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0원  | 20060227  | 9781584884743

Generalized Additive Models: An Introduction with R imparts a thorough understanding of the theory and practical applications of GAMs and related advanced models, enabling informed use of these flexible tools. The author's approach is based on a framework of penalized regression splines, and he builds the necessary foundation through motivating chapters on linear and generalized linear models. Use of the freely available R software helps explain the theory and illustrates the practicalities of linear, generalized linear, and generalized additive models, as well as their mixed effect extensions. An R package containing the featured datasets is freely available on the Internet.

Wood, Simon  | Chapman & Hall

157,470원  | 20170726  | 9781498728331

 | Springer Nature B.V.

74,480원  | 20150914  | 9781493928194

This book presents a greatly enlarged statistical framework compared to generalized linear models (GLMs) with which to approach regression modelling. Comprising of about half-a-dozen major classes of statistical models, and fortified with necessary infrastructure to make the models more fully operable, the framework allows analyses based on many semi-traditional applied statistics models to be performed as a coherent whole.

Thomas W. Yee  | Springer Verlag

268,180원  | 20150914  | 9781493928170

This book treats distributions and classical models as generalized regression models, and the result is a much broader application base for GLMs and GAMs. The book can be used in senior undergraduate or first-year postgraduate courses on GLMs or categorical data analysis and as a methodology resource for VGAM users.