Extending the Linear Model with R

by Julian James Faraway

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.

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

Features
-> Provides readers with an up-to-date, well-stocked toolbox of statistical methodologies
-> Includes numerous real examples that illustrate the use of R for data analysis
-> Covers GLM diagnostics, generalized linear mixed models, trees, and the use of neural networks in statistics
-> Reviews linear models as well as the basics of using R
-> Offers the datasets and other material on the author’s website

Genres: Mathematics, Reference, Nonfiction

414 pages, Hardcover

First published December 20, 2005

Reviews

[Shelby Gonzalez · Rating 3 out of 5]

Read for my stats class on linear regression

[Jacek · Rating 4 out of 5]

The contents are nothing but amazing, but the editing quality is very poor, some formulas are painful to read. Hence 4 stars instead of 5.

[Duncan McKinnon · Rating 4 out of 5]

A great continuation of the textbook on linear models. Would have never realized the intracies and vast array of models and methods available for interpreting relationships and predicting responses. I think the audience would benefit also from a more philosophical interpretation of the methods and whether they are meant to be used in inferential or predictive approaches. The coverage of a wide range of modeling tools and statistical topics definitely makes this book a worthwhile read.