• Provides readers with an up-to-date, well-stocked toolbox of statistical methodologies
• Includes numerous real examples that illustrate the use of R for data analysis
• Covers GLM diagnostics, generalized linear mixed models, trees, and the use of neural networks in statistics
• Reviews linear models as well as the basics of using R
  

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.

本帖隐藏的内容

ELM2.rar
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本附件包括：

• （Texts_in_statistical_science）_Faraway，_Julian_James－Extending_the_linear_model_with_R__generalized_linear，_mixed_effects_and_nonparametric_regression_models－CRC_Press_（2016）.pdf

ELM2scripts.zip
大小:(24.02 KB)

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本附件包括：

• am.R
• bayesme.R
• binapp.R
• binomial.R
• binresp.R
• contingency.R
• glm.R
• glmm.R
• introlm.R
• likelihood.R
• multinom.R
• neural.R
• npreg.R
• otherglm.R
• poisson.R
• random.R
• repmeas.R
• trees.R