An R companion to applied regression:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Los Angeles
Sage
[2019]
|
| Ausgabe: | 3. edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | xxx, 577 Seiten Diagramme, Karten |
| ISBN: | 9781544336473 |
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| 240 | 1 | 0 | |a An R and S-Plus companion to applied regression |
| 245 | 1 | 0 | |a An R companion to applied regression |c John Fox, Sanford Weisberg |
| 250 | |a 3. edition | ||
| 264 | 1 | |a Los Angeles |b Sage |c [2019] | |
| 264 | 4 | |c © 2019 | |
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Datensatz im Suchindex
| _version_ | 1804178744626118656 |
|---|---|
| adam_text | Contents Preface What Is R? Obtaining and Installing R andRStudio Installing R on a Windows System Installing RonamacOS System Installing RStudio InstaUing and Using R Packages Optional: Customizing R Optional: Installing ΚΓρΧ Using This Book Chapter Synopses Typographical Conventions New in the Third Edition The Website for the R Companion Beyond the R Companion Acknowledgments About the Authors 1 Getting Started With R and RStudio 1.1 Projects in RStudio 1.2 R Basics 1.2.1 Interacting With R Through theConsole 1.2.2 Editing R Commands in the Console 1.2.3 R Functions 1.2.4 Vectors and Variables 1.2.5 Nonnumeric Vectors 1.2.6 Indexing Vectors 1.2.7 User-Defined Functions 1.3 Fixing Errors and Getting Help 1.3.1 When Things Go Wrong 1.3.2 Getting Help and Information 1.4 Organizing Your Work in R and RStud ¡0 and Making It Repro ducible 1.4.1 Using the RStudio Editor With R Script Files 1.4.2 Writing R Markdown Documents 1.5 An Extended Illustration: Duncan’s Occupational-Prestige Re gression 1.5.1 Examining the Data 1.5.2 Regression Analysis 1.5.3 Regression Diagnostics xiii xiv xvi xvii xvii xviii xx xxii xxiii xxiii xxiv xxv xxvi xxvii xxviii xxviii xxx 1 2 5 5 7 7 11 14 16 18 21 21 23 25 25 28 33 36 39 40 v
Vi ■ CONTENTS 1.6 1.7 2 3 CONTENTS R Functions for Basic Statistics Generic Functions and Their Methods* 47 47 Reading and Manipulating Data 2.1 Data Input 2.1.1 Accessing Data From a Package 2.1.2 Entering a Data Frame Directly 2.1.3 Reading Data From Plain-Text Files 2.1.4 Files and Paths 2.1.5 Exporting or Saving a Data Frame to a File 2.1.6 Reading and Writing Other FileFormats 2.2 Other Approaches to Reading and Managing Data Sets in R 2.3 Working With Data Frames 2.3.1 How the R Interpreter Finds Objects 2.3.2 Missing Data 2.3.3 Modifying and Transforming Data 2.3.4 Binding Rows and Columns 2.3.5 Aggregating Data Frames 2.3.6 Merging Data Frames 2.3.7 Reshaping Data 2.4 Working With Matrices, Arrays, and Lists 2.4.1 Matrices 2.4.2 Arrays 2.4.3 Lists 2.4.4 Indexing 2.5 Dates and Times 2.6 Character Data 2.7 Large Data Sets in R* 2.7.1 How Large Is “Large”? 2.7.2 Reading and Saving Large Data Sets 2.8 Complementary Reading and References 53 54 54 56 59 63 65 66 67 69 69 72 79 86 87 89 91 95 96 97 98 99 107 110 117 118 120 122 Exploring and Transforming Data 3.1 Examining Distributions 3.1.1 Histograms 3.1.2 Density Estimation 3.1.3 Quantile-Comparison Plots 3.1.4 Boxplots 3.2 Examining Relationships 3.2.1 Scatterplots 3.2.2 Parallel Boxplots 3.2.3 More on the plot ( ) Function 3.3 Examining Multivariate Data 3.3.1 Three-Dimensional Plots 3.3.2 Scatterplot Matrices 3.4 Transforming Data 123 124 124 128 130 133 134 134 141 144 145 145 146 148 3.5 3.6 3.7 3.4.1 Logarithms: The Champion of Transformations 3.4.2 Power Transformations 3.4.3 Transformations and Exploratory
