Generalized Additive Models: An Introduction with R

In 2006 I published a book called Generalized Additive Models: An Introduction with R , which aims to introduce GAMs as penalized GLMs, and Generalized Additive Mixed Models as examples of generalized linear mixed models. It also serves as a useful reference for the mgcv package in R. The book has chapters on linear models, generalized linear models, how a GAM is constructed using penalized regression splines, GAM theory, using GAMs with mgcv and finally on mixed models and generalized additive mixed models.

You can take a look at Chapter 1 here ).

The current errata list for the first edition can be found here .

Second edition 2017

The second edition of the book was published in 2017. The second edition is a major re-write covering the advances in the decade since the first edition. It has a completely revised structure, with greater emphasis on mixed models and the equivalence of smooths and Gaussian random fields. An enhanced range of smoothers is covered, along side a thorough upgrading of the chapter on GAM theory, and many new examples including functional data analysis, survival analysis, location-scale modelling and more.

The errata list for the second edition is here.

First edition book reviews (that I know about):

Software changes affecting the first edition.

Changes with mgcv 1.5

The major change in 1.5 is that smoothness selection can now be done using REML or ML, in additition to GCV, GACV or AIC/UBRE. This has lead to some changes in how `gam' is called:

Changes with mgcv 1.4

mgcv 1.4 has several features not covered in the book, and means that the output presented in the book will differ slightly in a few places. The output changes are as follows: The new features in mgcv 1.4 are: