An R companion to applied regression / John Fox, Sanford Weisberg.

  • Fox, John, 1947-
Date:
[2011]
  • Books

About this work

Description

This book aims to provide a broad introduction to the R statistical computing environment (R Development Core Team, 2009a) in the context of applied regression analysis, which is typically studied by social scientists and others in a second course in applied statistics. We assume that the reader is learning or is otherwise familiar with the statistical methods that we describe; thus, this book is a companion to a text or course on modern applied regression, such as, but not necessarily, our own Applied Regression Analysis and Generalized Linear Models, second edition (Fox, 2008) and Applied Linear Regression, third edition (Weisberg, 2005).

Publication/Creation

Los Angeles : SAGE, [2011]

Physical description

xxii, 449 pages : black and white illustrations ; 26 cm

Edition

2nd edition.

Notes

Revised edition of: An R and S-Plus companion to applied regression. c2002.

Contents

1 Getting Started With R -- 1.1 R Basics -- 1.2 An Extended Illustration: Duncan's Occupational-Prestige Regression -- 1.3 R Functions for Basic Statistics -- 1.4 Generic Functions and Their Methods -- 1.5 The R Commander Graphical User Interface -- 2 Reading and Manipulating Data -- 2.1 Data Input -- 2.2 Working With Data Frames -- 2.3 Matrices, Arrays, and Lists -- 2.4 Manipulating Character Data -- 2.5 Handling Large Data Sets in R -- 2.6 More on the Representation of Data in R -- 2.7 Complementary Reading and References -- 3 Explori and Transforming Data -- 3.1 Examining Distributions -- 3.2 Examining Relationships -- 3.3 Examining Multivariate Data -- 3.4 Transforming Data -- 3.5 Point Labeling and Identification -- 3.6 Complementary Reading and References -- 4 Fitting Linear Models -- 4.1 Introduction -- 4.2 Linear Least-Squares Regression -- 4.3 Working With Coefficients -- 4.4 Testing Hypotheses About Regression Coefficients -- 4.5 Model Selection -- 4.6 More on Factors -- 4.7 Overparametrized Models -- 4.8 The Arguments of the 1m Function -- 4.9 Using 1m Objects -- 4.10 Complementary Reading and References -- 5 Fitting Generalized Linear Models -- 5.1 The Structure of GLMs -- 5.2 The glm Function in R -- 5.3 GLMs for Binary-Response Data -- 5.4 Binomial Data -- 5.5 Poisson GLMs for Count Data -- 5.6 Loglinear Models for Contingency Tables -- 5.7 Multinomial Response Data -- 5.8 Nested Dichotomies -- 5.9 Proportional-Odds Model -- 5.10 Extensions -- 5.11 Arguments to glm -- 5.12 Fitting GLMs by Iterated Weighted Least Squares -- 5.13 Complementary Reading and References -- 6 Diagnosing Problems in Linear and Generalized Linear Models -- 6.1 Residuals -- 6.2 Basic Diagnostic Plots -- 6.3 Unusual Data -- 6.4 Transformations After Fitting a Regression Model -- 6.5 Nonconstant Error Variance -- 6.6 Diagnostics for Generalized Linear Models -- 6.7 Collinearity and Variance Inflation Factors -- 6.8 Complementary Reading and References -- 7 Drawing Graphs -- 7.1 A General Approach to R Graphics -- 7.2 Putting It Together: Explaining Nearest-Neighbor Kernel Regression -- 7.3 Lattice and Other Graphics Packages in R -- 7.4 Graphics Devices -- 7.5 Complementary Reading and References -- 8 Writing Programs -- 8.1 Defining Functions -- 8.2 Working With Matrices -- 8.3 Program Control: Conditionals, Loops, and Recursion -- 8.4 Apply and Its Relatives -- 8.5 Illustrative R Programs -- 8.6 Improving R Programs -- 8.7 Object-Oriented Programming in R -- 8.8 Writing Statistical-Modeling Functions in R -- 8.9 Environments and Scope in R -- 8.10 R Programming Advice -- 8.11 Complementary Reading and References.

Bibliographic information

Includes bibliographical references and indexes.

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Where to find it

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Identifiers

ISBN

  • 9781412975148
  • 141297514X