[jira] [Commented] (MATH-814) Kendalls Tau Implementation
[ https://issues.apache.org/jira/browse/MATH-814?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=13810722#comment-13810722 ] Thomas Neidhart commented on MATH-814: -- Hi Matt, added your patch in r1537660 with some modifications: * use FastMath instead of Math * javadoc formatting and linewidth * use same class interface as other correlations * use existing Pair class instead of ComparablePair * simplify the correlation method to only support double[] atm (may be extended if needed) * added testcases for longley and swiss fertility data sets based on our correlation testsuite for R Thanks a lot for your contribution! The other points wrt a commons base class / interface are perfectly valid and I would be very much in favor. It should be fairly easy to introduce an abstract base class Correlation for the 3 implementations that we have right now. btw. we prefer abstract base classes over interfaces, as they are more or less the same as interfaces, but make it possible to extend without breaking compatibility. We should create a separate issue for this, feel free to work on this already and provide a patch if you are interested. Kendalls Tau Implementation --- Key: MATH-814 URL: https://issues.apache.org/jira/browse/MATH-814 Project: Commons Math Issue Type: New Feature Affects Versions: 4.0 Environment: All Reporter: devl Assignee: Phil Steitz Labels: correlation, rank Fix For: 4.0 Attachments: kendalls-tau.patch Original Estimate: 840h Remaining Estimate: 840h Implement the Kendall's Tau which is a measure of Association/Correlation between ranked ordinal data. A basic description is available at http://en.wikipedia.org/wiki/Kendall_tau_rank_correlation_coefficient however the test implementation will follow that defined by Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Edition, Page 1393 Test 30, ISBN-10: 1439858012 | ISBN-13: 978-1439858011. The algorithm is proposed as follows. Given two rankings or permutations represented by a 2D matrix; columns indicate rankings (e.g. by an individual) and row are observations of each rank. The algorithm is to calculate the total number of concordant pairs of ranks (between columns), discordant pairs of ranks (between columns) and calculate the Tau defined as tau= (Number of concordant - number of discordant)/(n(n-1)/2) where n(n-1)/2 is the total number of possible pairs of ranks. The method will then output the tau value between -1 and 1 where 1 signifies a perfect correlation between the two ranked lists. Where ties exist within a ranking it is marked as neither concordant nor discordant in the calculation. An optional merge sort can be used to speed up the implementation. Details are in the wiki page. Although this implementation is not particularly complex it would be useful to have it in a consistent format in the commons math package in addition to existing correlation tests. Kendall's Tau is used effectively in comparing ranks for products, rankings from search engines or measurements from engineering equipment. -- This message was sent by Atlassian JIRA (v6.1#6144)
[jira] [Commented] (MATH-814) Kendalls Tau Implementation
[ https://issues.apache.org/jira/browse/MATH-814?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=13808595#comment-13808595 ] Matt Adereth commented on MATH-814: --- Apologies in advance for not being consistent with SpearmansCorrelation. I couldn't bring myself to make these methods non-static or to use the approach of having the constructor take the data. If this is a problem, I have no issue making the requisite change. Another consideration that I didn't do, but would be willing to try, would be to make a new Correlation interface that exposes the methods that should be common to Spearmans, Kendalls, and Pearsons. It would probably make sense to then have an AbstractCorrelation base class that handles the sheparding of data between Matrix, double[][], and double[], double[]. Finally, if you do think a Correlation interface makes sense, I'd also like to propose a NonParametricCorrelation interface for Spearmans and Kendalls which would have an additional method for computing the Correlation between two ListComparable objects. Kendalls Tau Implementation --- Key: MATH-814 URL: https://issues.apache.org/jira/browse/MATH-814 Project: Commons Math Issue Type: New Feature Affects Versions: 4.0 Environment: All Reporter: devl Assignee: Phil Steitz Labels: correlation, rank Fix For: 4.0 Attachments: kendalls-tau.patch Original Estimate: 840h Remaining Estimate: 840h Implement the Kendall's Tau which is a measure of Association/Correlation between ranked ordinal data. A basic description is