Product Description

Join the revolution ignited by the ground-breaking R system! Starting with an introduction to R, covering standard regression methods, then presenting more advanced topics, this book guides users through the practical and powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display and interpretation of data. The many worked examples, taken from real-world research, are accompanied by commentary on what is done and why. A website provides computer code and data sets, allowing readers to reproduce all analyses. Updates and solutions to selected exercises are also available. Assuming only basic statistical knowledge, the book is ideal for research scientists, final-year undergraduate or graduate level students of applied statistics, and practising statisticians. It is both for learning and for reference. This revised edition reflects changes in R since 2003 and has new material on survival analysis, random coefficient models, and the handling of high-dimensional data.

Product Details

• Amazon Sales Rank: #297679 in Books
• Published on: 2006-12-26
• Original language: English
• Number of items: 1
• Binding: Hardcover
• 528 pages

Review
From reviews of previous edition: 'The strength of the book is in the extensive examples of practical data analysis with complete examples of the R code necessary to carry out the analyses ... I would strongly recommend the book to scientists who have already had a regression or a linear models course and who wish to learn to use R ... I give it a strong recommendation to the scientist or data analyst who wishes to have an easy-to-read and an understandable reference on the use of R for practical data analysis.' R News From reviews of previous edition: 'This book does an excellent job of describing the basics of a variety of statistical tools, both classical and modern, through examples from a wide variety of disciplines ... the book's writing style is very readable, with clear explanations and precise introductions of all topics and terminology ... the book also provides a wealth of examples from various physical and social sciences, engineering, and medicine that have been effectively chosen to illustrate not only the basics of the statistical methods, but also some of the interesting subtleties of the analyses that may require careful interpretation and discussion ... I believe that they have ... created a readable book that is rich with clear explanations and illustrative examples of the capability of a diverse set of tools. The packaging of the material with the R language is natural, and the extensive web pages of resources complement the book's usefulness for a road audience of statisticians and practitioners.' Biometrics From reviews of previous edition: 'This book does an excellent job of describing the basics of a variety of statistical tools, both classical and modern, through examples from a wide variety of disciplines ... With its focus on ideas and concepts, rather than an extensive formula-based presentation, the book finds a nice balance between discussing statistical concepts and teaching the basics of the freely-available statistical package R ... a readable book that is rich with clear explanations and illustrative examples of the capability of a diverse set of tools. The packaging of the material with the R language is natural, and the extensive web pages of resources complement the book's usefulness for a broad audience of statisticians and practitioners.' Journal of the American Statistical Association From reviews of previous edition: '... a very useful book that can be recommended for applied statisticians and other scientists who want to use R for data analysis, and as a textbook for an applied statistics course using R.' Journal of Applied Statistics From reviews of previous edition: '... an excellent intermediate-level text ... Though a bit more terse than Dalgaard's Introductory Statistics with R, Maindonald and Braun's exposition of the R language is nonetheless first rate.' DM Review Online

Review
From reviews of previous edition: "The strength of the book is in the extensive examples of practical data analysis with complete examples of the R code necessary to carry out the analyses. I would strongly recommend the book to scientists who have already had a regression or a linear models course and who wish to learn to use R. I give it a strong recommendation to the scientist or data analyst who wishes to an easy-to-read and an understandable reference on the use of R for practical data analysis."
R News

From reviews of previous edition: "The text includes a wealth of practical examples, drawn from a variety of practical applications which should be easily understood by the reader. The methods demonstrated are suitable for use in areas such as biology, social science, medicine and engineering. The core of the book is taken up with detailed discussion of regression methods which leads onto more advanced statistical concepts."
ISI Short Book Reviews

From reviews of previous edition: "This book does an excellent job of describing the basics of a variety of statistical tools, both classical and modern, through examples from a wide variety of disciplines; the book's writing style is very readable, with clear explanations and precise introductions of all topics and terminology; the book also provides a wealth of examples from various physical and social sciences, engineering, and medicine that have been effectively chosen to illustrate not only the basics of the statistical methods, but also some of the interesting subtleties of the analyses that may require careful interpretation and discussion. I believe that they have created a readable book that is rich with clear explanations and illustrative examples of the capability of a diverse set of tools. The packaging of the material with the R language is natural, and the extensive web page of resources complements the book's usefulness for a road audience of statisticians and practitioners."
Biometrics

