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. 2020 Apr 25;69(6):1163–1179. doi: 10.1093/sysbio/syaa035

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© The Author(s) 2020. Published by Oxford University Press on behalf of the Society of Systematic Biologists.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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Figure A2. — Binary skyline model selection. We simulate 500 conditionally independent phylogenies from skyline models and test the classification ability of model selection criteria. The null model is a Kingman coalescent with , and the alternative features a sharp fall to at time units. The sampled tips of the phylogeny are introduced at and only. Graph (i) gives the probability of correct classification as a function of data size . The FIA performs best, on average, but the BIC is better at small . Graph (ii) gives the true (TPR) and false positive rates (FPR) of the metrics. The FIA and QK have the best overall rates.

Inline graphic — Binary skyline model selection. We simulate 500 conditionally independent phylogenies from skyline models and test the classification ability of model selection criteria. The null model is a Kingman coalescent with , and the alternative features a sharp fall to at time units. The sampled tips of the phylogeny are introduced at and only. Graph (i) gives the probability of correct classification as a function of data size . The FIA performs best, on average, but the BIC is better at small . Graph (ii) gives the true (TPR) and false positive rates (FPR) of the metrics. The FIA and QK have the best overall rates.