An R Companion to Applied Regression
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Go to College Publishing WebsiteDescription
The Third Edition has been reorganized and 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 edition, introducing additional capabilities and making the software more consistent and easier to use. They also advocate an everyday data-analysis workflow that encourages reproducible research. To this end, they provide coverage of RStudio, an interactive development environment for R that allows readers to organize and document their work in a simple and intuitive fashion, and then easily share their results with others. Also included is coverage of R Markdown, showing how to create documents that mix R commands with explanatory text.
“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.”
–Christopher Hare, University of California, Davis
Contents
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
Additional materials
Description
The Third Edition has been reorganized and 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 edition, introducing additional capabilities and making the software more consistent and easier to use. They also advocate an everyday data-analysis workflow that encourages reproducible research. To this end, they provide coverage of RStudio, an interactive development environment for R that allows readers to organize and document their work in a simple and intuitive fashion, and then easily share their results with others. Also included is coverage of R Markdown, showing how to create documents that mix R commands with explanatory text.
“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.”
–Christopher Hare, University of California, Davis
Contents
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
Additional materials
Reviews
September 2018 | 608 pages | Sage US
| Format | Published Date | ISBN | Price |
|---|
The Third Edition has been reorganized and 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 edition, introducing additional capabilities and making the software more consistent and easier to use. They also advocate an everyday data-analysis workflow that encourages reproducible research. To this end, they provide coverage of RStudio, an interactive development environment for R that allows readers to organize and document their work in a simple and intuitive fashion, and then easily share their results with others. Also included is coverage of R Markdown, showing how to create documents that mix R commands with explanatory text.
“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.”
–Christopher Hare, University of California, Davis
Table Of Contents:
- 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