Graphs in phylogenetic comparative analysis: Anscombe's quartet revisited
In 1973, the statistician Francis Anscombe used a clever set of bivariate datasets (now known as Anscombe's quartet) to illustrate the importance of graphing data as a component of statistical analyses. In his example, each of the four datasets yielded identical regression coefficients and mode...
- Autores:
-
Revell, Liam J.
Schliep, Klaus
Valderrama, Eugenio
Richardson, James E.
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Universidad El Bosque
- Repositorio:
- Repositorio U. El Bosque
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unbosque.edu.co:20.500.12495/3625
- Acceso en línea:
- http://hdl.handle.net/20.500.12495/3625
https://doi.org/10.1111/2041-210X.13067
https://repositorio.unbosque.edu.co
- Palabra clave:
- Comparative methods
Macroevolution
Phylogeny
Plotting
Visualization
- Rights
- openAccess
- License
- Acceso abierto
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|
dc.title.spa.fl_str_mv |
Graphs in phylogenetic comparative analysis: Anscombe's quartet revisited |
dc.title.translated.spa.fl_str_mv |
Graphs in phylogenetic comparative analysis: Anscombe's quartet revisited |
title |
Graphs in phylogenetic comparative analysis: Anscombe's quartet revisited |
spellingShingle |
Graphs in phylogenetic comparative analysis: Anscombe's quartet revisited Comparative methods Macroevolution Phylogeny Plotting Visualization |
title_short |
Graphs in phylogenetic comparative analysis: Anscombe's quartet revisited |
title_full |
Graphs in phylogenetic comparative analysis: Anscombe's quartet revisited |
title_fullStr |
Graphs in phylogenetic comparative analysis: Anscombe's quartet revisited |
title_full_unstemmed |
Graphs in phylogenetic comparative analysis: Anscombe's quartet revisited |
title_sort |
Graphs in phylogenetic comparative analysis: Anscombe's quartet revisited |
dc.creator.fl_str_mv |
Revell, Liam J. Schliep, Klaus Valderrama, Eugenio Richardson, James E. |
dc.contributor.author.none.fl_str_mv |
Revell, Liam J. Schliep, Klaus Valderrama, Eugenio Richardson, James E. |
dc.subject.keywords.spa.fl_str_mv |
Comparative methods Macroevolution Phylogeny Plotting Visualization |
topic |
Comparative methods Macroevolution Phylogeny Plotting Visualization |
description |
In 1973, the statistician Francis Anscombe used a clever set of bivariate datasets (now known as Anscombe's quartet) to illustrate the importance of graphing data as a component of statistical analyses. In his example, each of the four datasets yielded identical regression coefficients and model fits, and yet when visualized revealed strikingly different patterns of covariation between x and y. Phylogenetic comparative methods (the set of methodologies that use phylogenies, often combined with phenotypic trait data, to make inferences about evolution) are statistical methods too; yet visualizing the data and phylogeny in a sensible way that would permit us to detect unexpected patterns or unanticipated deviations from model assumptions is not a routine component of phylogenetic comparative analyses. Here, we use a quartet of phylogenetic datasets to illustrate that the same estimated parameters and model fits can be obtained from data that were generated using markedly different procedures—including pure Brownian motion evolution and randomly selected data uncorrelated with the tree. Just as in the case of Anscombe's quartet, when graphed the differences between the four datasets are quickly revealed. The intent of this article is to help build the general case that phylogenetic comparative methods are statistical methods and consequently that graphing or visualization should invariably be included as an essential step in our standard data analytical pipelines. Phylogenies are complex data structures and thus visualizing data on trees in a meaningful and useful way is a challenging endeavour. We recommend that the development of graphical methods for simultaneously visualizing data and tree should continue to be an important goal in phylogenetic comparative biology. |
