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.
"There can be no doubt that this book will become a landmark in the multidimensional scaling literature…"
-Journal of Classification, 15:143-148 (1998)
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.