
Biplots
Chapman & Hall/CRC (Publisher)
1st Edition
Published on 1. December 1995
Book
Hardback
280 pages
978-0-412-71630-0 (ISBN)
Description
Biplots are the multivariate analog of scatter plots, approximating the multivariate distribution of a sample in a few dimensions to produce a graphic display. In addition, they superimpose representations of the variables on this display so that the relationships between the sample and the variable can be studied. Like scatter plots, biplots are useful for detecting patterns and for displaying the results found by more formal methods of analysis.
In recent years the theory of biplots has been considerably extended. The approach adopted here is geometric, permitting a natural integration of well-known methods, such as components analysis, correspondence analysis, and canonical variate analysis as well as some newer and less well-known methods, such as nonlinear biplots and biadditive models.
In recent years the theory of biplots has been considerably extended. The approach adopted here is geometric, permitting a natural integration of well-known methods, such as components analysis, correspondence analysis, and canonical variate analysis as well as some newer and less well-known methods, such as nonlinear biplots and biadditive models.
Reviews / Votes
"There can be no doubt that this book will become a landmark in the multidimensional scaling literature..."-Journal of Classification, 15:143-148 (1998)
More details
Series
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Dimensions
Height: 280 mm
Width: 210 mm
Weight
408 gr
ISBN-13
978-0-412-71630-0 (9780412716300)
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Schweitzer Classification
Persons
Gower\, J.C.; Hand\, David J.
Author
Series Editor
Content
Introduction. Principle Components Analysis. Other Linear Biplots. Multiple Correspondence Analysis. Canonical Biplots. Non-Linear Biplots. Generalized Biplots. Biadditive Models. Correspondence Analysis. Relationship between Correspondence Analysis and Multiple Correspondence Analysis. Other Plots. Appendix.