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| Preface | ||
| Acknowledgments | ||
| 1 | Introducing R and S-Plus | 1 |
| 1.1 | S Basics | 1 |
| 1.2 | An Extended Illustration: Duncan's Occupational-Prestige Regression | 18 |
| 1.3 | S Functions for Basic Statistics | 34 |
| 2 | Reading and Manipulating Data | 37 |
| 2.1 | Data Input | 38 |
| 2.2 | Working with Data Frames | 49 |
| 2.3 | Matrices, Arrays, and Lists | 68 |
| 2.4 | Data Attributes, Modes, and Classes | 78 |
| 3 | Exploring and Transforming Data | 85 |
| 3.1 | Examining Distributions | 86 |
| 3.2 | Examining Relationships | 93 |
| 3.3 | Examining Multivariate Data | 102 |
| 3.4 | Transforming Data | 106 |
| 4 | Fitting Linear Models | 119 |
| 4.1 | Linear Least Squares Regression | 119 |
| 4.2 | Dummy-Variable Regression | 126 |
| 4.3 | Analysis of Variance Models | 136 |
| 4.4 | User-Specified Contrasts | 142 |
| 4.5 | General Linear Hypotheses | 145 |
| 4.6 | Data and Confidence Ellipses | 147 |
| 4.7 | More on lm and Model Formulas | 149 |
| 5 | Fitting Generalized Linear Models | 155 |
| 5.1 | The Structure of Generalized Linear Models | 155 |
| 5.2 | Models for Categorical Responses | 158 |
| 5.3 | Poisson Generalized Linear Models for Count Data | 177 |
| 5.4 | Odds and Ends | 185 |
| 5.5 | Fitting Generalized Linear Models by Iterated Weighted Least Squares | 189 |
| 6 | Diagnosing Problems in Linear and Generalized Linear Models | 191 |
| 6.1 | Unusual Data | 192 |
| 6.2 | Nonnormal Errors | 201 |
| 6.3 | Nonconstant Error Variance | 206 |
| 6.4 | Nonlinearity | 210 |
| 6.5 | Collinearity and Variable Selection | 216 |
| 6.6 | Diagnostics for Generalized Linear Models | 225 |
| 7 | Drawing Graphs | 235 |
| 7.1 | A General Approach to S Graphics | 236 |
| 7.2 | Putting it Together: Effect Displays | 247 |
| 7.3 | Graphics Devices | 255 |
| 8 | Writing Programs | 257 |
| 8.1 | Defining Functions | 258 |
| 8.2 | Working with Matrices | 261 |
| 8.3 | Program Control: Conditionals, Loops, and Recursion | 268 |
| 8.4 | Apply and its Relatives | 278 |
| 8.5 | Object-Oriented Programming in S | 283 |
| 8.6 | Writing S Programs | 292 |
| References | 295 | |
| Index of Data Sets | 299 | |
| Index of Functions, Operators, Control Structures, and Other Symbols | 300 | |
| Author Index | 305 | |
| Subject Index | 306 | |
| About the Author | 312 |
| Price | Condition | Delivery | Seller | Action |
| $99.99 | Digital |
| WonderClub |
Kimberly Shickel
reviewed An R and S-Plus Companion to Applied Regression on February 02, 2016 An R and S-Plus Companion to Applied Regression
Delivers on its promise, no more, no less.
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Add An R and S-Plus Companion to Applied Regression, This book fits right into a needed niche: rigorous enough to give full explanation of the power of the S language, yet accessible enough to assign to social science graduate students without fear of intimidation. It is a tremendous balance of applied , An R and S-Plus Companion to Applied Regression to the inventory that you are selling on WonderClubX
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Add An R and S-Plus Companion to Applied Regression, This book fits right into a needed niche: rigorous enough to give full explanation of the power of the S language, yet accessible enough to assign to social science graduate students without fear of intimidation. It is a tremendous balance of applied , An R and S-Plus Companion to Applied Regression to your collection on WonderClub |