Chapman and Hall/CRC
399 pages | 115 B/W Illus.
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
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.
Praise for the First Edition:
"… well-written and the discussions are easy to follow … very useful as a reference book for applied statisticians and would also serve well as a textbook for students graduating in statistics."
—Computational Statistics, April 2009, Vol. 24
"The text is well organized and carefully written … provides an overview of many modern statistical methodologies and their applications to real data using software. This makes it a useful text for practitioners and graduate students alike."
—Journal of the American Statistical Association, December 2007, Vol. 102, No. 480
"I enjoyed this text as much as [Faraway’s Linear Models with R]. The book is recommended as a textbook for a computational statistical and data mining course including GLMs and non-parametric regression, and will also be of great value to the applied statistician whose statistical programming environment of choice is R."
—Journal of Applied Statistics, July 2007, Vol. 34, No. 5
"This is a very pleasant book to read. It clearly demonstrates the different methods available and in which situations each one applies. It covers almost all of the standard topics beyond linear models that a graduate student in statistics should know. It also includes discussion of topics such as model diagnostics, rarely addressed in books of this type. The presentation incorporates an abundance of well-chosen examples … this book is highly recommended …"
—Biometrics, December 2006
"The second edition of book ‘Extending the linear model with R’ by Julian Faraway is an easily readable and relatively thorough (without being theory heavy) sequel of the earlier ‘Linear Models with R’ by the same author. The book itself is written in a self-paced tutorial style in easily digestible chunks integrating descriptions of underlying methodology, with data analysis and R code. The organization of the book is well thought through. The flow of the book is problem driven rather than driven by the underlying statistical theory . . . the second edition is more polished in terms of the figures used, R code and output display and a crisper typesetting of equations."
—John T. Ormerod, University of Sydney
"It has been a great pleasure to review this book, which delivers both a readily accessible and reader-friendly account of a wide range of statistical models in the context of R software. Since the publication of the very well received first edition of the book, R has considerably expanded both in popularity and in the number of packages available. The second editionof the book takes advantage of the greater functionality available now in R, and substantially revises and adds several new topics."
—Andrzej Galecki, The International Biometric Society
Heart Disease Example
Goodness of Fit
Binomial and Proportion Responses
Binomial Regression Model
Pearson’s χ2 Statistic
Variations on Logistic Regression
Prospective and Retrospective Sampling
Prediction and Effective Doses
Matched Case-Control Studies
Dispersed Poisson Model
Zero Inflated Count Models
Larger Two-Way Tables
Three-Way Contingency Tables
Multinomial Logit Model
Linear Discriminant Analysis
Hierarchical or Nested Responses
Ordinal Multinomial Responses
Generalized Linear Models
Fitting a GLM
Inverse Gaussian GLM
Joint Modeling of the Mean and Dispersion
Estimating Random Effects
Blocks as Random Effects
Repeated Measures and Longitudinal Data
Multiple Response Multilevel Models
Bayesian Mixed Effect Models
Mixed Effect Models for Nonnormal Responses
Generalized Linear Mixed Models
Generalized Estimating Equations
Discussion of Methods
Modeling Ozone Concentration
Additive Models Using mgcv
Generalized Additive Models
Alternating Conditional Expectations
Additivity and Variance Stabilization
Generalized Additive Mixed Models
Multivariate Adaptive Regression Splines
Classification Using Forests
Statistical Models as NNs
Feed-Forward Neural Network with One Hidden Layer
Appendix A: Likelihood Theory
Appendix B: About R