A practical guide to data analysis using R: an example-based approach
Using diverse real-world examples, this text examines what models used for data analysis mean in a specific research context. What assumptions underlie analyses, and how can you check them? Building on the successful 'Data Analysis and Graphics Using R,' 3rd edition (Cambridge, 2010), it e...
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| Hauptverfasser: | , , |
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| Format: | Buch |
| Sprache: | English |
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Cambridge
Cambridge University Press
2024
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| Ausgabe: | First published |
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| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | Using diverse real-world examples, this text examines what models used for data analysis mean in a specific research context. What assumptions underlie analyses, and how can you check them? Building on the successful 'Data Analysis and Graphics Using R,' 3rd edition (Cambridge, 2010), it expands upon topics including cluster analysis, exponential time series, matching, seasonality, and resampling approaches. An extended look at p-values leads to an exploration of replicability issues and of contexts where numerous p-values exist, including gene expression. Developing practical intuition, this book assists scientists in the analysis of their own data, and familiarizes students in statistical theory with practical data analysis. The worked examples and accompanying commentary teach readers to recognize when a method works and, more importantly, when it doesn't. Each chapter contains copious exercises. Selected solutions, notes, slides, and R code are available online, with extensive references pointing to detailed guides to R. |
| Beschreibung: | xxiii, 526 Seiten Diagramme |
| ISBN: | 9781009282277 |
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Contents List of Figures Preface 1 Learning from Data, and Tools for the Task Questions, and Data That May Point to Answers Graphical Tools for Data Exploration Data Summary Distributions: Quantifying Uncertainty Simple Forms of Regression Model Data-Based Judgments - Frequentist, in a Bayesian World Information Statistics and Bayesian Methods with Bayes Factors 1.8 Resampling Methods for SEs, Tests, and Confidence Intervals 1.9 Organizing and Managing Work, and Tools That Can Assist 1.10 The Changing Environment forData Analysis 1.11 Further, or Supplementary,Reading 1.12 Exercises 1.1 1.2 1.3 1.4 1.5 1.6 1.7 2 Generalizing from Models 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12 3 Model Assumptions -Statistics, Binomial Proportions, and Correlations Extra-Binomial and Extra-Poisson Variation Contingency Tables Issues for Regression with a Single Explanatory Variable Empirical Assessment of Predictive Accuracy One- and Two-Way Comparisons Data with a Nested Variation Structure Bayesian Estimation - Further Commentary and Approaches Recap Further Reading Exercises Multiple Linear Regression 3.1 3.2 Basic Ideas: the allbacks Book Weight Data The Interpretation of Model Coefficients page xi xvii 1 2 12 22 30 42 48 58 66 70 72 79 80 88 88 91 95 100 104 116 121 130 131 136 137 137 144 144 148
Contents viii 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 Choosing the Model, and Checking It Out Robust Regression, Outliers, and Influence Assessment and Comparison of Regression Models Problems with Many Explanatory Variables Errors in X Multiple Regression Models - Additional Points Recap Further Reading Exercises 161 171 176 183 191 195 201 202 203 Exploiting the Linear Model Framework 4.1 Levels of a Factor - Using Indicator Variables 4.2 Block Designs and Balanced Incomplete Block Designs Fitting Multiple Lines 4.3 Methods for Fitting Smooth Curves 4.4 4.5 * Quantile Regression Further Reading and Remarks 4.6 Exercises 4.7 208 209 213 216 219 238 240 240 Generalized Linear Models, and Survival Analysis Generalized Linear Models 5.1 5.2 Logistic Multiple Regression Logistic Models for Categorical Data - an Example 5.3 5.4 Models for Counts - Poisson, Quasipoisson, and Negative 245 245 250 260 5.5 5.6 5.7 5.8 5.9 5.10 5.11 Binomial Fitting Smooths Additional Notes on Generalized Linear Models Models with an Ordered Categorical or Categorical Response Survival Analysis Transformations for Proportions and Counts Further Reading Exercises 261 274 276 278 281 288 289 290 Time Series Models Time Series - Some Basic Ideas 6.1 6.2 Regression Modeling with ARIMA Errors 6.3 * Nonlinear Time Series 6.4 Further Reading Exercises 6.5 292 293 304 313 314 315 Multilevel Models, and Repeated Measures 7.1 Corn Yield Data - Analysis Using aov() Analysis Using lme4: :lmer() 7.2 Survey Data, with Clustering 7.3 A Multilevel Experimental Design 7.4 Within- and Between-Subject Effects 7.5 A
