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Extending the Linear Model with R : Generalized Linear, Mixed Effects and Nonparametric Regression Models. 2nd ed. Rを用いた線形モデルの拡張 第2版
・ISBN 978-1-4987-2096-0 hard with passcode GB£ 96.99
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| 著者・編者 | Faraway, Julian J., |
|---|---|
| シリーズ | Chapman & Hall/CRC Texts in Statistical Science |
| 出版社 | (Chapman & Hall / CRC, US) |
| 出版年月 | 2016 |
| ページ数 | 399 pp. |
| 言語 | ENG |
| ニュース番号 | ＜631-358＞ |
解説
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.