From Previous Edition: "Provide considerable insight into very powerful procedures."
A. Ralph Henderson, Clinical Chemistry

"There are many books published in applied statistics that explain the R language. However, the book under review stands out due to its versatility and because it is easy to follow and understand the context."
Ita Cirovic Donev, The Mathematical Association of America

"...A gentle tour guide for new R users, aiming to help them navigate through many powerful tools that the open source R system offers."
Zhaohui Steve Qin, Center for Statistical Genetics, BioInformatics


"The style of the book is a commendable "learn by example" - each of the many statistical techniques is centered on real-world examples. The collective of topics is eclectic and the book also comes with extensive R code."
Carl James Schwarz, Biometrics

About the Author
John Maindonald is Visiting Fellow at the Mathematical Sciences Institute, Australian National University. He has collaborated extensively with scientists in a wide range of application areas, from medicine and public health, to population genetics, machine learning, economic history, and forensic linguistics.

John Braun is Associate Professor of Statistical and Actuarial Sciences, University of Western Ontario. He has collaborated with biostatisticians, biologists, psychologists and most recently has become involved with a network of forestry researchers.

Customer Reviews

Good introduction to R book
This is a good introductory book to R, covering basics of the language, statistical models, inference, regression (linear and logistic), experimental design, time series, classification, multivariate analysis, etc.

The book uses liberally examples and in most cases has the code for the output or graphics as footnotes at the bottom of the page. The book also tries to teach the statistics to a degree, which one can see as an annoyance (just teach me R!) or helpful if you are shaky on your stats. The book also lists a fair number of references to other books on S-plus and R to help point you in the direction towards achieving a higher level of adeptness and other references to learn more about the topics covered in the book.

The book also has exercises at the end of each chapter to get you into R and using the system. The answers to the exercises are not in the book, but are available in pdf format on the books corresponding website.

data analysis presented through R
The authors have written a very good and somewhat unique book on statistical data analysis. The emphasis is on linear models. graphics and diagnostics for identifying violations of modeling assumprions. They build up from the basics starting with simple one variable linear regression and correlation and then moving to multiple regression. Special cases of linear models suchas polynomial regression are presened. They then move on to various generalizations. When the residuals are correlated they consider time series models for the correlation structure of the residuals. Other specialized and important problems such as repeated measures for longitudinal data are covered.

Logistic regression is also introduced and shown to be a member of a larger class of models called generalized linear models which differ from linear models in that the dependent variable is a transformation of the basic dependent variable. The transformation is called the link function. For logistic regression the transformation is called the logit function. Hierarchical (or multi-level)models are also considered.

There is also a chapter on classification and regression trees. The final methods chapter covers multvariate analysis including classifcation, principal components,and propensity scores. These are topics not commonly seen in a first course on regression or data analysis.

What makes the book unique is a thorough introduction to the R programming language and the presntation of every technique with examples in R that both motivate the need for the technique and the details of the implementation in R. There is a lot of R code given and references to a variety of sources for R that can be found on the internet. The book can serve both as an introduction to data analysis and a tutorial on the R programming language. This can be useful as a text for undergraduate and graduate students. It is also an excellent reference for researchers who want to use R and its application to practical problems. The book also has an appendix that shows the relationship between R and S and SPlus, highlighting the differences. The first chapter is a careful introduction to R and the last chapter covers advanced applications in R.

The graphics used throughout the book are excellently presented and there are even a few color graphs. This text has just had a second edition published but my review is based on the 2003 version which is the one I purchased.

Good both for reference and for learning R
This is a great book for people under pressure (I'm a first-year biostat grad student, so I know whereof I speak) who want to get into doing serious data analysis quickly using R. It's also a good reference once you know the language better. The only reason I didn't give it five stars is that the organization is a little confusing, particularly when you're trying to find sample code to produce a particular figure or analysis -- overall, though, I think it's the best R-specific book out there for the general user.