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Instability of ordination results under changes in input data order: explanations and remedies
Correspondence analysis (CA) and its Detrended form (DCA) produced by the program CANOCO are unstable under reordering of the species and sites in the input data matrix. In CA, the main cause of the instability is the use of insufficiently stringent convergence criteria in the power algorithm used t...
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Published in: | Journal of vegetation science 1997-06, Vol.8 (3), p.447-454 |
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description | Correspondence analysis (CA) and its Detrended form (DCA) produced by the program CANOCO are unstable under reordering of the species and sites in the input data matrix. In CA, the main cause of the instability is the use of insufficiently stringent convergence criteria in the power algorithm used to estimate the eigenvalues. The use of stricter criteria gives results that are acceptably stable. The divisive classification program TWINSPAN uses CA based on a similar algorithm, but with extremely lax convergence criteria, and is thus susceptible to extreme instability. We detected an order-dependent programming error in the non-linear rescaling procedure that forms part of DCA. When this bug is corrected, much of the instability in DCA disappears. The stability of DCA solutions is further enhanced by the use of strict convergence criteria. In our trials, much of the instability occurred in axes 3 and 4, but ine should not assume that published two-dimensional ordinations are sufficiently accurate. Data sets which have pairs of almost equal eigenvalues among the first three axes could suffer from marked instability in the first two dimensions. We recommend that a debugged, strict version of CANOCO be released. Meanwhile, users can check the stability of their CA and DCA ordinations using the software that we have made available on the World Wide Web (http:// www.helsinki.fi/~jhoksane/). An accurate program for CA, a debugged, strict version of DECORANA (for DCA) and a strict version of TWINSPAN are also available at our site. |
doi_str_mv | 10.2307/3237336 |
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In CA, the main cause of the instability is the use of insufficiently stringent convergence criteria in the power algorithm used to estimate the eigenvalues. The use of stricter criteria gives results that are acceptably stable. The divisive classification program TWINSPAN uses CA based on a similar algorithm, but with extremely lax convergence criteria, and is thus susceptible to extreme instability. We detected an order-dependent programming error in the non-linear rescaling procedure that forms part of DCA. When this bug is corrected, much of the instability in DCA disappears. The stability of DCA solutions is further enhanced by the use of strict convergence criteria. In our trials, much of the instability occurred in axes 3 and 4, but ine should not assume that published two-dimensional ordinations are sufficiently accurate. Data sets which have pairs of almost equal eigenvalues among the first three axes could suffer from marked instability in the first two dimensions. We recommend that a debugged, strict version of CANOCO be released. Meanwhile, users can check the stability of their CA and DCA ordinations using the software that we have made available on the World Wide Web (http:// www.helsinki.fi/~jhoksane/). An accurate program for CA, a debugged, strict version of DECORANA (for DCA) and a strict version of TWINSPAN are also available at our site.