Overview
- Supports the assertion that multiple views of data have a greater prospect of revealing prominent patterns than single views
- This book provides a portal into the realms of the open source data analysis system called R though exposition by example
- The authors formulate approaches that are operable in R that support preliminary and progressive prioritization
Part of the book series: Environmental and Ecological Statistics (ENES, volume 6)
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Table of contents (14 chapters)
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Front Matter
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Motivation and Computation
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Synergistic Scalings, Contingent Clustering and Distance Domains
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Front Matter
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Precedence and Progressive Prioritization
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Front Matter
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Transformation Techniques and Virtual Variates
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Front Matter
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Back Matter
About this book
This monograph is multivariate, multi-perspective and multipurpose. We intend to be innovatively integrative through statistical synthesis. Innovation requires capacity to operate in ways that are not ordinary, which means that conventional computations and generic graphics will not meet the needs of an adaptive approach. Flexible formulation and special schematics are essential elements that must be manageable and economical.
Authors and Affiliations
Bibliographic Information
Book Title: Multivariate Methods of Representing Relations in R for Prioritization Purposes
Book Subtitle: Selective Scaling, Comparative Clustering, Collective Criteria and Sequenced Sets
Authors: Wayne L. Myers, Ganapati P. Patil
Series Title: Environmental and Ecological Statistics
DOI: https://doi.org/10.1007/978-1-4614-3122-0
Publisher: Springer New York, NY
eBook Packages: Earth and Environmental Science, Earth and Environmental Science (R0)
Copyright Information: Springer Science+Business Media, LLC 2012
Hardcover ISBN: 978-1-4614-3121-3Published: 24 March 2012
Softcover ISBN: 978-1-4899-9028-0Published: 13 April 2014
eBook ISBN: 978-1-4614-3122-0Published: 24 March 2012
Series ISSN: 2363-9660
Series E-ISSN: 2363-9679
Edition Number: 1
Number of Pages: XVIII, 298
Topics: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Statistics for Life Sciences, Medicine, Health Sciences