## Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second EditionStart 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. |

### Contents

1 | |

Binary Response | 25 |

Binomial and Proportion Responses | 51 |

Variations on Logistic Regression | 67 |

Count Regression | 83 |

Contingency Tables | 103 |

Multinomial Data | 129 |

Generalized Linear Models | 151 |

Bayesian Mixed Effect Models | 255 |

Mixed Effect Models for Nonnormal Responses | 275 |

Nonparametric Regression | 297 |

Additive Models | 321 |

Trees | 343 |

Neural Networks | 365 |

Likelihood Theory | 375 |

About R | 383 |

Other GLMs | 175 |

Random Effects | 195 |

Repeated Measures and Longitudinal Data | 237 |

385 | |

Back Cover | 395 |

### Other editions - View all

Extending the Linear Model with R: Generalized Linear, Mixed Effects and ... Julian J. Faraway No preview available - 2016 |