An R Companion to Applied Regression

Chapter 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*

Chapter 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

Chapter 3: Exploring and Transforming Data

• • Examining Distributions
• • Examining Relationships
• • Examining Multivariate Data
• • Transforming Data
• • Point Labeling and Identication
• • Scatterplot Smoothing
• • Complementary Reading and References

Chapter 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

Chapter 5: Standard Errors, Confidence Intervals, Tests

• • Coefficient Standard Errors
• • Confidence Intervals
• • Testing Hypotheses About Regression Coefficients
• • Complementary Reading and References

Chapter 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

Chapter 7: Fitting Mixed-Effects Models

• • Background: The Linear Model Revisited
• • Linear Mixed-Effects Models
• • Generalized Linear Mixed Models
• • Complementary Reading

Chapter 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

Chapter 9: Drawing Graphs

• • A General Approach to R Graphics
• • Putting It Together: Local Linear Regression
• • Other R Graphics Packages
• • Complementary Reading and References

Chapter 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