This guide offers a practical introduction to Generalized Additive Models (GAMs) using R, designed for individuals without a strong statistical background. It emphasizes hands-on learning by implementing basic principles in R, rather than focusing on extensive mathematical theory.
Introduction to GAMs with R
This resource provides a practical handbook for understanding and applying Generalized Additive Models (GAMs) using the R programming language. It prioritizes practical application over deep mathematical theory, making it accessible to a wider audience, including those without a strong statistical background.
Multiple Linear Regression Review
The guide begins with a review of multiple linear regression, using data from studies on human crania size and ambient light levels. This foundational knowledge is crucial for understanding additive models.
Introduction to Additive Models
Additive models are introduced using deep-sea fishery data, illustrating the application of these models in real-world scenarios. This section builds upon the understanding of multiple linear regression established in the previous chapter.
Smoothing Techniques
Simple linear regression techniques are used to program a smoother, demonstrated with research on pelagic bioluminescent organisms. This section introduces the concept of smoothing, a key component of GAMs.
Generalized Additive Models (GAMs) in Detail
The deep-sea fishery study is revisited to delve into generalized additive models. This chapter provides a detailed discussion of GAMs and their application.
Case Studies with Different Distributions
The guide presents detailed case studies illustrating the application of Gaussian, Poisson, negative binomial, zero-inflated Poisson, and binomial generalized additive models.
Gaussian GAMs
Gaussian GAMs are explored using seabird studies, demonstrating the application of GAMs to continuous data.
Poisson GAMs
Poisson GAMs are applied to squid studies, illustrating the use of GAMs for count data.
Negative Binomial GAMs
Negative binomial GAMs are used in fish parasite studies, showcasing the application of GAMs to overdispersed count data.
Zero-Inflated Poisson GAMs
The application of Zero-Inflated Poisson GAMs are explored, providing insights into handling count data with excess zeros.
Binomial GAMs
Binomial GAMs are presented, illustrating how GAMs can be used for proportion data.
Alt: Fish with visible parasites, used in a study applying negative binomial GAMs.
Data sets and R code
All datasets utilized in the book are available as .txt files. By right-clicking on the file, you can easily download and save the data for practice. The corresponding R code is also available for each chapter.
Support File
HighstatLibV8.R is a support file designed to enhance the functionality of R for statistical analysis. It provides additional functions and tools that complement the standard R library, making it easier to perform complex statistical tasks.
Errata
A current errata list is available for the book, ensuring the accuracy and reliability of the information presented.
Support Material for Beginner’s Guide to GLM and GLMM with R
Chapter 1 of Beginner’s Guide to Generalized Additive Models with R (2012) is available as free access to readers of some of our books. This chapter provides an introduction to multiple linear regression, a foundational knowledge for Beginner’s Guide to GLM and GLMM with R.
Conclusion
This guide offers a comprehensive and practical introduction to GAMs with R, focusing on real-world applications and hands-on learning. By working through the examples and case studies, readers can gain a solid understanding of GAMs and their application in various fields. This resource provides the tools and knowledge necessary to begin applying GAMs to your own data analysis projects.
