Extending the linear model with R : generalized linear, mixed effects and nonparametric regression models
著者
書誌事項
Extending the linear model with R : generalized linear, mixed effects and nonparametric regression models
（Texts in statistical science）
CRC Press, an imprint of Taylor & Francis Group, c2016
2nd ed
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注記
"A CHAPMAN & HALL BOOK"--T.p
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
Introduction. Binary Response. Binomial and Proportion Responses. Variations on Logistic Regression. Count Regression. Contingency Tables. Multinomial Data. Generalized Linear Models. Other GLMS. Random Effects. Repeated Measures and Longitudinal Data. Bayesian Mixed Effect Models. Mixed Effect Models for Nonnormal Responses. Nonparametric Regression. Additive Models. Trees. Neural Networks. Appendices. Bibliography. Index.
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