An R companion to applied regression. (2018)

• Record Type: Book
• Further Information: Note: John Fox, Harvey Sanford Weisberg.
• Authors: Fox, John, 1947-; Weisberg, Sanford, 1947-
• 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;
• Edition: Third edition
• Publisher Details: Los Angeles : SAGE
• Publication Date: 2018
• Extent: 1 online resource
• Subjects: 519.5360285; Regression analysis -- Data processing; R (Computer program language)
• Languages: English
• ISBNs: 9781544336459
• Related ISBNs: 9781544336473
• Notes: Note: Description based on CIP data; resource not viewed.
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