Extending the Linear Model with R

Description

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. 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 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