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

Julian J. Faraway

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March 24, 2016 by Chapman and Hall/CRC
Textbook - 399 Pages - 115 B/W Illustrations
ISBN 9781498720960 - CAT# K25559
Series: Chapman & Hall/CRC Texts in Statistical Science

USD$99.95

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Features

  • Provides readers with an up-to-date, well-stocked toolbox of statistical methodologies
  • Includes numerous real examples that illustrate the use of R for data analysis
  • Covers GLM diagnostics, generalized linear mixed models, trees, and the use of neural networks in statistics
  • Reviews linear models as well as the basics of using R
  • Offers the datasets and other material on the author’s website

Summary

Start Analyzing a Wide Range of Problems

Since the publication of the bestselling, highly recommended first edition, R has considerably expanded both in popularity and in the number of packages available. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition takes advantage of the greater functionality now available in R and substantially revises and adds several topics.

New to the Second Edition

  • Expanded coverage of binary and binomial responses, including proportion responses, quasibinomial and beta regression, and applied considerations regarding these models
  • New sections on Poisson models with dispersion, zero inflated count models, linear discriminant analysis, and sandwich and robust estimation for generalized linear models (GLMs)
  • Revised chapters on random effects and repeated measures that reflect changes in the lme4 package and show how to perform hypothesis testing for the models using other methods
  • New chapter on the Bayesian analysis of mixed effect models that illustrates the use of STAN and presents the approximation method of INLA
  • Revised chapter on generalized linear mixed models to reflect the much richer choice of fitting software now available
  • Updated coverage of splines and confidence bands in the chapter on nonparametric regression
  • New material on random forests for regression and classification
  • Revamped R code throughout, particularly the many plots using the ggplot2 package
  • Revised and expanded exercises with solutions now included

Demonstrates the Interplay of Theory and Practice

This textbook continues to cover a range of techniques that grow from the linear regression model. It presents three extensions to the linear framework: GLMs, mixed effect models, and nonparametric regression models. The book explains data analysis using real examples and includes all the R commands necessary to reproduce the analyses.

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