An R and S-Plus Companion to Applied Regression

Preface 1. Introducing R and S-PLUS S Basics An Extended Illustration S Functions for Basic Statistics 2. Reading and Manipulating Data Data Input Working with Data Frames Matrices, Arrays, and Lists Data Attributes, Modes, and Classes 3. Exploring and Transforming Data Examining Distributions Examining Relationships Examining Multivariate Data Transforming Data 4. Fitting Linear Models Linear Least-Squares Regression Dummy-Variable Regression Analysis of Variance Models User-Specified Contrasts* General Linear Hypotheses* Data and Confidence Ellipses More on 1m and Model Formulas 5. Fitting Generalized Linear Models The Structure of GLMs Models for Categorical Responses Poisson GLMs for Count Data Odds and Ends Fitting GLMs by Iterated Weighted Least-Squares* 6. Diagnosing Problems Unusual Data Non-Normal Errors Non-Constant Error Variance Nonlinearity Collinearity and Variable Selection Diagnostics for Generalized Linear Models 7. Drawing Graphs A General Approach to S Graphics Putting it Together Effect Displays Graphics Devices 8. Writing Programs Defining Functions Working With Matrices* Program Control: Conditionals, Loops, and Recursion Apply and its Relatives Object-Oriented Programming in S* Writing S Programs