Extending the Linear Model with R

Start Analysing 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 generalised linear models (GLMs).
• Revised chapters on random effects and repeated measures that reflect changes in the lme4 package, showing 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 generalised 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.