Data Analysis 3.4.4 Transforming Restricted-Range Variables 3.4.5 Other Transformations Point Labeling and Identification 3.5.1 The identify ( ) Function 3.5.2 Automatic Point Labeling Scatterplot Smoothing Complementary Reading and References 4 Fitting Linear Models 4.1 The Linear Model 4.2 Linear Least-Squares Regression 4.2.1 Simple Linear Regression 4.2.2 Multiple Linear Regression 4.2.3 Standardized Regression Coefficients 4.3 Predictor Effect Plots 4.4 Polynomial Regression and Regression Splines 4.4.1 Polynomial Regression 4.4.2 Regression Splines* 4.5 Factors in Linear Models 4.5.1 A Linear Model With One Factor: One-Way Analysis of Variance 4.5.2 Additive Models With Numeric Predictors and Factors 4.6 Linear Models With Interactions 4.6.1 Interactions Between Numeric Predictors and Factors 4.6.2 Shortcuts for Writing Linear-Model Formulas 4.6.3 Multiple Factors 4.6.4 Interactions Between Numeric Predictors* 4.7 More on Factors 4.7.1 Dummy Coding 4.7.2 Other Factor Codings 4.7.3 Ordered Factors and Orthogonal-Polynomial Contrasts 4.7.4 User-Specified Contrasts* 4.7.5 Suppressing the Intercept in a Model With Factors* 4.8 Too Many Regressors* 4.9 The Arguments of the lm ( ) Function 4.9.1 4.9.2 4.9.3 4.9.4 4.9.5 4.9.6 4.9.7 4.9.8 formula data subset weights na. action method, model, x, y, qr* singular, ok* contrasts 148 154 162 167 168 169 169 170 171 172 173 174 176 176 183 185 187 190 190 194 197 201 205 207 207 213 214 222 224 224 224 227 230 231 232 235 235 238 238 239 239 240 240 240 ■ vii
viii ■ CONTENTS CONTENTS 4.9.9 offset 4.10 5 6 Complementary Reading and References Coefficient Standard Errors, Confidence Intervals, and Hypothesis Tests 5.1 Coefficient Standard Errors 5.1.1 Conventional Standard Errors of Least-Squares Regres sion Coefficients 5.1.2 Robust Regression Coefficient Standard Errors 5.1.3 Using the Bootstrap to Compute Standard Errors 5.1.4 The Delta Method for Standard Errors ofNonlinear Func tions* 5.2 Confidence Intervals 5.2.1 Wald Confidence Intervals 5.2.2 Bootstrap Confidence Intervals 5.2.3 Confidence Regions and Data Ellipses* 5.3 Testing Hypotheses About Regression Coefficients 5.3.1 Wald Tests 5.3.2 Likelihood-Ratio Tests and the Analysis of Variance 5.3.3 Sequential Analysis of Variance 5.3.4 The Anovat) Function 5.3.5 Testing General Linear Hypotheses* 5.4 Complementary Reading and References Fitting Generalized Linear Models 6.1 Review of the Structure of GLMs 6.2 The glm ( ) Function in R 6.3 GLMs for Binary Response Data 6.3.1 Example: Women’s Labor Force Participation 6.3.2 Example: Volunteering for a Psychological Experiment 6.3.3 Predictor Effect Plots for Logistic Regression 6.3.4 Analysis of Deviance and Hypothesis Tests for Logistic Regression 6.3.5 Fitted and Predicted Values 6.4 Binomial Data 6.5 Poisson GLMs for Count Data 6.6 Loglinear Models for Contingency Tables 6.6.1 Two-Dimensional Tables 6.6.2 Three-Dimensional Tables 6.6.3 Sampling Plans for Loglinear Models 6.6.4 Response Variables 6.7 Multinomial Response Data 6.8 Nested Dichotomies 6.9 The Proportional-Odds Model 6.9.1 Testing for Proportional Odds 6.10
Extensions 6.10.1 More on the Anova ( ) Function 24ο 241 243 244 6.11 244 246 248 252 254 254 255 256 258 258 259 260 262 267 270 271 272 276 276 278 282 283 285 289 289 296 301 301 304 306 307 309 314 317 319 322 322 6.12 6.13 6.10.2 Gamma Models 6.10.3 Quasi-Likelihood Estimation 6.10.4 Overdispersed Binomial and Poisson Models Arguments to glm ( ) 323 325 326 330 6.11.1 weights 6.11.2 start, etastart, mustárt 6.11.3 offset 6.11.4 control 6.11.5 model, method, x, y 331 Fitting GLMs by Iterated Weighted Least Squares* Complementary Reading and References 332 333 331 331 332 332 7 Fitting Mixed-Effects Models 7.1 Background: The Linear Model Revisited 7.1.1 The Linear Model in Matrix Form* 7.2 Linear Mixed-Effects Models 7.2.1 Matrix Form of the Linear Mixed-Effects Model* 7.2.2 An Application to Hierarchical Data 7.2.3 Wald Tests for Linear Mixed-Effects Models 7.2.4 Examining the Random Effects: Computing BLUPs 7.2.5 An Application to Longitudinal Data 7.2.6 Modeling the Errors 