Generalized Additive Models: An Introduction with R
Abstract
The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation. It is self-contained, providing the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before presenting a balanced treatment of the theory and applications of GAMs and related models. The author bases his approach on a framework of penalized regression splines, and while firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of R software helps explain the theory and illustrates the practical application of the methodology. Each chapter contains an extensive set of exercises, with solutions in an appendix or in the book’s R data package gamair, to enable use as a course text or for self-study.
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Analyzing the Time Course of Pupillometric Data
van Rij, J., Hendriks, P., van Rijn, H., Baayen, R. H., & Wood, S. N. (2019) · Trends in Hearing · doi:10.1177/2331216519832483 · Cited by 220 · see citing works
Self-citationCOMPONENTS OF UNCERTAINTY IN SPECIES DISTRIBUTION ANALYSIS: A CASE STUDY OF THE GREAT GREY SHRIKE
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Characterising and understanding emission sources using bivariate polar plots and k-means clustering
Carslaw, D. C., & Beevers, S. D. (2012) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2012.09.005 · Cited by 221 · see citing works
The cave of shadows: Addressing the human factor with generalized additive mixed models
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Carvalho, L., McDonald, C., de Hoyos, C., et al. (2013) · Journal of Applied Ecology · doi:10.1111/1365-2664.12059 · Cited by 216 · see citing works
Stokes, E. J., Strindberg, S., Bakabana, P. C., Elkan, P. W., Iyenguet, F. C., Madzoké, B., Malanda, G. A. F., Mowawa, B. S., Moukoumbou, C., Ouakabadio, F. K., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010294 · Cited by 216 · see citing works
- Authors
- Wood, S. N.
- Corresponding author
- Wood, Simon N.
- Venue
- Chapman & Hall/CRC eBooks
- Publisher
- Chapman and Hall/CRC
- Published
- 2017-05-18
- OpenAlex
- W3112717819
- Cited by
- 13,852
- References
- 0
- Citations by year
- 2026: 1378 · 2025: 1355 · 2024: 1085 · 2023: 934 · 2022: 976 · 2021: 1373 · 2020: 1048 · 2019: 787 · 2018: 603 · 2017: 556 · 2016: 560 · 2015: 536 · 2014: 585 · 2013: 499 · 2012: 480
- FWCI
- 208.09
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Chapman & Hall/CRC eBooks (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- generalized additive models, penalized spline regression, linear mixed models, generalized linear models, linear models, R software, nonparametric regression
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