BVB |
| isbn | 9780470973929 0470973927 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025203350 |
| oclc_num | 798968363 |
| open_access_boolean | |
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| physical | XXIV, 1051 S. Ill., graph. Darst. |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | Wiley |
| record_format | marc |
| spellingShingle | Crawley, Michael J. 1949- The R book Zeitreihenanalyse (DE-588)4067486-1 gnd R Programm (DE-588)4705956-4 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4067486-1 (DE-588)4705956-4 (DE-588)4056995-0 |
| title | The R book |
| title_auth | The R book |
| title_exact_search | The R book |
| title_full | The R book Michael J. Crawley |
| title_fullStr | The R book Michael J. Crawley |
| title_full_unstemmed | The R book Michael J. Crawley |
| title_short | The R book |
| title_sort | the r book |
| topic | Zeitreihenanalyse (DE-588)4067486-1 gnd R Programm (DE-588)4705956-4 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Zeitreihenanalyse R Programm Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025203350&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT crawleymichaelj therbook |
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Teilbibliothek Mathematik & Informatik
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