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…mehr
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.
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.
Inhaltsangabe
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
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
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