</description><identifier>ISSN: 1100-9233</identifier><identifier>EISSN: 1654-1103</identifier><identifier>DOI: 10.2307/3237336</identifier><language>eng</language><publisher>Oxford, UK: Blackwell Publishing Ltd</publisher><subject>Algorithm ; Algorithms ; cluster analysis ; Clustering ; Computer software ; correlation analysis ; Correspondence analysis ; Datasets ; Detrended correspondence analysis ; Eigenanalysis ; Eigenvalues ; Eigenvectors ; equations ; Fortran ; Forum ; Input data ; mathematical models ; Non-linear rescaling ; Ordination ; plant ecology ; Polls ; Software bugs ; statistical analysis ; theory ; Tolerance</subject><ispartof>Journal of vegetation science, 1997-06, Vol.8 (3), p.447-454</ispartof><rights>Copyright 1997 IAVS; Opulus Press Uppsala</rights><rights>1997 IAVS ‐ the International Association of Vegetation Science</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3427-a2ac073606933785dfb748ae17c1caf3f07bf42f3f6d0b5bf5dd328dde48b1853</citedby><cites>FETCH-LOGICAL-c3427-a2ac073606933785dfb748ae17c1caf3f07bf42f3f6d0b5bf5dd328dde48b1853</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/3237336$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/3237336$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,780,784,27923,27924,58237,58470</link.rule.ids></links><search><creatorcontrib>Oksanen, Jari</creatorcontrib><creatorcontrib>Minchin, Peter R.</creatorcontrib><title>Instability of ordination results under changes in input data order: explanations and remedies</title><title>Journal of vegetation science</title><description>Correspondence analysis (CA) and its Detrended form (DCA) produced by the program CANOCO are unstable under reordering of the species and sites in the input data matrix. In CA, the main cause of the instability is the use of insufficiently stringent convergence criteria in the power algorithm used to estimate the eigenvalues. The use of stricter criteria gives results that are acceptably stable. The divisive classification program TWINSPAN uses CA based on a similar algorithm, but with extremely lax convergence criteria, and is thus susceptible to extreme instability. We detected an order-dependent programming error in the non-linear rescaling procedure that forms part of DCA. When this bug is corrected, much of the instability in DCA disappears. The stability of DCA solutions is further enhanced by the use of strict convergence criteria. In our trials, much of the instability occurred in axes 3 and 4, but ine should not assume that published two-dimensional ordinations are sufficiently accurate. Data sets which have pairs of almost equal eigenvalues among the first three axes could suffer from marked instability in the first two dimensions. We recommend that a debugged, strict version of CANOCO be released. Meanwhile, users can check the stability of their CA and DCA ordinations using the software that we have made available on the World Wide Web (http:// www.helsinki.fi/~jhoksane/). An accurate program for CA, a debugged, strict version of DECORANA (for DCA) and a strict version of TWINSPAN are also available at our site.</description><subject>Algorithm</subject><subject>Algorithms</subject><subject>cluster analysis</subject><subject>Clustering</subject><subject>Computer software</subject><subject>correlation analysis</subject><subject>Correspondence analysis</subject><subject>Datasets</subject><subject>Detrended correspondence analysis</subject><subject>Eigenanalysis</subject><subject>Eigenvalues</subject><subject>Eigenvectors</subject><subject>equations</subject><subject>Fortran</subject><subject>Forum</subject><subject>Input data</subject><subject>mathematical models</subject><subject>Non-linear rescaling</subject><subject>Ordination</subject><subject>plant ecology</subject><subject>Polls</subject><subject>Software bugs</subject><subject>statistical analysis</subject><subject>theory</subject><subject>Tolerance</subject><issn>1100-9233</issn><issn>1654-1103</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1997</creationdate><recordtype>article</recordtype><recordid>eNp1kFFLwzAUhYMoOKf4BwTz5oNUk9w26XyToXMy9WFugg-GtElmZteOpMPt39tRmU_ChXvgfOfCPQidUnLFgIhrYCAA-B7qUJ7EEaUE9htNCYl6DOAQHYUwJ4SKHqcd9DEsQ60yV7h6gyuLK69dqWpXldibsCrqgFelNh7nn6qcmYBd2cxyVWOtarXFjb_BZr0sVBsLWJW6yS6MdiYcowOrimBOfncXTe7vXvsP0ehlMOzfjqIcYiYixVROBHDCewAiTbTNRJwqQ0VOc2XBEpHZmDWCa5IlmU20BpZqbeI0o2kCXXTR3s19FYI3Vi69Wyi_kZTIbS3yt5aGvGzJb1eYzX-YfJyO00Z10VlLz0Nd-R39dyxqbRdqs97Zyn9JLkAk8u15IKcc-Pv4iUho-POWt6qSauZdkJMxIxQIS5uHezH8AJUdhIo</recordid><startdate>199706</startdate><enddate>199706</enddate><creator>Oksanen, Jari</creator><creator>Minchin, Peter R.