Comparing dominance hierarchy methods using a data-splitting approach with real-world data
Comparing dominance hierarchy methods using a data-splitting approach with real-world data
复制标题
使用数据分割方法的优势层次方法与真实世界数据进行比较
DOI：
10.1093/beheco/araa095
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发表时间：
2020
影响因子：
2.4
通讯作者：
L. Barrett
中科院分区：
文献类型：
--
作者：
C. Vilette;T. Bonnell;P. Henzi;L. Barrett
用于推断社会等级的数值方法的发展导致了大量可供选择的选项。先前的工作通过使用模拟数据集来确定给定的排序方法是否能够准确地再现已知存在于数据中的优势层次结构，从而建立了这些方法的有效性。在这里，我们提供了一种互补的方法，通过询问由给定方法产生的计算排名顺序是否准确地预测两个对手之间随后比赛的结果，来评估计算优势等级的可靠性。我们的方法使用了一种数据分割的“训练-测试”方法，并演示了它在野生长尾猴（Chlorocebus pygerythrus）超过3年收集的真实数据中的应用。我们评估了7种方法和6种分析变量的可靠性。在我们的研究系统中，所有13种测试方法都能很好地预测未来的攻击性结果，尽管在推断的等级顺序上存在一些差异。当我们将数据集分成6个月的训练期和一个变量测试数据集时，所有方法都正确地预测了随后10个月的积极结果。在这10个月之后，预测的可靠性下降，反映了该群体人口构成的变化。我们还展示了数据分割方法如何不仅为研究人员提供了一种确定其数据集最可靠方法的方法，而且还允许他们评估社会群体中年龄-性别阶层的排名可靠性变化情况，从而根据其研究系统的具体属性定制他们选择的方法。
The development of numerical methods for inferring social ranks has resulted in an overwhelming array of options to choose from. Previous work has established the validity of these methods through the use of simulated datasets, by determining whether a given ranking method can accurately reproduce the dominance hierarchy known to exist in the data. Here, we offer a complementary approach that assesses the reliability of calculated dominance hierarchies by asking whether the calculated rank order produced by a given method accurately predicts the outcome of a subsequent contest between two opponents. Our method uses a data-splitting “training–testing” approach, and we demonstrate its application to real-world data from wild vervet monkeys (Chlorocebus pygerythrus) collected over 3 years. We assessed the reliability of seven methods plus six analytical variants. In our study system, all 13 methods tested performed well at predicting future aggressive outcomes, despite some differences in the inferred rank order produced. When we split the dataset with a 6-month training period and a variable testing dataset, all methods predicted aggressive outcomes correctly for the subsequent 10 months. Beyond this 10-month cut-off, the reliability of predictions decreased, reflecting shifts in the demographic composition of the group. We also demonstrate how a data-splitting approach provides researchers not only with a means of determining the most reliable method for their dataset but also allows them to assess how rank reliability changes among age–sex classes in a social group, and so tailor their choice of method to the specific attributes of their study system.
推断纵向层次结构：研究主导动态的框架和方法
影响因子：
4.8
作者：
Strauss, Eli D.;Holekamp, Kay E.;Jackson, ed., Andrew
通讯作者：
Jackson, ed., Andrew