The importance of proper model assumption in Bayesian phylogenetics
The importance of proper model assumption in Bayesian phylogenetics
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DOI：
10.1080/10635150490423520
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发表时间：
2004-04-01
影响因子：
6.5
通讯作者：
Moriarty, EC
中科院分区：
文献类型：
--
作者：
Lemmon, AR;Moriarty, EC
我们通过检查超过5，000个贝叶斯分析和6个核苷酸取代的嵌套模型，研究了贝叶斯遗传学背景下适当模型假设的重要性。模型误设会严重影响二分后验概率估计。当忽略率异质性时，这些偏差最为明显。在一个特定的bipartition看到的偏见的类型似乎受到强烈的影响，bipartition周围的分支的长度。在Felsenstein区，当假设模型参数不足时，二分型的后验概率估计值有偏差，但当假设模型参数过度时，后验概率估计值无偏差。然而，对于逆Felsenstein区，参数化不足和参数化过度都会导致有偏差的二分后验概率，尽管参数化过度引起的偏差不太明显，并且随着序列长度的增加而消失。模型参数估计值也受到模型误设的影响。参数化不足会导致某些参数估计值出现偏差，例如分支长度和伽玛形状参数，而参数化过度会导致某些参数估计值的精度下降。我们提醒研究人员，以确保最合适的模型是假设采用先验模型选择方法和后验模型充分性测试。
We studied the importance of proper model assumption in the context of Bayesian phylogenetics by examining > 5,000 Bayesian analyses and six nested models of nucleotide substitution. Model misspecification can strongly bias bipartition posterior probability estimates. These biases were most pronounced when rate heterogeneity was ignored. The type of bias seen at a particular bipartition appeared to be strongly influenced by the lengths of the branches surrounding that bipartition. In the Felsenstein zone, posterior probability estimates of bipartitions were biased when the assumed model was underparameterized but were unbiased when the assumed model was overparameterized. For the inverse Felsenstein zone, however, both underparameterization and overparameterization led to biased bipartition posterior probabilities, although the bias caused by overparameterization was less pronounced and disappeared with increased sequence length. Model parameter estimates were also affected by model misspecification. Underparameterization caused a bias in some parameter estimates, such as branch lengths and the gamma shape parameter, whereas overparameterization caused a decrease in the precision of some parameter estimates. We caution researchers to assure that the most appropriate model is assumed by employing both a priori model choice methods and a posteriori model adequacy tests.