Generalized Additive Models: An Introduction with R (2nd Edition)

By Simon N. Wood

Generalized Additive Models: An Introduction with R, Second Edition is the authoritative introduction to generalized additive models (GAMs) for applied statistical modeling. Published by CRC Press / Chapman & Hall, this book explains how GAMs extend generalized linear models by allowing flexible, data-driven smooth functions.

Written by the creator of the widely used mgcv R package, the book emphasizes practical modeling, interpretation, and diagnostics using real datasets. It is a cornerstone text for statisticians, data scientists, and applied researchers working with nonlinear relationships.

What This Book Does

This book teaches how to build, estimate, interpret, and validate generalized additive models using R, enabling flexible regression analysis for complex data.

Key Features of the 2nd Edition

• Clear introduction to GAM theory and smoothing methods
• Hands-on modeling using the mgcv package in R
• Coverage of penalized regression splines and smoothing parameter selection
• Model checking, diagnostics, and visualization
• Examples with real-world datasets
• Widely used reference for applied GAM modeling

Who Should Use This Book?

• Statistics and data science students
• Applied statisticians and data analysts
• Ecologists, epidemiologists, and social scientists
• Researchers using R for nonlinear regression
• Graduate courses in statistical modeling

Why It’s Essential

• The definitive applied introduction to GAMs
• Written by the developer of mgcv
• Balances theory, implementation, and interpretation
• Trusted reference across applied sciences

The essential guide to flexible regression with generalized additive models.
Order today from BooksGoat and master GAMs with R.

Product Details

• ISBN-13: 9781498728331
• Edition: 2nd
• Author: Simon N. Wood
• Publisher: CRC Press / Chapman & Hall
• Format: Paperback
• Condition: New
• Availability: In Stock
• Shipping: Free Shipping

Table of Contents (Highlights)

• Introduction to Generalized Additive Models
• Smoothing and Penalized Regression Splines
• Fitting GAMs with mgcv
• Model Selection and Inference
• Diagnostics and Model Checking
• Extensions and Advanced Topics
• Applications to Real Data

FAQs

• Is this book suitable for beginners? Yes, it is written as an accessible introduction for applied users with basic regression knowledge.
• Does it require R programming? Yes, examples are implemented in R using the mgcv package.
• Is it useful beyond statistics? Yes, it is widely used in ecology, epidemiology, and social sciences.