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Identifying model violations under the multispecies coalescent model using P2C2M.SNAPP.
Duckett, Drew J; Pelletier, Tara A; Carstens, Bryan C.
Afiliação
  • Duckett DJ; Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH, USA.
  • Pelletier TA; Biology Department, Radford University, Radford, VA, USA.
  • Carstens BC; Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH, USA.
PeerJ ; 8: e8271, 2020.
Article em En | MEDLINE | ID: mdl-31949994
Phylogenetic estimation under the multispecies coalescent model (MSCM) assumes all incongruence among loci is caused by incomplete lineage sorting. Therefore, applying the MSCM to datasets that contain incongruence that is caused by other processes, such as gene flow, can lead to biased phylogeny estimates. To identify possible bias when using the MSCM, we present P2C2M.SNAPP. P2C2M.SNAPP is an R package that identifies model violations using posterior predictive simulation. P2C2M.SNAPP uses the posterior distribution of species trees output by the software package SNAPP to simulate posterior predictive datasets under the MSCM, and then uses summary statistics to compare either the empirical data or the posterior distribution to the posterior predictive distribution to identify model violations. In simulation testing, P2C2M.SNAPP correctly classified up to 83% of datasets (depending on the summary statistic used) as to whether or not they violated the MSCM model. P2C2M.SNAPP represents a user-friendly way for researchers to perform posterior predictive model checks when using the popular SNAPP phylogenetic estimation program. It is freely available as an R package, along with additional program details and tutorials.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article