Data Analysis and Graphics Using R: An Example-based Approach

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Cambridge University Press, Dec 26, 2006 - Computers
4 Reviews
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.
 

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Contents

Preface page xix
5
Styles of data analysis
43
Statistical models
78
4
101
5
144
Multiple linear regression
173
Exploiting the linear model framework
219
Generalized linear models and survival analysis
246
Multilevel models and repeated measures
301
Treebased classification and regression
350
Regression on principal component or discriminant scores
408
The R system additional topics
421
Epilogue models
470
Index of Terms
491
Index of Authors
501
Copyright

Time series models
286

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Page 478 - Smyth, GK 2004. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments.
Page 474 - Selection bias in gene extraction on the basis of microarray gene-expression data.

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About the author (2006)

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.

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