An R Companion to Applied Regression

Auteur

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.

Texte du rabat

An 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.

Résumé
An R Companion to Applied Regression is a broad introduction to the R statistical computing environment in the context of applied regression analysis.

Contenu

1. Getting Started with R and RStudio
   Projects in RStudio
   R Basics
   Fixing Errors and Getting Help
   Organizing Your Work in R and RStudio
   An Extended Illustration
   R Functions for Basic Statistics
   Generic Functions and Their Methods*
   
   2. Reading and Manipulating Data
      Data Input
      Managing Data
      Working With Data Frames
      Matrices, Arrays, and Lists
      Dates and Times
      Character Data
      Large Data Sets in R*
      Complementary Reading and References
   3. Exploring and Transforming Data
      Examining Distributions
      Examining Relationships
      Examining Multivariate Data
      Transforming Data
      Point Labeling and Identication
      Scatterplot Smoothing
      Complementary Reading and References
   4. Fitting Linear Models
      The Linear Model
      Linear Least-Squares Regression
      Predictor Effect Plots
      Polynomial Regression and Regression Splines
      Factors in Linear Models
      Linear Models with Interactions
      More on Factors
      Too Many Regressors*
      The Arguments of the lm Function
      Complementary Reading and References
   5. Standard Errors, Confidence Intervals, Tests
      Coefficient Standard Errors
      Confidence Intervals
      Testing Hypotheses About Regression Coefficients
      Complementary Reading and References
   6. Fitting Generalized Linear Models
      The Structure of GLMs
      The glm() Function in R
      GLMs for Binary-Response Data
      Binomial Data
      Poisson GLMs for Count Data
      Loglinear Models for Contingency Tables
      Multinomial Response Data
      Nested Dichotomies
      The Proportional-Odds Model
      Extensions
      Arguments to glm()
      Fitting GLMs by Iterated Weighted Least-Squares*
      Complementary Reading and References
   7. Fitting Mixed-Effects Models
      Background: The Linear Model Revisited
      Linear Mixed-Effects Models
      Generalized Linear Mixed Models
      Complementary Reading
   8. Regression Diagnostics
      Residuals
      Basic Diagnostic Plots
      Unusual Data
      Transformations After Fitting a Regression Model
      Non-Constant Error Variance
      Diagnostics for Generalized Linear Models
      Diagnostics for Mixed-Effects Models
      Collinearity and Variance-Inflation Factors
      Additional Regression Diagnostics
      Complementary Reading and References
   9. Drawing Graphs
      A General Approach to R Graphics
      Putting It Together: Local Linear Regression
      Other R Graphics Packages
      Complementary Reading and References
   10. An Introduction to R Programming
       Why Learn to Program in R?
       Defining Functions: Preliminary Examples
       Working With Matrices*
       Conditionals, Loops, and Recursion
       Avoiding Loops
       Optimization Problems*
       Monte-Carlo Simulations*
       Debugging R Code*
       Object-Oriented Programming in R*
       Writing Statistical-Modeling Functions in R*
       Organizing Code for R Functions
       Complementary Reading and References