The importance of data partitioning and the utility of bayes factors in Bayesian phylogenetics
The importance of data partitioning and the utility of bayes factors in Bayesian phylogenetics
复制标题
DOI：
10.1080/10635150701546249
复制
发表时间：
2007-01-01
影响因子：
6.5
通讯作者：
Lemmon, Alan R.
中科院分区：
文献类型：
--
作者：
Brown, Jeremy M.;Lemmon, Alan R.
随着更大、更复杂的数据集被用来推断系统发育，这些系统发育的准确性越来越需要适应分子进化过程中的异质性的进化模型。我们研究了不正确的数据划分对系统发育准确性的影响，以及贝叶斯分析中选择不同划分策略的常用方法--贝叶斯因子的第I类错误率和敏感性。我们还使用贝叶斯因子来测试经验数据，以确定是否需要以一种没有预期生物学意义的方式划分数据。当假设不正确的划分策略时，后验概率估计具有误导性。当假设的模型分区不足时，误差最大。这些结果表明，模型划分对于大型数据集是重要的。贝叶斯因子表现良好，给出了5%的I型错误率，这与标准的频率主义者假设检验非常一致。当跨类模型异质性反映经验数据的异质性时，贝叶斯因子的敏感度较高。这些结果表明，贝叶斯因子代表了一种在划分策略中进行选择的稳健方法。最后，在经验数据中包含意想不到的划分的测试结果反映了模拟结果，尽管这种测试的结果高度依赖于对不同类别之间的比率差异的考虑。最后，我们讨论了划分数据的其他方法，以及贝叶斯因子的其他应用。
As larger, more complex data sets are being used to infer phylogenies, accuracy of these phylogenies increasingly requires models of evolution that accommodate heterogeneity in the processes of molecular evolution. We investigated the effect of improper data partitioning on phylogenetic accuracy, as well as the type I error rate and sensitivity of Bayes factors, a commonly used method for choosing among different partitioning strategies in Bayesian analyses. We also used Bayes factors to test empirical data for the need to divide data in a manner that has no expected biological meaning. Posterior probability estimates are misleading when an incorrect partitioning strategy is assumed. The error was greatest when the assumed model was underpartitioned. These results suggest that model partitioning is important for large data sets. Bayes factors performed well, giving a 5% type I error rate, which is remarkably consistent with standard frequentist hypothesis tests. The sensitivity of Bayes factors was found to be quite high when the across- class model heterogeneity reflected that of empirical data. These results suggest that Bayes factors represent a robust method of choosing among partitioning strategies. Lastly, results of tests for the inclusion of unexpected divisions in empirical data mirrored the simulation results, although the outcome of such tests is highly dependent on accounting for rate variation among classes. We conclude by discussing other approaches for partitioning data, as well as other applications of Bayes factors.