publishDate |
2020 |
dc.date.accessioned.none.fl_str_mv |
2020-07-30T17:15:53Z |
dc.date.available.none.fl_str_mv |
2020-07-30T17:15:53Z |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.local.none.fl_str_mv |
Artículo de revista |
dc.type.coar.none.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/article |
format |
http://purl.org/coar/resource_type/c_6501 |
dc.identifier.issn.none.fl_str_mv |
2041-210X |
dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/20.500.12495/3625 |
dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1111/2041-210X.13067 |
dc.identifier.instname.spa.fl_str_mv |
instname:Universidad El Bosque |
dc.identifier.reponame.spa.fl_str_mv |
reponame:Repositorio Institucional Universidad El Bosque |
dc.identifier.repourl.none.fl_str_mv |
https://repositorio.unbosque.edu.co |
identifier_str_mv |
2041-210X instname:Universidad El Bosque reponame:Repositorio Institucional Universidad El Bosque |
url |
http://hdl.handle.net/20.500.12495/3625 https://doi.org/10.1111/2041-210X.13067 https://repositorio.unbosque.edu.co |
dc.language.iso.none.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartofseries.spa.fl_str_mv |
Methods in Ecology and Evolution, 2041-210X, Vol 9, Nro. 10, 2018, p. 2145-2154 |
dc.relation.uri.none.fl_str_mv |
https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13067 |
dc.rights.local.spa.fl_str_mv |
Acceso abierto |
dc.rights.accessrights.none.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess Acceso abierto |
dc.rights.creativecommons.none.fl_str_mv |
2018-07-16 |
rights_invalid_str_mv |
Acceso abierto http://purl.org/coar/access_right/c_abf2 2018-07-16 |
eu_rights_str_mv |
openAccess |
dc.format.mimetype.none.fl_str_mv |
application/pdf |
dc.publisher.spa.fl_str_mv |
Wiley |
dc.publisher.journal.spa.fl_str_mv |
Methods in Ecology and Evolution |
institution |
Universidad El Bosque |
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Revell, Liam J.Schliep, KlausValderrama, EugenioRichardson, James E.2020-07-30T17:15:53Z2020-07-30T17:15:53Z2041-210Xhttp://hdl.handle.net/20.500.12495/3625https://doi.org/10.1111/2041-210X.13067instname:Universidad El Bosquereponame:Repositorio Institucional Universidad El Bosquehttps://repositorio.unbosque.edu.coapplication/pdfengWileyMethods in Ecology and EvolutionMethods in Ecology and Evolution, 2041-210X, Vol 9, Nro. 10, 2018, p. 2145-2154https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13067Graphs in phylogenetic comparative analysis: Anscombe's quartet revisitedGraphs in phylogenetic comparative analysis: Anscombe's quartet revisitedArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85Comparative methodsMacroevolutionPhylogenyPlottingVisualizationIn 1973, the statistician Francis Anscombe used a clever set of bivariate datasets (now known as Anscombe's quartet) to illustrate the importance of graphing data as a component of statistical analyses. In his example, each of the four datasets yielded identical regression coefficients and model fits, and yet when visualized revealed strikingly different patterns of covariation between x and y. Phylogenetic comparative methods (the set of methodologies that use phylogenies, often combined with phenotypic trait data, to make inferences about evolution) are statistical methods too; yet visualizing the data and phylogeny in a sensible way that would permit us to detect unexpected patterns or unanticipated deviations from model assumptions is not a routine component of phylogenetic comparative analyses. Here, we use a quartet of phylogenetic datasets to illustrate that the same estimated parameters and model fits can be obtained from data that were generated using markedly different procedures—including pure Brownian motion evolution and randomly selected data uncorrelated with the tree. Just as in the case of Anscombe's quartet, when graphed the differences between the four