Ordination (statistics)

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In multivariate analysis, ordination is a method complementary to data clustering, and used mainly in exploratory data analysis (rather than in hypothesis testing). Ordination orders objects that are characterized by values on multiple variables (i.e., multivariate objects) so that similar objects are near each other and dissimilar objects are further from each other. These relationships between the objects, on each of several axes (one for each variable), are then characterized numerically and/or graphically. Many ordination techniques exist, including principal components analysis (PCA), non-metric multidimensional scaling (NMDS), correspondence analysis and its derivatives (CA, DCA, CCA), Bray-Curtis ordination, and redundancy analysis (RDA), among others.

1 Uses
2 See also
3 References
4 External links

[edit] Uses

Ordination can be used on the analysis of any set of multivariate objects. It is frequently used in several environmental or ecological sciences, particularly plant community ecology. It is also used in genetics and systems biology for microarray data analysis, and in psychometrics.

[edit] See also

[edit] References

Gauch, H. G., Jr. 1982. Multivariate Analysis in Community Ecology. Cambridge University Press, Cambridge.
Jongman et al, 1995. Data Analysis in Community and Landscape Ecology. Cambridge University Press, Cambridge.
Birks, H.J.B, 1998. An Annotated Bibliography Of Canonical Correspondence Analysis And Related Constrained Ordination Methods 1986-1996. Botanical Institute, University of Bergen. World Wide Web: http://www.bio.umontreal.ca/Casgrain/cca_bib/index.html