Ship-Ship-Hooray! Free Shipping on $25+ Details >
Multiple Comparisons Using R

Multiple Comparisons Using R - 10 edition

Multiple Comparisons Using R - 10 edition

ISBN13: 9781584885740

ISBN10: 1584885742

Multiple Comparisons Using R by Frank Bretz - ISBN 9781584885740
Edition: 10
Copyright: 2010
Publisher: CRC Press I, LLC
Published: 2010
International: No
Multiple Comparisons Using R by Frank Bretz - ISBN 9781584885740

ISBN13: 9781584885740

ISBN10: 1584885742

Edition: 10

Summary

Adopting a unifying theme based on maximum statistics, Multiple Comparisons Using Rdescribes the common underlying theory of multiple comparison procedures through numerous examples. It also presents a detailed description of available software implementations in R. The R packages and source code for the analyses are available at http://CRAN.R-project.org

After giving examples of multiplicity problems, the book covers general concepts and basic multiple comparisons procedures, including the Bonferroni method and Simes' test. It then shows how to perform parametric multiple comparisons in standard linear models and general parametric models. It also introduces the multcomppackage in R, which offers a convenient interface to perform multiple comparisons in a general context. Following this theoretical framework, the book explores applications involving the Dunnett test, Tukey's all pairwise comparisons, and general multiple contrast tests for standard regression models, mixed-effects models, and parametric survival models. The last chapter reviews other multiple comparison procedures, such as resampling-based procedures, methods for group sequential or adaptive designs, and the combination of multiple comparison procedures with modeling techniques.

Controlling multiplicity in experiments ensures better decision making and safeguards against false claims. A self-contained introduction to multiple comparison procedures, this book offers strategies for constructing the procedures and illustrates the framework for multiple hypotheses testing in general parametric models. It is suitable for readers with R experience but limited knowledge of multiple comparison procedures and vice versa.

Top Arrow

Top