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An R companion to applied regression

Author: John Fox; Sanford Weisberg
Publisher: Los Angeles : SAGE, [2011] ©2011
Edition/Format:   Print book : English : 2nd editionView all editions and formats
Database:WorldCat
Summary:
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  Read more...
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Document Type: Book
All Authors / Contributors: John Fox; Sanford Weisberg
ISBN: 9781412975148 141297514X
OCLC Number: 648922089
Notes: Revised edition of: An R and S-Plus companion to applied regression. c2002.
Description: xxii, 449 pages : black and white illustrations ; 26 cm
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.
Responsibility: John Fox, Sanford Weisberg.

Abstract:

The authors provide a step-by-step guide to using the high-quality free statistical software R, an emphasis on integrating statistical computing in R with the practice of data analysis, coverage of  Read more...

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"The text is very clearly written. It contains much wisdom and useful hints for those trying to analyze data with R." -- Robert W. Hayden

 
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