An R Companion to Applied RegressionSAGE Publications, 2011 - 449 pages This 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. |
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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Common terms and phrases
added-variable plot Anova argument axis bc bc bc binomial boxplots called car package chapter column command computed Cook's distances data frame data set default defined degrees of freedom density diagnostics distribution effect plot elements Error t value Estimate Std example F-statistic factor FALSE fcategory Female Figure fitted values function(x GLMs graph graphics hat-values histogram income interactions Intercept interlocks iterations labels least-squares levels Likelihood ratio tests linear models lm(formula log2(income logistic regression logit lowess Mac OS X Male method missing data model fit model matrix not.work NULL object observations occupational-prestige output p-value panel parameter partic partner.status points Poisson Poisson regression Prestige data produces prof prof prof programming R-squared regression model regressors repwt response variable result returns sample saturated model scatterplot Section specified standard error statistical Studentized residuals subset tion transformation Type II tests vector Wald tests Windows
