Grant Taran, Kluge Arnold G
Faculdade de Biociências, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, RS, Av. Ipiranga 6681, 90619-900, Brazil.
3140 Dolph Drive, Ann Arbor, MI 48103, USA.
Cladistics. 2008 Dec;24(6):1051-1064. doi: 10.1111/j.1096-0031.2008.00231.x. Epub 2008 Aug 27.
In addition to hypothesis optimality, the evaluation of clade (group, edge, split, node) support is an important aspect of phylogenetic analysis. Here we clarify the logical relationship between support and optimality and formulate adequacy conditions for support measures. Support, S, and optimality, O, are both empirical knowledge claims about the strength of hypotheses, h , h , …h , in relation to evidence, e, given background knowledge, b. Whereas optimality refers to the absolute strength of hypotheses, support refers to the relative strength of hypotheses. Consequently, support and optimality are logically related such that they vary in direct proportion to each other, S(h | e,b) ∝ O(h | e,b). Furthermore, in order for a support measure to be objective it must quantify support as a function of explanatory power. For example, Goodman-Bremer support and ratio of explanatory power (REP) support satisfy the adequacy requirement S(h | e,b) ∝ O(h | e,b) and calculate support as a function of explanatory power. As such, these are adequate measures of objective support. The equivalent measures for statistical optimality criteria are the likelihood ratio (or log-likelihood difference) and likelihood difference support measures for maximum likelihood and the posterior probability ratio and posterior probability difference support measures for Bayesian inference. These statistical support measures satisfy the adequacy requirement S(h | e,b) ∝ O(h | e,b) and to that extent are internally consistent; however, they do not quantify support as a function of explanatory power and therefore are not measures of objective support. Neither the relative fit difference (RFD; relative GB support) nor any of the parsimony (bootstrap and jackknife character resampling) or statistical [bootstrap character resampling, Markov chain Monte Carlo (MCMC) clade frequencies] support measures based on clade frequencies satisfy the adequacy condition S(h | e,b) ∝ O(h | e,b) or calculate support as a function of explanatory power. As such, they are not adequate support measures. © The Willi Hennig Society 2008.
除了假设最优性之外，进化枝（类群、分支、分裂、节点）支持度的评估是系统发育分析的一个重要方面。在此，我们阐明了支持度与最优性之间的逻辑关系，并制定了支持度度量的充分条件。支持度S和最优性O都是关于假设h、h、…h相对于证据e以及背景知识b的强度的经验性知识主张。最优性指的是假设的绝对强度，而支持度指的是假设的相对强度。因此，支持度和最优性在逻辑上相关，它们彼此成正比变化，即S(h | e,b) ∝ O(h | e,b)。此外，为了使支持度度量具有客观性，它必须将支持度量化为解释力的函数。例如，古德曼 - 布雷默支持度和解释力比率（REP）支持度满足充分条件S(h | e,b) ∝ O(h | e,b)，并将支持度计算为解释力的函数。因此，这些是客观支持度的充分度量。统计最优性标准的等效度量是最大似然法的似然比（或对数似然差）和似然差支持度度量，以及贝叶斯推断的后验概率比和后验概率差支持度度量。这些统计支持度度量满足充分条件S(h | e,b) ∝ O(h | e,b)，并且在这个程度上是内部一致的；然而，它们没有将支持度量化为解释力的函数，因此不是客观支持度的度量。相对拟合差异（RFD；相对GB支持度）以及任何基于进化枝频率的简约法（自展法和刀切法特征重抽样）或统计方法（自展法特征重抽样、马尔可夫链蒙特卡罗（MCMC）进化枝频率）支持度度量都不满足充分条件S(h | e,b) ∝ O(h | e,b)，也没有将支持度计算为解释力的函数。因此，它们不是充分的支持度度量。© 威利·亨尼希学会2008年。
