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{{short description|Statistical method}}
{{short description|Statistical method}}
'''Ordination''' or '''gradient analysis''', in [[multivariate analysis]], is a method complementary to [[data clustering]], and used mainly in [[exploratory data analysis]] (rather than in [[hypothesis testing]]). Ordination [[partially ordered set|orders]] objects that are characterized by values on multiple variables (multivariate objects) so that similar objects are near each other and dissimilar objects are farther from each other. Such 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]] (CA) and its derivatives ([[detrended correspondence analysis|detrended CA (DCA)]], canonical CA (CCA)), [[Bray&ndash;Curtis ordination]], and [[redundancy analysis]] (RDA), among others. Contemporary developments for ordination focus on machine learning techniques<ref>{{Cite journal |last1=Milošević |first1=Djuradj |last2=Medeiros |first2=Andrew S. |last3=Stojković Piperac |first3=Milica |last4=Cvijanović |first4=Dušanka |last5=Soininen |first5=Janne |last6=Milosavljević |first6=Aleksandar |last7=Predić |first7=Bratislav |date=2022-04-01 |title=The application of Uniform Manifold Approximation and Projection (UMAP) for unconstrained ordination and classification of biological indicators in aquatic ecology |url=https://www.sciencedirect.com/science/article/pii/S0048969721074428 |journal=Science of the Total Environment |language=en |volume=815 |pages=152365 |doi=10.1016/j.scitotenv.2021.152365 |pmid=34963591 |s2cid=245497943 |issn=0048-9697}}</ref> or using statistical models instead.<ref>{{Cite journal |last1=Hui |first1=Francis K.C. |last2=Taskinen |first2=Sara |last3=Pledger |first3=Shirley |last4=Foster |first4=Scott D. |last5=Warton |first5=David I. |date=2015 |editor-last=O'Hara |editor-first=Robert B. |title=Model‐based approaches to unconstrained ordination |url=https://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12236 |journal=Methods in Ecology and Evolution |language=en |volume=6 |issue=4 |pages=399–411 |doi=10.1111/2041-210X.12236 |s2cid=62624917 |issn=2041-210X}}</ref><ref>{{Cite journal |last1=Warton |first1=David I. |last2=Blanchet |first2=F. Guillaume |last3=O’Hara |first3=Robert B. |last4=Ovaskainen |first4=Otso |last5=Taskinen |first5=Sara |last6=Walker |first6=Steven C. |last7=Hui |first7=Francis K. C. |date=2015-12-01 |title=So Many Variables: Joint Modeling in Community Ecology |url=https://www.sciencedirect.com/science/article/pii/S0169534715002402 |journal=Trends in Ecology & Evolution |language=en |volume=30 |issue=12 |pages=766–779 |doi=10.1016/j.tree.2015.09.007 |pmid=26519235 |issn=0169-5347}}</ref><ref>{{Cite journal |last=Yee |first=Thomas W. |title=A New Technique for Maximum-Likelihood Canonical Gaussian Ordination |date=2004 |url=http://doi.wiley.com/10.1890/03-0078 |journal=Ecological Monographs |language=en |volume=74 |issue=4 |pages=685–701 |doi=10.1890/03-0078 |issn=0012-9615}}</ref>
'''Ordination''' or '''gradient analysis''', in [[multivariate analysis]], is a method complementary to [[data clustering]], and used mainly in [[exploratory data analysis]] (rather than in [[hypothesis testing]]). Ordination [[partially ordered set|orders]] objects that are characterized by values on multiple variables (multivariate objects) so that similar objects are near each other and dissimilar objects are farther from each other. Such 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]] (CA) and its derivatives ([[detrended correspondence analysis|detrended CA (DCA)]], canonical CA (CCA)), [[Bray&ndash;Curtis ordination]], and [[redundancy analysis]] (RDA), among others. Contemporary developments for ordination focus on machine learning techniques<ref>{{Cite journal |last1=Milošević |first1=Djuradj |last2=Medeiros |first2=Andrew S. |last3=Stojković Piperac |first3=Milica |last4=Cvijanović |first4=Dušanka |last5=Soininen |first5=Janne |last6=Milosavljević |first6=Aleksandar |last7=Predić |first7=Bratislav |date=2022-04-01 |title=The application of Uniform Manifold Approximation and Projection (UMAP) for unconstrained ordination and classification of biological indicators in aquatic ecology |url=https://www.sciencedirect.com/science/article/pii/S0048969721074428 |journal=Science of the Total Environment |language=en |volume=815 |pages=152365 |doi=10.1016/j.scitotenv.2021.152365 |pmid=34963591 |s2cid=245497943 |issn=0048-9697}}</ref> or using statistical models instead.<ref>{{Cite journal |last1=Hui |first1=Francis K.C. |last2=Taskinen |first2=Sara |last3=Pledger |first3=Shirley |last4=Foster |first4=Scott D. |last5=Warton |first5=David I. |date=2015 |editor-last=O'Hara |editor-first=Robert B. |title=Model‐based approaches to unconstrained ordination |url=https://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12236 |journal=Methods in Ecology and Evolution |language=en |volume=6 |issue=4 |pages=399–411 |doi=10.1111/2041-210X.12236 |s2cid=62624917 |issn=2041-210X|doi-access=free }}</ref><ref>{{Cite journal |last1=Warton |first1=David I. |last2=Blanchet |first2=F. Guillaume |last3=O’Hara |first3=Robert B. |last4=Ovaskainen |first4=Otso |last5=Taskinen |first5=Sara |last6=Walker |first6=Steven C. |last7=Hui |first7=Francis K. C. |date=2015-12-01 |title=So Many Variables: Joint Modeling in Community Ecology |url=https://www.sciencedirect.com/science/article/pii/S0169534715002402 |journal=Trends in Ecology & Evolution |language=en |volume=30 |issue=12 |pages=766–779 |doi=10.1016/j.tree.2015.09.007 |pmid=26519235 |issn=0169-5347}}</ref><ref>{{Cite journal |last=Yee |first=Thomas W. |title=A New Technique for Maximum-Likelihood Canonical Gaussian Ordination |date=2004 |url=http://doi.wiley.com/10.1890/03-0078 |journal=Ecological Monographs |language=en |volume=74 |issue=4 |pages=685–701 |doi=10.1890/03-0078 |issn=0012-9615}}</ref>