Mixed Model with a Betabinomial Error 7.6 318 320 325 329 335 344 349
Contents 7.7 7.8 7.9 7.10 7.11 7.12 Observation-Level Random Effects - the moths Dataset Repeated Measures in Time Further Notes on Multilevel Models Recap Further Reading Exercises ix 356 357 367 371 371 371 8 Tree-Based Classification and Regression 8.1 Tree-Based Methods - Uses and Basic Notions 8.2 Splitting Criteria, with Illustrative Examples 8.3 The Practicalities of Tree Construction - Two Examples 8.4 From One Tree to a Forest - a More Global Optimality 8.5 Additional Notes - One Tree, or Many Trees? 8.6 Further Reading and Extensions 8.7 Exercises 373 374 378 384 390 393 395 396 9 Multivariate Data Exploration and Discrimination 9.1 Multivariate Exploratory Data Analysis 9.2 Principal Component Scores in Regression 9.3 Cluster Analysis 9.4 Discriminant Analysis 9.5 *High-Dimensional Data - RNA-Seq Gene Expression 9.6 High-Dimensional Data from Expression Arrays 9.7 Balance and Matching - Causal Inference from Observational Data 9.8 Multiple Imputation 9.9 Further Reading 9.10 Exercises 400 401 408 412 422 429 433 Epilogue Appendix A.l A.2 A.3 A.4 A.5 A.6 A The R System: a Brief Overview Getting Started with R R Data Structures Functions and Operators Calculations with Matrices, Arrays, Lists, and Data Frames Brief Notes on R Graphics Packages and Functions Plotting Characters, Symbols, Line Types, and Colors References References to R Packages Index of R Functions Index of Terms 443 457 462 463 467 469 469 473 483 487 490 493 495 508 514 519 |
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| spelling | Maindonald, John H. 1937- Verfasser (DE-588)141432918 aut A practical guide to data analysis using R an example-based approach John H. Maindonald, W. John Braun, Jeffrey L. Andrews First published Cambridge Cambridge University Press 2024 xxiii, 526 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Using diverse real-world examples, this text examines what models used for data analysis mean in a specific research context. What assumptions underlie analyses, and how can you check them? Building on the successful 'Data Analysis and Graphics Using R,' 3rd edition (Cambridge, 2010), it expands upon topics including cluster analysis, exponential time series, matching, seasonality, and resampling approaches. An extended look at p-values leads to an exploration of replicability issues and of contexts where numerous p-values exist, including gene expression. Developing practical intuition, this book assists scientists in the analysis of their own data, and familiarizes students in statistical theory with practical data analysis. The worked examples and accompanying commentary teach readers to recognize when a method works and, more importantly, when it doesn't. Each chapter contains copious exercises. Selected solutions, notes, slides, and R code are available online, with extensive references pointing to detailed guides to R. Histogramm (DE-588)4159984-6 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Deskriptive Statistik (DE-588)4070313-7 gnd rswk-swf Statistics / Data processing Statistics / Graphic methods / Data processing R (Computer program language) Datenanalyse (DE-588)4123037-1 s Deskriptive Statistik (DE-588)4070313-7 s Histogramm (DE-588)4159984-6 s R Programm (DE-588)4705956-4 s Statistik (DE-588)4056995-0 s DE-604 Braun, W. John 1963- Verfasser (DE-588)13396440X aut Andrews, Jeffrey L. Verfasser aut 9781009282277 Erscheint auch als Online-Ausgabe 978-1-009-28228-4 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=035128516&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Maindonald, John H. 1937- Braun, W. John 1963- Andrews, Jeffrey L. A practical guide to data analysis using R an example-based approach Histogramm (DE-588)4159984-6 gnd R Programm (DE-588)4705956-4 gnd Datenanalyse (DE-588)4123037-1 gnd Statistik (DE-588)4056995-0 gnd Deskriptive Statistik (DE-588)4070313-7 gnd |
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| title | A practical guide to data analysis using R an example-based approach |
| title_auth | A practical guide to data analysis using R an example-based approach |
| title_exact_search | A practical guide to data analysis using R an example-based approach |
| title_full | A practical guide to data analysis using R an example-based approach John H. Maindonald, W. John Braun, Jeffrey L. Andrews |
| title_fullStr | A practical guide to data analysis using R an example-based approach John H. Maindonald, W. John Braun, Jeffrey L. Andrews |
| title_full_unstemmed | A practical guide to data analysis using R an example-based approach John H. Maindonald, W. John Braun, Jeffrey L. Andrews |
| title_short | A practical guide to data analysis using R |
| title_sort | a practical guide to data analysis using r an example based approach |
| title_sub | an example-based approach |
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