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

Third Edition
September 2018 | 608 pages | SAGE Publications, Inc
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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. 

“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

 
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

Made a good supplement with a heavy emphasis on R.

Mike Minnotte
Mathematics Dept, University Of North Dakota
February 11, 2022
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Key features

NEW TO THIS EDITION: 

  • New coverage of linear and generalized linear mixed-effects models and a new section in the existing chapter on model diagnostics.
  • Increased emphasis on interpreting the results of fitting statistical models to data along with integrated discussion of predictor effect plots as a means of visualizing regression models, particularly models that are difficult to interpret directly from coefficient estimates.
  • A new emphasis on work-flow of data analysis and an explanation on how to use dynamic R Markdown documents in the RStudio interactive development environment to facilitate the process of data analysis and to make it easily reproducible.
  • Focused coverage of R programming is emphasized for performing the tasks that readers are likely to encounter in the process of analyzing data while still providing an introduction to statistical programming.
  • Newer and superior tools that have become available, such as in the treatment of data input and manipulation, are used throughout.

 KEY FEATURES: 

  • Detailed, worked-out examples introduce various facilities of R.
  • The book focuses on how to use R in everyday data analysis.
  • More demanding material is marked with an asterisk so that it may be skipped without loss of continuity.
  • A companion website includes several appendices for various extensions of regression analysis that are not covered in the text, downloadable scripts for all of the examples in the text, and more.

For instructors

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