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

by Julian James Faraway

Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies.
Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. A supporting Web site at www.stat.lsa.umich.edu/ faraway/ELM holds all of the data described in the book.
Statisticians need to be familiar with a broad range of ideas and techniques. This book provides a well-stocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught.

Genres: Reference, Nonfiction, Mathematics

312 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.