Data from: Detection of implausible phylogenetic inferences using posterior predictive assessment of model fit

Systematic phylogenetic error caused by the simplifying assumptions made in models of molecular evolution may be impossible to avoid entirely when attempting to model evolution across massive, diverse datasets. However, not all deficiencies of inference models result in unreliable phylogenetic estimates. The field of phylogenetics lacks a direct method to identify cases where model specification adversely affects inferences. Posterior predictive simulation is a flexible and intuitive approach for assessing goodness-of-fit of the assumed model and priors in a Bayesian phylogenetic analysis. Here, I propose new test statistics for use in posterior predictive assessment of model fit. These test statistics compare phylogenetic inferences from posterior predictive datasets to inferences from the original data. A simulation study demonstrates the utility of these new statistics. The new tests reject the plausibility of inferred tree lengths or topologies more often when data/model combinations produce biased inferences. I also apply this approach to exemplar empirical datasets, highlighting the value of the novel assessments.

在针对大规模多样化数据集开展进化建模时，由分子进化模型（molecular evolution models）所采用的简化假设所引发的系统性系统发育误差，或许完全无法避免。不过，并非所有推断模型的缺陷都会导致不可靠的系统发育推断结果。目前系统发育学领域尚无直接方法，可用于识别那些因模型设定不当而对推断产生负面影响的情形。后验预测模拟（posterior predictive simulation）是一种灵活且直观的方法，可用于评估贝叶斯系统发育分析（Bayesian phylogenetic analysis）中所假定模型与先验分布的拟合优度。本文提出了可用于模型拟合后验预测评估的新型检验统计量（test statistics）。这些检验统计量将后验预测数据集得到的系统发育推断结果，与原始数据得到的推断结果进行对比。一项模拟研究验证了这些新型检验统计量的实用价值。当数据与模型的组合会产生有偏推断时，新型检验会更频繁地拒绝所推断的树长或拓扑结构的合理性。本文还将该方法应用于典型经验数据集（empirical datasets），凸显了这类新型评估手段的应用价值。