Ordination orgradient analysis, inmultivariate analysis, is a method complementary todata clustering, and used mainly inexploratory data analysis (rather than inhypothesis testing). In contrast to cluster analysis, ordinationorders quantities in a (usually lower-dimensional) latent space. In the ordination space, quantities that are near each other share attributes (i.e., are similar to some degree), and dissimilar objects are farther from each other. Such relationships between the objects, on each of several axes orlatent variables, are then characterized numerically and/or graphically in abiplot.
Ordination methods can broadly be categorized in eigenvector-, algorithm-, or model-based methods. Many classical ordination techniques, including principal components analysis,correspondence analysis (CA) and its derivatives (detrended correspondence analysis, canonical correspondence analysis, andredundancy analysis, belong to the first group).
The third group includes model-based ordination methods, which can be considered as multivariate extensions ofGeneralized Linear Models.[1][2][3][4] Model-based ordination methods are more flexible in their application than classical ordination methods, so that it is for example possible to include random-effects.[5] Unlike in the aforementioned two groups, there is no (implicit or explicit) distance measure in the ordination. Instead, a distribution needs to be specified for the responses as is typical for statistical models. These and other assumptions, such as the assumed mean-variance relationship, can be validated with the use of residual diagnostics, unlike in other ordination methods.
Ordination can be used on the analysis of any set of multivariate objects. It is frequently used in several environmental or ecological sciences, particularly plantcommunity ecology. It is also used ingenetics andsystems biology formicroarray data analysis and inpsychometrics.
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
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Pagani et al., 2015.Methodi Ordinatio: a proposed methodology to select and rank relevant scientific papers encompassing the impact factor, number of citation, and year of publication. Scientometrics, December 2015, Volume 105, Issue 3, pp 2109–2135.
