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An R Companion to Applied Regression
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An R Companion to Applied Regression

Third Edition


December 2018 | 608 pages | SAGE Publications, Inc
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 step-by-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 web-based support materials.

The Third Edition has been reorganized and 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. The authors have substantially updated both the car and effects packages for R for this edition, introducing additional capabilities and making the software more consistent and easier to use. They also advocate an everyday data-analysis workflow that encourages reproducible research. To this end, they provide coverage of RStudio, an interactive development environment for R that allows readers to organize and document their work in a simple and intuitive fashion, and then easily share their results with others. Also included is coverage of R Markdown, showing how to create documents that mix R commands with explanatory text. 

 
1. Getting Started with R and RStudio
Projects in RStudio  
R Basics  
Fixing Errors and Getting Help  
Organizing Your Work in R and RStudio  
An Extended Illustration  
R Functions for Basic Statistics  
Generic Functions and Their Methods*  
 
2. Reading and Manipulating Data
Data Input  
Managing Data  
Working With Data Frames  
Matrices, Arrays, and Lists  
Dates and Times  
Character Data  
Large Data Sets in R*  
Complementary Reading and References  
 
3. Exploring and Transforming Data
Examining Distributions  
Examining Relationships  
Examining Multivariate Data  
Transforming Data  
Point Labeling and Identication  
Scatterplot Smoothing  
Complementary Reading and References  
 
4. Fitting Linear Models
The Linear Model  
Linear Least-Squares Regression  
Predictor Effect Plots  
Polynomial Regression and Regression Splines  
Factors in Linear Models  
Linear Models with Interactions  
More on Factors  
Too Many Regressors*  
The Arguments of the lm Function  
Complementary Reading and References  
 
5. Standard Errors, Confidence Intervals, Tests
Coefficient Standard Errors  
Confidence Intervals  
Testing Hypotheses About Regression Coefficients  
Complementary Reading and References  
 
6. Fitting Generalized Linear Models
The Structure of GLMs  
The glm() Function in R  
GLMs for Binary-Response Data  
Binomial Data  
Poisson GLMs for Count Data  
Loglinear Models for Contingency Tables  
Multinomial Response Data  
Nested Dichotomies  
The Proportional-Odds Model  
Extensions  
Arguments to glm()  
Fitting GLMs by Iterated Weighted Least-Squares*  
Complementary Reading and References  
 
7. Fitting Mixed-Effects Models
Background: The Linear Model Revisited  
Linear Mixed-Effects Models  
Generalized Linear Mixed Models  
Complementary Reading  
 
8. Regression Diagnostics
Residuals  
Basic Diagnostic Plots  
Unusual Data  
Transformations After Fitting a Regression Model  
Non-Constant Error Variance  
Diagnostics for Generalized Linear Models  
Diagnostics for Mixed-Effects Models  
Collinearity and Variance-Inflation Factors  
Additional Regression Diagnostics  
Complementary Reading and References  
 
9. Drawing Graphs
A General Approach to R Graphics  
Putting It Together: Local Linear Regression  
Other R Graphics Packages  
Complementary Reading and References  
 
10. An Introduction to R Programming
Why Learn to Program in R?  
Defining Functions: Preliminary Examples  
Working With Matrices*  
Conditionals, Loops, and Recursion  
Avoiding Loops  
Optimization Problems*  
Monte-Carlo Simulations*  
Debugging R Code*  
Object-Oriented Programming in R*  
Writing Statistical-Modeling Functions in R*  
Organizing Code for R Functions  
Complementary Reading and References  

Supplements

Student Study Site

An accompanying website for the book found at study.sagepub.com/RCompanion provides:

  • R scripts for examples by chapter
  • Data files used in the book
  • The car package (Companion to Applied Regression), an accompanying software for regression diagnostics and other regression-related tasks
  • Other resources to help students get the most out of the text

An R Companion to Applied Regression continues to provide the most comprehensive and user-friendly guide to estimating, interpreting, and presenting results from regression models in R.”

Christopher Hare
University of California, Davis

“This is the best book I’ve read for teaching the modern practice of regression. By going deeply into both R and applied regression, it manages to use each topic to motivate and illustrate the other. The whole is much greater than sum of the parts because each thread so effectively reinforces the other. There are many nice surprises in this new edition.  R Studio and markdown are used to encourage a reproducible workflow. There’s an excellent and accessible chapter on mixed and longitudinal data that expands the reach of regression methods to the much more complex data structures typical of current practice. Like its predecessors, this edition is a model of clear, thoughtful exposition. It’s an outstanding contribution to the teaching and practice of regression.”

Georges Monette
York University

“This is an impressive update to a book I have long admired. The authors have brought the description of how to do data analysis and plots of Applied Regression related data to a modern and more comprehensive level.”

Michael Friendly
York University

Sample Materials & Chapters

Ch. 1: Getting Started with R and Rstudio

Ch. 8: Regression Diagnostics


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ISBN: 9781544336473
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