</creator><general>Blackwell Publishing Ltd</general><general>Opulus Press</general><scope>FBQ</scope><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>199706</creationdate><title>Instability of ordination results under changes in input data order: explanations and remedies</title><author>Oksanen, Jari ; Minchin, Peter R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3427-a2ac073606933785dfb748ae17c1caf3f07bf42f3f6d0b5bf5dd328dde48b1853</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1997</creationdate><topic>Algorithm</topic><topic>Algorithms</topic><topic>cluster analysis</topic><topic>Clustering</topic><topic>Computer software</topic><topic>correlation analysis</topic><topic>Correspondence analysis</topic><topic>Datasets</topic><topic>Detrended correspondence analysis</topic><topic>Eigenanalysis</topic><topic>Eigenvalues</topic><topic>Eigenvectors</topic><topic>equations</topic><topic>Fortran</topic><topic>Forum</topic><topic>Input data</topic><topic>mathematical models</topic><topic>Non-linear rescaling</topic><topic>Ordination</topic><topic>plant ecology</topic><topic>Polls</topic><topic>Software bugs</topic><topic>statistical analysis</topic><topic>theory</topic><topic>Tolerance</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Oksanen, Jari</creatorcontrib><creatorcontrib>Minchin, Peter R.</creatorcontrib><collection>AGRIS</collection><collection>Istex</collection><collection>CrossRef</collection><jtitle>Journal of vegetation science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Oksanen, Jari</au><au>Minchin, Peter R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Instability of ordination results under changes in input data order: explanations and remedies</atitle><jtitle>Journal of vegetation science</jtitle><date>1997-06</date><risdate>1997</risdate><volume>8</volume><issue>3</issue><spage>447</spage><epage>454</epage><pages>447-454</pages><issn>1100-9233</issn><eissn>1654-1103</eissn><abstract>Correspondence analysis (CA) and its Detrended form (DCA) produced by the program CANOCO are unstable under reordering of the species and sites in the input data matrix. In CA, the main cause of the instability is the use of insufficiently stringent convergence criteria in the power algorithm used to estimate the eigenvalues. The use of stricter criteria gives results that are acceptably stable. The divisive classification program TWINSPAN uses CA based on a similar algorithm, but with extremely lax convergence criteria, and is thus susceptible to extreme instability. We detected an order-dependent programming error in the non-linear rescaling procedure that forms part of DCA. When this bug is corrected, much of the instability in DCA disappears. The stability of DCA solutions is further enhanced by the use of strict convergence criteria. In our trials, much of the instability occurred in axes 3 and 4, but ine should not assume that published two-dimensional ordinations are sufficiently accurate. Data sets which have pairs of almost equal eigenvalues among the first three axes could suffer from marked instability in the first two dimensions. We recommend that a debugged, strict version of CANOCO be released. Meanwhile, users can check the stability of their CA and DCA ordinations using the software that we have made available on the World Wide Web (http:// www.helsinki.fi/~jhoksane/). An accurate program for CA, a debugged, strict version of DECORANA (for DCA) and a strict version of TWINSPAN are also available at our site.</abstract><cop>Oxford, UK</cop><pub>Blackwell Publishing Ltd</pub><doi>10.2307/3237336</doi><tpages>8</tpages></addata></record> |
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source | Wiley:Jisc Collections:Wiley Read and Publish Open Access 2024-2025 (reading list); JSTOR Archival Journals and Primary Sources Collection |
subjects | Algorithm Algorithms cluster analysis Clustering Computer software correlation analysis Correspondence analysis Datasets Detrended correspondence analysis Eigenanalysis Eigenvalues Eigenvectors equations Fortran Forum Input data mathematical models Non-linear rescaling Ordination plant ecology Polls Software bugs statistical analysis theory Tolerance |
title | Instability of ordination results under changes in input data order: explanations and remedies |
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