An R Companion to Applied RegressionAn R Companion to Applied Regression is a broad introduction to the R statistical computing environment in the context of applied regression analysis. John Fox and Sanford Weisberg provide a step-by-step guide to using the free statistical software R, an emphasis on integrating statistical computing in R with the practice of data analysis, coverage of generalized linear models, and substantial web-based support materials. The Third Edition includes a new chapter on mixed-effects models, new and updated data sets, and a de-emphasis on statistical programming, while retaining a general introduction to basic R programming. The authors have substantially updated both the car and effects packages for R for this new edition, and include coverage of RStudio and R Markdown. |
Contents
CHAPTER 1 GETTING STARTED WITH R AND RSTUDIO | 1 |
CHAPTER 2 READING AND MANIPULATING DATA | 53 |
CHAPTER 3 EXPLORING AND TRANSFORMING DATA | 123 |
CHAPTER 4 FITTING LINEAR MODELS | 173 |
CHAPTER 5 COEFFICIENT STANDARD ERRORS CONFIDENCE INTERVALS AND HYPOTHESIS TESTS | 243 |
CHAPTER 6 FITTING GENERALIZED LINEAR MODELS | 271 |
CHAPTER 7 FITTING MIXEDEFFECTS MODELS | 335 |
CHAPTER 8 REGRESSION DIAGNOSTICS FOR LINEAR GENERALIZED LINEAR AND MIXEDEFFECTS MODELS | 385 |
CHAPTER 9 DRAWING GRAPHS | 437 |
CHAPTER 10 AN INTRODUCTION TO R PROGRAMMING | 477 |
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Common terms and phrases
added-variable plot ageGroup analysis Anova argument axis bc bc bc binomial bootstrap boxplots called car package carData package Chapter column command computed confidence intervals covariance matrix cses data frame data set data.frame default density deviance distribution Duncan elements Estimate Std example F-statistic factor female Figure fitted values function GLMs graph graphics histogram hypothesis includes income inner margin line interaction intercept interlocks labels levels likelihood-ratio test linear models linear regression log2(income male Markdown Markdown document mean mean.ses Median method missing data mixed models mixed-effects models model fit model formula NULL numeric predictors object output p-value panel parameters points Poisson predictor effect plot produced programming random effects regression coefficients regression model regressors response variable row names RStudio sample saturated model scatterplot Section slope specify standard errors statistical Studentized residuals tion transformation vector vertical Wald Wald tests zero zipmod