Vector generalized linear and additive models : with an implementation in R / Thomas W. Yee.

Includes bibliographical references and index.

1. Introduction.-
2. LMs, GLMs and GAMs.-
3. VGLMs.-
4. VGAMs.-
5. Reduced-Rank VGLMs.-
6. Constrained Quadratic Ordination.-
7. Constrained Additive Ordination.-
8. Using the VGAM Package.-
9. Other Topics.-
10. Some LM and GLM variants.-
11. Univariate Discrete Distributions.-
12. Univariate Continuous Distributions.-
13. Bivariate Continuous Distributions.-
14. Categorical Data Analysis.-
15. Quantile and Expectile Regression.-
16. Extremes.-
17. Zero-inated, Zero-altered and Positive Discrete Distributions.-
18. On VGAM Family Functions.-
A. Background Material.-
Glossary.-
References.-
Index.

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. 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. In the second part of the book, the R package VGAM allows readers to grasp immediately applications of the methodology. R code is integrated in the text, and datasets are used throughout. Potential applications include ecology, finance, biostatistics, and social sciences. The methodological contribution of this book stands alone and does not require use of the VGAM package.