An R Companion to Applied Regression

John Fox received a BA from the City College of New York and a PhD from the University of Michigan, both in Sociology. He is Professor Emeritus of Sociology at McMaster University in Hamilton, Ontario, Canada, where he was previously the Senator William McMaster Professor of Social Statistics. Prior to coming to McMaster, he was Professor of Sociology, Professor of Mathematics and Statistics, and Coordinator of the Statistical Consulting Service at York University in Toronto. Professor Fox is the author of many articles and books on applied statistics, including \emph{Applied Regression Analysis and Generalized Linear Models, Third Edition} (Sage, 2016). He is an elected member of the R Foundation, an associate editor of the Journal of Statistical Software, a prior editor of R News and its successor the R Journal, and a prior editor of the Sage Quantitative Applications in the Social Sciences monograph series. Sanford Weisberg is Professor Emeritus of statistics at the University of Minnesota.  He has also served as the director of the University′s Statistical Consulting Service, and has worked with hundreds of social scientists and others on the statistical aspects of their research.  He earned a BA in statistics from the University of California, Berkeley, and a Ph.D., also in statistics, from Harvard University, under the direction of Frederick Mosteller.  The author of more than 60 articles in a variety of areas, his methodology research has primarily been in regression analysis, including graphical methods, diagnostics, and computing.  He is a fellow of the American Statistical Association and former Chair of its Statistical Computing Section.  He is the author or coauthor of several books and monographs, including the widely used textbook Applied Linear Regression, which has been in print for almost forty years.

"An R Companion to Applied Regression continues to provide the most comprehensive and user-friendly guide to estimating, interpreting, and presenting results from regression models in R."

• 1. Getting Started with R and RStudioProjects in RStudioR BasicsFixing Errors and Getting HelpOrganizing Your Work in R and RStudioAn Extended IllustrationR Functions for Basic StatisticsGeneric Functions and Their Methods*2. Reading and Manipulating DataData InputManaging DataWorking With Data FramesMatrices, Arrays, and ListsDates and TimesCharacter DataLarge Data Sets in R*Complementary Reading and References3. Exploring and Transforming DataExamining DistributionsExamining RelationshipsExamining Multivariate DataTransforming DataPoint Labeling and IdenticationScatterplot SmoothingComplementary Reading and References4. Fitting Linear ModelsThe Linear ModelLinear Least-Squares RegressionPredictor Effect PlotsPolynomial Regression and Regression SplinesFactors in Linear ModelsLinear Models with InteractionsMore on FactorsToo Many Regressors*The Arguments of the lm FunctionComplementary Reading and References5. Standard Errors, Confidence Intervals, TestsCoefficient Standard ErrorsConfidence IntervalsTesting Hypotheses About Regression CoefficientsComplementary Reading and References6. Fitting Generalized Linear ModelsThe Structure of GLMsThe glm() Function in RGLMs for Binary-Response DataBinomial DataPoisson GLMs for Count DataLoglinear Models for Contingency TablesMultinomial Response DataNested DichotomiesThe Proportional-Odds ModelExtensionsArguments to glm()Fitting GLMs by Iterated Weighted Least-Squares*Complementary Reading and References7. Fitting Mixed-Effects ModelsBackground: The Linear Model RevisitedLinear Mixed-Effects ModelsGeneralized Linear Mixed ModelsComplementary Reading8. Regression DiagnosticsResidualsBasic Diagnostic PlotsUnusual DataTransformations After Fitting a Regression ModelNon-Constant Error VarianceDiagnostics for Generalized Linear ModelsDiagnostics for Mixed-Effects ModelsCollinearity and Variance-Inflation FactorsAdditional Regression DiagnosticsComplementary Reading and References9. Drawing GraphsA General Approach to R GraphicsPutting It Together: Local Linear RegressionOther R Graphics PackagesComplementary Reading and References10. An Introduction to R ProgrammingWhy Learn to Program in R?Defining Functions: Preliminary ExamplesWorking With Matrices*Conditionals, Loops, and RecursionAvoiding LoopsOptimization Problems*Monte-Carlo Simulations*Debugging R Code*Object-Oriented Programming in R*Writing Statistical-Modeling Functions in R*Organizing Code for R FunctionsComplementary Reading and References