2nd Edition

Extending the Linear Model with R Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition

By Julian J. Faraway Copyright 2016
    414 Pages 115 B/W Illustrations
    by Chapman & Hall

    413 Pages 115 B/W Illustrations
    by Chapman & Hall

    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.


    Binary Response
    Heart Disease Example
    Logistic Regression
    Model Selection
    Goodness of Fit
    Estimation Problems

    Binomial and Proportion Responses
    Binomial Regression Model
    Pearson’s χ2 Statistic
    Beta Regression

    Variations on Logistic Regression
    Latent Variables
    Link Functions
    Prospective and Retrospective Sampling
    Prediction and Effective Doses
    Matched Case-Control Studies

    Count Regression
    Poisson Regression
    Dispersed Poisson Model
    Rate Models
    Negative Binomial
    Zero Inflated Count Models

    Contingency Tables
    Two-by-Two Tables
    Larger Two-Way Tables
    Correspondence Analysis
    Matched Pairs
    Three-Way Contingency Tables
    Ordinal Variables

    Multinomial Data
    Multinomial Logit Model
    Linear Discriminant Analysis
    Hierarchical or Nested Responses
    Ordinal Multinomial Responses

    Generalized Linear Models
    GLM Definition
    Fitting a GLM
    Hypothesis Tests
    GLM Diagnostics
    Sandwich Estimation
    Robust Estimation

    Other GLMs
    Gamma GLM
    Inverse Gaussian GLM
    Joint Modeling of the Mean and Dispersion
    Quasi-Likelihood GLM
    Tweedie GLM

    Random Effects
    Estimating Random Effects
    Blocks as Random Effects
    Split Plots
    Nested Effects
    Crossed Effects
    Multilevel Models

    Repeated Measures and Longitudinal Data
    Longitudinal Data
    Repeated Measures
    Multiple Response Multilevel Models

    Bayesian Mixed Effect Models

    Mixed Effect Models for Nonnormal Responses
    Generalized Linear Mixed Models
    Binary Response
    Count Response
    Generalized Estimating Equations

    Nonparametric Regression
    Kernel Estimators
    Local Polynomials
    Confidence Bands
    Discussion of Methods
    Multivariate Predictors

    Additive Models
    Modeling Ozone Concentration
    Additive Models Using mgcv
    Generalized Additive Models
    Alternating Conditional Expectations
    Additivity and Variance Stabilization
    Generalized Additive Mixed Models
    Multivariate Adaptive Regression Splines

    Regression Trees
    Tree Pruning
    Random Forests
    Classification Trees
    Classification Using Forests

    Neural Networks
    Statistical Models as NNs
    Feed-Forward Neural Network with One Hidden Layer
    NN Application

    Appendix A: Likelihood Theory
    Appendix B: About R




    Julian J. Faraway is a professor of statistics in the Department of Mathematical Sciences at the University of Bath. His research focuses on the analysis of functional and shape data with particular application to the modeling of human motion. He earned a PhD in statistics from the University of California, Berkeley.

    "What I liked most with this book was the comprehensive treatment of the practical application of GLMs, covering most outcomes an applied statistician will encounter, and at the same time presenting just enough of the necessary theoretical basis for the discussed methods. Combined with the thorough discussion of the R output, the text will serve as a useful guide for the reader when applying the methods to his or her own data set."
    Psychometrika, 2018

    "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

    Praise for the First Edition:
    "… 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

    "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