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..". well-written and the discussions are easy to follow ... very useful as a reference book for applied statisticians and would also serve well as a textbook for students graduating in statistics."
--Computational Statistics, April 2009, Vol. 24

"The text is well organized and carefully written ... provides an overview of many modern statistical methodologies and their applications to real data using software. This makes it a useful text for practitioners and graduate students alike."
--Journal of the American Statistical Association, December 2007, Vol. 102, No. 480

"I enjoyed this text as much as [Faraway's Linear Models with R]. The book is recommended as a textbook for a computational statistical and data mining course including GLMs and non-parametric regression, and will also be of great value to the applied statistician whose statistical programming environment of choice is R."
--Journal of Applied Statistics, July 2007, Vol. 34, No. 5

"This is a very pleasant book to read. It clearly demonstrates the different methods available and in which situations each one applies. It covers almost all of the standard topics beyond linear models that a graduate student in statistics should know. It also includes discussion of topics such as model diagnostics, rarely addressed in books of this type. The presentation incorporates an abundance of well-chosen examples ... this book is highly recommended ..."
--Biometrics, December 2006

"The second edition of book 'Extending the linear model with R' by Julian Faraway is an easily readable and relatively thorough (without being theory heavy) sequel of the earlier 'Linear Models with R' by the same author. The book itself is written in a self-paced tutorial style in easily digestible chunks integrating descriptions of underlying methodology, with data analysis and R code. The organization of the book is well thought through. The flow of the book is problem driven rather than driven by the underlying statistical theory . . . the second edition is more polished in terms of the figures used, R code and output display and a crisper typesetting of equations."
--John T. Ormerod, University of Sydney
"It has been a great pleasure to review this book, which delivers both a readily accessible and reader-friendly account of a wide range of statistical models in the context of R software. Since the publication of the very well received first edition of the book, R has considerably expanded both in popularity and in the number of packages available. The second editionof the book takes advantage of the greater functionality available now in R, and substantially revises and adds several new topics."
--Andrzej Galecki, The International Biometric Society

Preface

1. Introduction

2. Binary Response

3. Binomial and Proportion Responses

4. Variations on Logistics Regression

5. Count Regression

6. Contingency Tables

7. Multinomial Data

8. Generalized Linear Models

9. Other GLMs

10. Random Effects

11. Repeated Measures and Longitudinal Data

12. Bayesian Mixed Effect Models

13. Mixed Effect Models for Nonnormal Responses

14. Nonparametric Regression

15. Additive Models

16. Trees

17. Neural Networks


A. Likelihood Theory

B. About R

Bibliography

Index

Start Analyzing a Wide Range of Problems

Since the publication of the bestselling, highly recommended first edition, R has considerably expanded both in popularity and in the number of packages available. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition takes advantage of the greater functionality now available in R and substantially revises and adds several topics.

New to the Second Edition

Expanded coverage of binary and binomial responses, including proportion responses, quasibinomial and beta regression, and applied considerations regarding these models
New sections on Poisson models with dispersion, zero inflated count models, linear discriminant analysis, and sandwich and robust estimation for generalized linear models (GLMs)
Revised chapters on random effects and repeated measures that reflect changes in the lme4 package and show how to perform hypothesis testing for the models using other methods
New chapter on the Bayesian analysis of mixed effect models that illustrates the use of STAN and presents the approximation method of INLA
Revised chapter on generalized linear mixed models to reflect the much richer choice of fitting software now available
Updated coverage of splines and confidence bands in the chapter on nonparametric regression
New material on random forests for regression and classification
Revamped R code throughout, particularly the many plots using the ggplot2 package
Revised and expanded exercises with solutions now included
Demonstrates the Interplay of Theory and Practice

This textbook continues to cover a range of techniques that grow from the linear regression model. It presents three extensions to the linear framework: GLMs, mixed effect models, and nonparametric regression models. The book explains data analysis using real examples and includes all the R commands necessary to reproduce the analyses.