Data · dataset · 2018
Supplementary materials to 'The effect of dictionary omissions on phylogenies computationally inferred from lexical data' by Igor Yanovich.
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File S1: R code for generating pseudo-datasets for studying dictionary omissionFile S2: Python 2.7 code preparing likelihood-adjusted data for BEASTFile S3: Recoded Lezgian NEXUS file with Lezgian data from Kassian (2015).
Description
The recoding was as described in Section 2.1.File S4: Posterior clade probabilities from a MrBayes analysis analogous to Fig. 5(a).Analyses reported in Fig. 5 were performed using BEAST, while those in Fig. 6 were done using MrBayes.
Here, I provide a part of the log of a MrBayes analysis with mostly the same settings as the BEAST analysis reported in Fig. 5(a). (The two differ in using different wide priors on the speciation rate.) Comparison of the clade posteriors in File S4 (containing the output of MrBayes) shows that both software packages correctly sample from one and the same posterior, as expected. Furthermore, the tree height is almost exactly the same (BEAST: [0.1191, 0.1332] 95% HPDI, MrBayes: [0.1196, 0.1333] 95% HPDI), though the inferred speciation rates differ (BEAST: [6.8348, 18.204] 95% HPDI, MrBayes: [3.9682, 10.5462] 95% HPDI).
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The difference in the speciation rate goes in the direction we expect, given the influence of the prior (the exponential(1) prior in the MrBayes analysis depresses that rate compared to the uniform prior in the BEAST analysis).
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- DOI doi.org/10.6084/m9.figshare.6580871.v1 ↗
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Topics
- Stated by source
- Languages and literature
- Inferred from text
- Linguistic structures 78%
Related
Provenance · 1 source records, 10 field assertions
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|---|---|---|---|
| DataCite | 10.6084/m9.figshare.6580871.v1 | 13 d ago | JSON v1 |
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| concepts[field].anzsrc:field:470409 | enrichment · DataCite | taxonomy-embedding@1.1.0 | title+keywords+description (78%) |
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