==Applications==
==Applications==

Revision as of 05:12, 29 January 2023

Ordination or gradient analysis, in multivariate analysis, 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 (multivariate objects) so that similar objects are near each other and dissimilar objects are farther from each other. Such 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 (CA) and its derivatives (detrended CA (DCA), canonical CA (CCA)), Bray–Curtis ordination, and redundancy analysis (RDA), among others. Contemporary developments for ordination focus on machine learning techniques[1] or using statistical models instead.[2][3][4]

Applications

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.

See also

References

  1. ^ Milošević, Djuradj; Medeiros, Andrew S.; Stojković Piperac, Milica; Cvijanović, Dušanka; Soininen, Janne; Milosavljević, Aleksandar; Predić, Bratislav (2022-04-01). "The application of Uniform Manifold Approximation and Projection (UMAP) for unconstrained ordination and classification of biological indicators in aquatic ecology". Science of the Total Environment. 815: 152365. doi:10.1016/j.scitotenv.2021.152365. ISSN 0048-9697. PMID 34963591. S2CID 245497943.
  2. ^ Hui, Francis K.C.; Taskinen, Sara; Pledger, Shirley; Foster, Scott D.; Warton, David I. (2015). O'Hara, Robert B. (ed.). "Model‐based approaches to unconstrained ordination". Methods in Ecology and Evolution. 6 (4): 399–411. doi:10.1111/2041-210X.12236. ISSN 2041-210X. S2CID 62624917.
  3. ^ Warton, David I.; Blanchet, F. Guillaume; O’Hara, Robert B.; Ovaskainen, Otso; Taskinen, Sara; Walker, Steven C.; Hui, Francis K. C. (2015-12-01). "So Many Variables: Joint Modeling in Community Ecology". Trends in Ecology & Evolution. 30 (12): 766–779. doi:10.1016/j.tree.2015.09.007. ISSN 0169-5347. PMID 26519235.
  4. ^ Yee, Thomas W. (2004). "A New Technique for Maximum-Likelihood Canonical Gaussian Ordination". Ecological Monographs. 74 (4): 685–701. doi:10.1890/03-0078. ISSN 0012-9615.
  • 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
  • Braak, C.J.F. ter & I.C. Prentice 1988 A theory of gradient analysis. Adv. Ecol. Res. 18:271-313.
  • 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.
  • 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.

Further reading

  1. General
  2. Specific Techniques
  3. Software
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