7.2.7 Sandwich Standard Errors for Least-Squares Estimates 7.3 Generalized Linear Mixed Models 7.3.1 Matrix Form of the GLMM* 7.3.2 Example: Minneapolis Police Stops 7.4 Complementary Reading 335 336 336 336 338 339 357 358 360 371 373 375 376 377 382 8 Regression Diagnostics for Linear, Generalized Linear, and Mixed-Effects Models 8.1 Residuals 8.2 Basic Diagnostic Plots 8.2.1 Plotting Residuals 8.2.2 Marginal-Model Plots 8.2.3 Added-Variable Plots 8.2.4 Marginal-Conditional Plots 8.3 Unusual Data 8.3.1 Outliers and Studentized Residuals 8.3.2 Leverage: Hat-Values 8.3.3 Influence Measures 8.4
Transformations After Fitting a Regression Model 8.4.1 Transforming the Response 8.4.2 Predictor Transformations 8.5 Nonconstant Error Variance 8.5.1 Testing for Nonconstant Error Variance 8.6 Diagnostics for Generalized Linear Models 385 386 388 388 391 392 395 396 397 398 399 405 406 410 414 416 417 ■ ÍX
x ■ CONTENTS CONTENTS 8.6.1 8.6.2 8.6.3 8.7 8.8 8.9 8.10 9 Residuals and Residual Plots Influence Measures Graphical Methods: Added-Variable Plots, ComponentPlus-Residual Plots, and Effect Plots With Partial Resid uals Diagnostics for Mixed-Effects Models 8.7.1 Mixed-Model Component-Plus-Residual Plots 8.7.2 Influence Diagnostics for Mixed Models Collinearity and Variance Inflation Factors Additional Regression Diagnostics Complementary Reading and References 418 421 422 425 425 428 429 434 435 Drawing Graphs 437 A General Approach to R Graphics 9.1 438 9.1.1 Defining a Coordinate System: plot () 439 9.1.2 Graphics Parameters: par ( ) 441 9.1.3 Adding Graphical Elements: axis () .points (), lines (), text ( ), et al. 442 9.1.4 Specifying Colors 452 Putting It Together: Explaining Local Linear Regression 9.2 454 9.2.1 Finer Control Over Plot Layout 461 Other R Graphics Packages 9.3 467 9.3.1 The lattice Package 467 9.3.2 The ggplot2 Package 469 9.3.3 Maps 472 9.3.4 Other Notable Graphics Packages 475 9.4 Complementary Reading and References 476 10 An Introduction to R Programming 10.1 Why Learn to Program in R? 10.2 Defining Functions: Preliminary Examples 10.2.1 Lagging a Variable 10.2.2 Creating an Influence Plot 10.3 Working With Matrices* 10.3.1 Basic Matrix Arithmetic 10.3.2 Matrix Inversion and the Solution of Linear Simultane ous Equations 10.3.3 Example: Linear Least-Squares Regression 10.3.4 Eigenvalues and Eigenvectors 10.3.5 Miscellaneous Matrix Computations 10.4 Program Control With Conditionals, Loops, and Recursion 10.4.1 Conditionals 10.4.2 Iteration
(Looping) 10.4.3 Recursion 10.5 Avoiding Loops: apply () and Its Relatives 10.5.1 To Loop or Not to Loop? 477 478 479 479 482 486 486 488 489 491 491 492 492 495 498 499 505 10.6 509 509 515 516 522 527 533 536 537 References 539 Subject Index 551 Data Set Index 567 Package Index 569 Index of Functions and Operators 571 Optimization Problems* 10.6.1 Zero-Inflated Poisson Regression Monte-Carlo Simulations* 10.7 10.7.1 Testing Regression Models Using Simulation 10.8 Debugging R Code* 10.9 Object-Oriented Programming in R* 10.10 Writing Statistical-Modeling Functions in R* 10.11 Organizing Code for R Functions 10.12 Complementary Reading and References ■ ХІ
R COMPANION о APPLIED REGRESSION An An R Companion to Applied Regression is a broad introduction to the R statistical computing environment in the context of applied regression analysis. John Fox and Sanford Weisberg provide a stepby-step guide to using the free statistical software R, an emphasis on integrating statistical computing in R with the practice of data analysis, coverage of generalized linear models, and substantial webbased support materials. The Third Edition includes a new chapter on mixed-effects models, new and updated data sets, and a de-emphasis on statistical programming, while retaining a general introduction to basic R programming.