available at http://en.wikipedia.org/wiki/Kendall_tau_rank_correlation_coefficient however the test implementation will follow that defined by Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Edition, Page 1393 Test 30, ISBN-10: 1439858012 | ISBN-13: 978-1439858011. The algorithm is proposed as follows. Given two rankings or permutations represented by a 2D matrix; columns indicate rankings (e.g. by an individual) and row are observations of each rank. The algorithm is to calculate the total number of concordant pairs of ranks (between columns), discordant pairs of ranks (between columns) and calculate the Tau defined as tau= (Number of concordant - number of discordant)/(n(n-1)/2) where n(n-1)/2 is the total number of possible pairs of ranks. The method will then output the tau value between -1 and 1 where 1 signifies a perfect correlation between the two ranked lists. Where ties exist within a ranking it is marked as neither concordant nor discordant in the calculation. An optional merge sort can be used to speed up the implementation. Details are in the wiki page. Although this implementation is not particularly complex it would be useful to have it in a consistent format in the commons math package in addition to existing correlation tests. Kendall's Tau is used effectively in comparing ranks for products, rankings from search engines or measurements from engineering equipment. -- This message was sent by Atlassian JIRA (v6.1#6144)
[jira] [Commented] (MATH-814) Kendalls Tau Implementation
[ https://issues.apache.org/jira/browse/MATH-814?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=13410709#comment-13410709 ] devl commented on MATH-814: --- Initial feedback from Phil Steitz I think a Kendal's Tau implementation would make a great addition to the correlation package (o.a.c.math3.stat.correlation). Here is how you can get started: 0) Get yourself set up to build commons math and run the unit tests. If you are familiar with maven, this should not be too hard. If you have any questions or run into problems checking out the sources, building locally, etc., don't hesitate to ask. 1) Look at the Spearman's implementation and the ranking classes in the stat.ranking package. That might give you some ideas on how to implement Kendal's consistently. 2) Open a JIRA ticket with the info above and start attaching patches implementing the new implementation class and associated test class. Run mvn site or checkstyle standalone to make sure your contributed code follows the style guidelines we use. 3) Be patient but persistent and we will get Kendall's Tau into commons math :) Kendalls Tau Implementation --- Key: MATH-814 URL: https://issues.apache.org/jira/browse/MATH-814 Project: Commons Math Issue Type: New Feature Affects Versions: 4.0 Environment: All Reporter: devl Labels: correlation, rank Fix For: 4.0 Original Estimate: 840h Remaining Estimate: 840h Implement the Kendall's Tau which is a measure of Association/Correlation between ranked ordinal data. A basic description is available at http://en.wikipedia.org/wiki/Kendall_tau_rank_correlation_coefficient however the test implementation will follow that defined by Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Edition, Page 1393 Test 30, ISBN-10: 1439858012 | ISBN-13: 978-1439858011. The algorithm is proposed as follows. Given two rankings or permutations represented by a 2D matrix; columns indicate rankings (e.g. by an individual) and row are observations of each rank. The algorithm is to calculate the total number of concordant pairs of ranks (between columns), discordant pairs of ranks (between columns) and calculate the Tau defined as tau= (Number of concordant - number of discordant)/(n(n-1)/2) where n(n-1)/2 is the total number of possible pairs of ranks. The method will then output the tau value between -1 and 1 where 1 signifies a perfect correlation between the two ranked lists. Where ties exist within a ranking it is marked as neither concordant nor discordant in the calculation. An optional merge sort can be used to speed up the implementation. Details are in the wiki page. Although this implementation is not particularly complex it would be useful to have it in a consistent format in the commons math package in addition to existing correlation tests. Kendall's Tau is used effectively in comparing ranks for products, rankings from search engines or measurements from engineering equipment. -- This message is automatically generated by JIRA. If you think it was sent incorrectly, please contact your JIRA administrators: https://issues.apache.org/jira/secure/ContactAdministrators!default.jspa For more information on JIRA, see: http://www.atlassian.com/software/jira
