## An R Companion to Applied RegressionThis is a broad introduction to the R statistical computing environment in the context of applied regression analysis. It is a thoroughly updated edition of John Fox’s bestselling text An R and S-Plus Companion to Applied Regression (SAGE, 2002). The Second Edition is intended as a companion to any course on modern applied regression analysis. The authors provide a step-by-step guide to using the high-quality free statistical software R, an emphasis on integrating statistical computing in R with the practice of data analysis, coverage of generalized linear models, enhanced coverage of R graphics and programming, and substantial web-based support materials. |

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### Contents

Chapter 1 Getting Started With R | 1 |

Chapter 2 Reading and Manipulating Data | 43 |

Chapter 3 Exploring and Transforming Data | 107 |

Chapter 4 Fitting Linear Models | 149 |

Chapter 5 Fitting Generalized Linear Models | 229 |

Chapter 6 Diagnosing Problems in Linear and Generalized Linear Models | 285 |

Chapter 7 Drawing Graphs | 329 |

Chapter 8 Writing Programs | 359 |

425 | |

Author Index | 431 |

433 | |

441 | |

447 | |

448 | |

About the Authors | 449 |

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1Q Median 3Q added-variable plot ANOVA argument axis bc bc bc binomial boxplots called car package chapter column command computed data frame data set default degrees of freedom density diagnostics distribution Duncan effect plot elements Error t value Estimate Std example F-statistic factor FALSE fcategory female Figure fitted values function(x GLMs graph graphics histogram income interactions Intercept iterations labels least-squares levels Likelihood ratio tests linear models lm(formula log2(income logistic regression logit lowess Mac OS X male Median 3Q Max method missing data model fit not.work NULL object observations occupational-prestige Ornstein’s output p-value panel parameter partic partner.status points Poisson Poisson regression Prestige data produces prof prof prof programming R-squared read.table regression model regressors repwt response variable result sample saturated model scatterplot Section specified standard error statistical Studentized residuals subset tion transformation Type II tests vector Wald tests Windows