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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.



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