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Generalized linear models with examples in R. (English) Zbl 1416.62020
Springer Texts in Statistics. New York, NY: Springer (ISBN 978-1-4419-0117-0/hbk; 978-1-4419-0118-7/ebook). xx, 562 p. (2018).
The authors provide an overview of how to fit and test generalized linear models in R. Generalized linear models go beyond linear models to also include dichotomous outcome models, count models, continuous positive models, and Tweedie EDMs. There is also a chapter on maximum likelihood estimation that is not necessary for understanding the rest of the book, but provides background on how the models are estimated.
The text itself acknowledges the theory, but has the focus on applied analysis. Nearly every page contains code examples in R that the reader can run. The data supporting this text are available in the GLMsData package, available from CRAN. The working examples allow readers to take the code from the book and apply it quickly and the text supports the code examples with an explanation of how to interpret the output.
The book is targeted at students and notes it is appropriate for graduate students. It is also useful to the junior statistician needing to learn how to work a model they are unfamiliar with. The practicing and experienced statistician can use this as a quick reference for working a model they may have forgotten the specific of.

MSC:
62-02 Research exposition (monographs, survey articles) pertaining to statistics
62J12 Generalized linear models (logistic models)
62-04 Software, source code, etc. for problems pertaining to statistics
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