An R Companion to Applied Regression

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

Table of Contents:
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

About the Author :
John Fox received a BA from the City College of New York and a PhD from the University of Michigan, both in Sociology. He is Professor Emeritus of Sociology at McMaster University in Hamilton, Ontario, Canada, where he was previously the Senator William McMaster Professor of Social Statistics. Prior to coming to McMaster, he was Professor of Sociology, Professor of Mathematics and Statistics, and Coordinator of the Statistical Consulting Service at York University in Toronto. Professor Fox is the author of many articles and books on applied statistics, including \emph{Applied Regression Analysis and Generalized Linear Models, Third Edition} (Sage, 2016). He is an elected member of the R Foundation, an associate editor of the Journal of Statistical Software, a prior editor of R News and its successor the R Journal, and a prior editor of the Sage Quantitative Applications in the Social Sciences monograph series.   Sanford Weisberg is Professor Emeritus of statistics at the University of Minnesota.  He has also served as the director of the University′s Statistical Consulting Service, and has worked with hundreds of social scientists and others on the statistical aspects of their research.  He earned a BA in statistics from the University of California, Berkeley, and a Ph.D., also in statistics, from Harvard University, under the direction of Frederick Mosteller.  The author of more than 60 articles in a variety of areas, his methodology research has primarily been in regression analysis, including graphical methods, diagnostics, and computing.  He is a fellow of the American Statistical Association and former Chair of its Statistical Computing Section.  He is the author or coauthor of several books and monographs, including the widely used textbook Applied Linear Regression, which has been in print for almost forty years.

Review :
"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." "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." "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."