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

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CRC Press, Feb 10, 2016 - 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

Binomial Data
25
Count Regression
55
Contingency Tables
69
Multinomial Data
95
Generalized Linear Models
113
Other GLMs
133
Random Effects
151
Repeated Measures and Longitudinal Data
183
Nonparametric Regression
209
Additive Models
229
Trees
251
Neural Networks
267
Likelihood Theory
277
R Information
285
Bibliography
287
Back Cover
295

Mixed Effect Models for Nonnormal Responses
199

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