datasets are quickly revealed. The intent of this article is to help build the general case that phylogenetic comparative methods are statistical methods and consequently that graphing or visualization should invariably be included as an essential step in our standard data analytical pipelines. Phylogenies are complex data structures and thus visualizing data on trees in a meaningful and useful way is a challenging endeavour. We recommend that the development of graphical methods for simultaneously visualizing data and tree should continue to be an important goal in phylogenetic comparative biology.Acceso abiertohttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessAcceso abierto2018-07-16LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.unbosque.edu.co/bitstreams/fcf3f284-8f6d-44af-b395-cc9866ba73d2/download8a4605be74aa9ea9d79846c1fba20a33MD53ORIGINALRevell, Liam J..pdfRevell, Liam J..pdfapplication/pdf308883https://repositorio.unbosque.edu.co/bitstreams/3d525b8e-ee32-435a-ae16-f73150afb08b/downloadc1ba75c3bd20ae4cbda6ab693b778bcfMD54THUMBNAILRevell, Liam J..pdf.jpgRevell, Liam J..pdf.jpgIM Thumbnailimage/jpeg8775https://repositorio.unbosque.edu.co/bitstreams/feb4fe7e-ea22-4d8e-b870-25c131cad2ff/download755653d73e11732b14f0e6ce985f6e8bMD55TEXTRevell, Liam J..pdf.txtRevell, Liam J..pdf.txtExtracted texttext/plain44832https://repositorio.unbosque.edu.co/bitstreams/d555a99f-6b0b-41f0-b90a-1b39a52c9183/downloada82f0a2ea9f0c10d427530d8654b2aacMD5620.500.12495/3625oai:repositorio.unbosque.edu.co:20.500.12495/36252024-02-07 08:56:53.115restrictedhttps://repositorio.unbosque.edu.coRepositorio Institucional Universidad El Bosquebibliotecas@biteca.comTk9URTogUExBQ0UgWU9VUiBPV04gTElDRU5TRSBIRVJFClRoaXMgc2FtcGxlIGxpY2Vuc2UgaXMgcHJvdmlkZWQgZm9yIGluZm9ybWF0aW9uYWwgcHVycG9zZXMgb25seS4KCk5PTi1FWENMVVNJVkUgRElTVFJJQlVUSU9OIExJQ0VOU0UKCkJ5IHNpZ25pbmcgYW5kIHN1Ym1pdHRpbmcgdGhpcyBsaWNlbnNlLCB5b3UgKHRoZSBhdXRob3Iocykgb3IgY29weXJpZ2h0Cm93bmVyKSBncmFudHMgdG8gRFNwYWNlIFVuaXZlcnNpdHkgKERTVSkgdGhlIG5vbi1leGNsdXNpdmUgcmlnaHQgdG8gcmVwcm9kdWNlLAp0cmFuc2xhdGUgKGFzIGRlZmluZWQgYmVsb3cpLCBhbmQvb3IgZGlzdHJpYnV0ZSB5b3VyIHN1Ym1pc3Npb24gKGluY2x1ZGluZwp0aGUgYWJzdHJhY3QpIHdvcmxkd2lkZSBpbiBwcmludCBhbmQgZWxlY3Ryb25pYyBmb3JtYXQgYW5kIGluIGFueSBtZWRpdW0sCmluY2x1ZGluZyBidXQgbm90IGxpbWl0ZWQgdG8gYXVkaW8gb3IgdmlkZW8uCgpZb3UgYWdyZWUgdGhhdCBEU1UgbWF5LCB3aXRob3V0IGNoYW5naW5nIHRoZSBjb250ZW50LCB0cmFuc2xhdGUgdGhlCnN1Ym1pc3Npb24gdG8gYW55IG1lZGl1bSBvciBmb3JtYXQgZm9yIHRoZSBwdXJwb3NlIG9mIHByZXNlcnZhdGlvbi4KCllvdSBhbHNvIGFncmVlIHRoYXQgRFNVIG1heSBrZWVwIG1vcmUgdGhhbiBvbmUgY29weSBvZiB0aGlzIHN1Ym1pc3Npb24gZm9yCnB1cnBvc2VzIG9mIHNlY3VyaXR5LCBiYWNrLXVwIGFuZCBwcmVzZXJ2YXRpb24uCgpZb3UgcmVwcmVzZW50IHRoYXQgdGhlIHN1Ym1pc3Npb24gaXMgeW91ciBvcmlnaW5hbCB3b3JrLCBhbmQgdGhhdCB5b3UgaGF2ZQp0aGUgcmlnaHQgdG8gZ3JhbnQgdGhlIHJpZ2h0cyBjb250YWluZWQgaW4gdGhpcyBsaWNlbnNlLiBZb3UgYWxzbyByZXByZXNlbnQKdGhhdCB5b3VyIHN1Ym1pc3Npb24gZG9lcyBub3QsIHRvIHRoZSBiZXN0IG9mIHlvdXIga25vd2xlZGdlLCBpbmZyaW5nZSB1cG9uCmFueW9uZSdzIGNvcHlyaWdodC4KCklmIHRoZSBzdWJtaXNzaW9uIGNvbnRhaW5zIG1hdGVyaWFsIGZvciB3aGljaCB5b3UgZG8gbm90IGhvbGQgY29weXJpZ2h0LAp5b3UgcmVwcmVzZW50IHRoYXQgeW91IGhhdmUgb2J0YWluZWQgdGhlIHVucmVzdHJpY3RlZCBwZXJtaXNzaW9uIG9mIHRoZQpjb3B5cmlnaHQgb3duZXIgdG8gZ3JhbnQgRFNVIHRoZSByaWdodHMgcmVxdWlyZWQgYnkgdGhpcyBsaWNlbnNlLCBhbmQgdGhhdApzdWNoIHRoaXJkLXBhcnR5IG93bmVkIG1hdGVyaWFsIGlzIGNsZWFybHkgaWRlbnRpZmllZCBhbmQgYWNrbm93bGVkZ2VkCndpdGhpbiB0aGUgdGV4dCBvciBjb250ZW50IG9mIHRoZSBzdWJtaXNzaW9uLgoKSUYgVEhFIFNVQk1JU1NJT04gSVMgQkFTRUQgVVBPTiBXT1JLIFRIQVQgSEFTIEJFRU4gU1BPTlNPUkVEIE9SIFNVUFBPUlRFRApCWSBBTiBBR0VOQ1kgT1IgT1JHQU5JWkFUSU9OIE9USEVSIFRIQU4gRFNVLCBZT1UgUkVQUkVTRU5UIFRIQVQgWU9VIEhBVkUKRlVMRklMTEVEIEFOWSBSSUdIVCBPRiBSRVZJRVcgT1IgT1RIRVIgT0JMSUdBVElPTlMgUkVRVUlSRUQgQlkgU1VDSApDT05UUkFDVCBPUiBBR1JFRU1FTlQuCgpEU1Ugd2lsbCBjbGVhcmx5IGlkZW50aWZ5IHlvdXIgbmFtZShzKSBhcyB0aGUgYXV0aG9yKHMpIG9yIG93bmVyKHMpIG9mIHRoZQpzdWJtaXNzaW9uLCBhbmQgd2lsbCBub3QgbWFrZSBhbnkgYWx0ZXJhdGlvbiwgb3RoZXIgdGhhbiBhcyBhbGxvd2VkIGJ5IHRoaXMKbGljZW5zZSwgdG8geW91ciBzdWJtaXNzaW9uLgo= |