|
| any_adam_object | 1 |
| author | Fox, John 1947- Weisberg, Sanford 1947- |
| author_GND | (DE-588)132520346 (DE-588)170214664 |
| author_facet | Fox, John 1947- Weisberg, Sanford 1947- |
| author_role | aut aut |
| author_sort | Fox, John 1947- |
| author_variant | j f jf s w sw |
| building | Verbundindex |
| bvnumber | BV045106915 |
| classification_rvk | SK 840 MR 2200 ST 601 QH 234 ST 250 CM 4000 |
| ctrlnum | (OCoLC)1076331094 (DE-599)BVBBV045106915 |
| discipline | Informatik Soziologie Psychologie Mathematik Wirtschaftswissenschaften |
| edition | 3. edition |
| format | Book |
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| id | DE-604.BV045106915 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:08:50Z |
| institution | BVB |
| isbn | 9781544336473 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030497357 |
| oclc_num | 1076331094 |
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| physical | xxx, 577 Seiten Diagramme, Karten |
| publishDate | 2019 |
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| publisher | Sage |
| record_format | marc |
| spelling | Fox, John 1947- Verfasser (DE-588)132520346 aut An R and S-Plus companion to applied regression An R companion to applied regression John Fox, Sanford Weisberg 3. edition Los Angeles Sage [2019] © 2019 xxx, 577 Seiten Diagramme, Karten txt rdacontent n rdamedia nc rdacarrier Fernsehen SPlus (DE-588)108720500X gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf S-PLUS (DE-588)4321162-8 gnd rswk-swf R Programm (DE-588)4705956-4 s Regressionsanalyse (DE-588)4129903-6 s Fernsehen SPlus (DE-588)108720500X b DE-604 S-PLUS (DE-588)4321162-8 s 1\p DE-604 Weisberg, Sanford 1947- Verfasser (DE-588)170214664 aut Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030497357&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030497357&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Fox, John 1947- Weisberg, Sanford 1947- An R companion to applied regression Fernsehen SPlus (DE-588)108720500X gnd R Programm (DE-588)4705956-4 gnd Regressionsanalyse (DE-588)4129903-6 gnd S-PLUS (DE-588)4321162-8 gnd |
| subject_GND | (DE-588)108720500X (DE-588)4705956-4 (DE-588)4129903-6 (DE-588)4321162-8 |
| title | An R companion to applied regression |
| title_alt | An R and S-Plus companion to applied regression |
| title_auth | An R companion to applied regression |
| title_exact_search | An R companion to applied regression |
| title_full | An R companion to applied regression John Fox, Sanford Weisberg |
| title_fullStr | An R companion to applied regression John Fox, Sanford Weisberg |
| title_full_unstemmed | An R companion to applied regression John Fox, Sanford Weisberg |
| title_short | An R companion to applied regression |
| title_sort | an r companion to applied regression |
| topic | Fernsehen SPlus (DE-588)108720500X gnd R Programm (DE-588)4705956-4 gnd Regressionsanalyse (DE-588)4129903-6 gnd S-PLUS (DE-588)4321162-8 gnd |
| topic_facet | Fernsehen SPlus R Programm Regressionsanalyse S-PLUS |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030497357&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030497357&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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