
An R and S-Plus Companion to Applied Regression
John Fox(Author)
SAGE Publications Inc (Publisher)
1st Edition
Published on 30. July 2002
Book
Paperback/Softback
328 pages
978-0-7619-2280-3 (ISBN)
Article exhausted; check for reprint
Description
`This book fits right into a needed niche: rigorous enough to give full explanation of the power of the S language, yet accessible enough to assign to social science graduate students without fear of intimidation. It is a tremendous balance of applied statistical "firepower" and thoughtful explanation. it meets all of the important mechanical needs: each example is given in detail, code and data are freely available, and the nuances of models are given rather than just the bare essentials. It also meets some important theoretical needs: linear models, categorical data analysis, an introduction to applying GLMs, a discussion of model diagnostics and useful instructions on writing customized functions' - Jeff Gill, University of Florida, Gainesville
Reviews / Votes
"The text does an outstanding job of providing the necessary mechanics and theory of the S language. I will use this book in every such course that I teach from this point on." -- Jeff Gill "The book provides a valuable supplement to texts on regression analysis and linear models by showing readers how to put into practice the strategies and techniques involved in modern statistical methodology. It explains clearly the use of a very sophisticated and powerful statistical software system. And, while the examples and objectives are focused closely on regression and related techniques, the discussion successfully conveys general advice and principles for statistical computing with the S system." -- William Jacoby "The style of presentation is rather informal and hands-on, software usage is demonstrated using examples, without lengthy discussions of theory...it is not necessary to switch to a more theoretical text while reading it...In summary, I highly recommend the book to anyone who wants to learn or teach applied regression analysis with S." -- Friedrich Leisch "Together, John Fox's Applied Regression Analysis, Linear Models and Related Methods and the R and S-plus Companion to Applied Regression have made a fantastic contribution to the world of quantitative social science methology...For students and professors that have yet to discover the wonders of the S language, the Companion is a must read." -- Ryan Baker * The Political Methologist * "The book is written in a lively style with an occasional witticism interspresed. The writing is scholarly without being stilted...Baker (2002) has said it nicely addressing the combination of the Companion and the Fox (1997) statistical text. He observed that Fox '...has made a fantastic contribution to the world of quantitative social science methodology' (page 6). My assessment exactly." -- Malcolm J. Ree * Journal of Educational and Behavioral Sciences * "This book is laid out in a way that facilitates its use in three major applications...it is eminently practical...Overall, Fox has created a remarkably useful book, not only for learning the S language, but also for understanding applied regression techniques as well." -- Paul Millar * Canadian Journal of Sociology * "[The book] provides an excellent vehicle for students and others without previous experience with S in any form to become comfortable with the basics of the enviornment and particularly with the tools available for the preliminary exploratory analysis and for fitting and evaluating linear and generalized linear models...Fox makes thoughtful and effective efforts to prevent the frustration that new users of sophisticated software all to often experience." -- Mary Kathryn Cowles * The American Statistician *More details
Language
English
Place of publication
Thousand Oaks
United States
Target group
College/higher education
Dimensions
Height: 254 mm
Width: 178 mm
Weight
680 gr
ISBN-13
978-0-7619-2280-3 (9780761922803)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
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John Fox | Sanford Weisberg
An R Companion to Applied Regression
Book
01/2011
2nd Edition
SAGE Publications Inc
€102.50
Article exhausted; check for reprint
Person
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.
Content
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
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