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.
