Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models

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CRC Press, Dec 20, 2005 - Mathematics - 312 pages
3 Reviews
Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies.

Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the data described in the book is available at http://people.bath.ac.uk/jjf23/ELM/ 

Statisticians need to be familiar with a broad range of ideas and techniques. This book provides a well-stocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught.
 

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Contents

1 Introduction
1
2 Binomial Data
28
3 Count Regression
61
4 Contingency Tables
76
5 Multinomial Data
106
6 Generalized Linear Models
126
7 Other GLMs
149
8 Random Effects
169
11 Nonparametric Regression
232
12 Additive Models
254
13 Trees
278
14 Neural Networks
296
Appendix A
307
Appendix B
316
Bibliography
318
Index
324

9 Repeated Measures and Longitudinal Data
203
10 Mixed Effect Models for Nonnormal Responses
221

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