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I'm looking for a text to help someone who uses GLMs in practice become familiar/comfortable with GAMs. Online or physical textbook would be fine. I am approaching this as a practitioner, so I would prefer practical to theoretical, but I'm confident I have the background to digest a more theoretical text if it happens to be particularly worthwhile.

I also use R, almost exclusively. If you happen to know of some R oriented texts/tutorials that'd be really appreciated.

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    $\begingroup$ I would go with Simon Wood's Generalised Additive Models: an introduction with R. Even though mgcv has moved on a lot from what was used in this book (See citation(package = "mgcv") for some of the more recent proper papers on the methods), this remains one of the best books I know for learning about GAMs (and GLMs) with a practical side in R and a theoretical side. $\endgroup$ Aug 1 '14 at 16:00
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    $\begingroup$ I ordered Gavin's book. It appears to be the perfect suggestion. I agree that he should have made it an answer. $\endgroup$
    – Eric
    Aug 14 '14 at 13:29
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Buja et al., (1989) give a nice overview of GAMs in the context of other additive nonparametric smoothing models. If I recall correctly, most of their examples can be easily done in R using existing packages.

Buja, A., Hastie, T., and Tibshirani, R. (1989). Linear Smoothers and Additive Models. The Annals of Statistics, 17(2):453–510.

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Hastie & Tibshirani's original textbook is still a great read IMO: Hastie, T. & Tibshirani, R. (1990) Generalized Additive Models, Chapman & Hall. I personally found it much easier to follow than Simon Wood's text, even if the latter is more up-to-date.

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I thought Michael Clark's overview is excellent: https://m-clark.github.io/generalized-additive-models/

A great short (very short) introduction to get you started.

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I came across this introductory video on GAM that I thought was helpful and easy to follow: https://www.youtube.com/watch?v=nXDYapfalt4

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Not a text but I found this to be a great resource for GAMs using R: https://noamross.github.io/gams-in-r-course/

It leads you through the nature of fitting a GAM without getting into the maths involved.

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See chapter 3 in "Mixed effects models and extensions in ecology with R. A Zuur, EN Ieno, N Walker, AA Saveliev, GM Smith"

Ch3 provides a really basic intro to GAM in both the GAM and MGCV packages. In further chapters the book covers a lot of the things you might do with GAM and goes into GAMM, and zero-inflated GAM.

Its a great intro and also helpful for advanced applications.

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