Extending the Linear Model with R (Texts in Statistical Science)

Extending the Linear Model with R (Texts in Statistical Science)

<P><EM>Start Analyzing a Wide Range of Problems </EM></P> <P>Since the publication of the bestselling, highly recommended first edition, R has considerably expanded both in popularity and in the number of packages available. <STRONG>Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition</STRONG> takes advantage of the greater functionality now available in R and substantially revises and adds several topics.</P> <P><EM>New to the Second Edition</EM></P> <UL> <LI>Expanded coverage of binary and binomial responses, including proportion responses, quasibinomial and beta regression, and applied considerations regarding these models </LI> <LI>New sections on Poisson models with dispersion, zero inflated count models, linear discriminant analysis, and sandwich and robust estimation for generalized linear models (GLMs) </LI> <LI>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</LI> <LI>New chapter on the Bayesian analysis of mixed effect models that illustrates the use of STAN and presents the approximation method of INLA </LI> <LI>Revised chapter on generalized linear mixed models to reflect the much richer choice of fitting software now available</LI> <LI>Updated coverage of splines and confidence bands in the chapter on nonparametric regression</LI> <LI>New material on random forests for regression and classification </LI> <LI>Revamped R code throughout, particularly the many plots using the ggplot2 package</LI> <LI>Revised and expanded exercises with solutions now included</LI></UL> <P><EM>Demonstrates the Interplay of Theory and Practice</EM></P> <P>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.</P>