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缺失数据对真实形态系统发育树的影响:缺失条目的数量和分布的影响。

The impact of missing data on real morphological phylogenies: influence of the number and distribution of missing entries.

作者信息

Prevosti Francisco J, Chemisquy María A

机构信息

División Mastozoología, Museo Argentino de Ciencias Naturales "Bernardino Rivadavia"- CONICET, Av. Angel Gallardo 470, C1405DJR Buenos Aires, Argentina.

Instituto de Botánica Darwinion (CONICET, ANCEFN), Labardén 200, Casilla de Correo 22, B1642HYD San Isidro, Buenos Aires, Argentina.

出版信息

Cladistics. 2010 Jun;26(3):326-339. doi: 10.1111/j.1096-0031.2009.00289.x. Epub 2009 Oct 19.

Abstract

Here we explore the effect of missing data in phylogenetic analyses using a large number of real morphological matrices. Different percentages and patterns of missing entries were added to each matrix, and their influence was evaluated by comparing the accuracy and error of most parsimonious trees. The relationships between accuracy and error and different parameters (e.g. the number of taxa and characters, homoplasy, support) were also evaluated. Our findings, based on real matrices, agree with the simulation studies, i.e. the negative effect increases with the percentage of missing entries, and decreases with the addition of more characters. This indicates that the main problem is the lack of information, not just the presence of missing data per se. Accuracy varies with different distribution patterns of missing entries; the worst case is when missing data are concentrated in a few taxa, while the best is when the missing entries are restricted to just a few characters. The results expand our knowledge of the missing data problem, corroborate many of the findings previously published using simulations, and could be useful for empirical or theoretical studies.  © The Willi Hennig Society 2009.

摘要

在此,我们使用大量真实的形态学矩阵来探究系统发育分析中缺失数据的影响。将不同百分比和模式的缺失数据添加到每个矩阵中,并通过比较最简约树的准确性和误差来评估其影响。还评估了准确性和误差与不同参数(如分类单元和性状的数量、同塑性、支持度)之间的关系。基于真实矩阵的研究结果与模拟研究一致,即负面影响随着缺失数据条目的百分比增加而增大,随着更多性状的添加而减小。这表明主要问题是信息不足,而不仅仅是缺失数据本身的存在。准确性会因缺失数据条目的不同分布模式而有所变化;最坏的情况是缺失数据集中在少数几个分类单元中,而最好的情况是缺失数据条目仅限于少数几个性状。这些结果扩展了我们对缺失数据问题的认识,证实了先前使用模拟方法发表的许多研究结果,并且可能对实证研究或理论研究有用。© 威利·亨尼希协会2009年。

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