Common datastream permutations of animal social network data are not appropriate for hypothesis testing using regression models.
Common datastream permutations of animal social network data are not appropriate for hypothesis testing using regression models.
复制标题
动物社交网络数据的常见数据流排列不适合使用回归模型进行假设检验。
DOI：
10.1111/2041-210x.13508
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发表时间：
2021-03
影响因子：
6.6
通讯作者：
中科院分区：
文献类型：
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作者：
1.社会网络方法已经成为描述、建模和检验关于动物社会结构的假说的关键工具。然而，由于网络数据的非独立性和混乱性的存在，经常需要专门的统计技术来检验这些网络中的假设。数据流置换最初是为了测试随机社会结构的零假设而开发的，现在已经成为测试动物社会网络中广泛的零假设的流行工具。特别是，它们被用来通过将这些排列与回归模型连接起来来测试外生因素是否与网络结构有关。2.在这里，我们证明了这些数据流排列通常不代表将动物社会网络分析与回归建模相结合的研究人员感兴趣的零假设，并使用模拟来演示使用这种方法的潜在陷阱。3.我们的模拟表明，如果用数据流排列来表明网络结构和协变量之间是否存在关系，则数据流排列可以导致极高的I类错误率，在某些情况下接近50%。在同一组仿真中，传统的节点-标签排列产生了合适的I类错误率(~5%)。4.我们的分析表明，数据流排列并不代表这些分析的合适的零假设。我们建议，在分别考虑网络数据的非独立性和观测的不可靠性问题时，可以找到这种方法的潜在替代方案。如果在数据收集期间引入的偏差可以在模型拟合之前或在模型本身内得到纠正，那么节点标签排列就可以作为一种有用的测试，用于将动物社会网络分析与回归建模相结合。
1. Social network methods have become a key tool for describing, modelling, and testing hypotheses about the social structures of animals. However, due to the non-independence of network data and the presence of confounds, specialized statistical techniques are often needed to test hypotheses in these networks. Datastream permutations, originally developed to test the null hypothesis of random social structure, have become a popular tool for testing a wide array of null hypotheses in animal social networks. In particular, they have been used to test whether exogenous factors are related to network structure by interfacing these permutations with regression models. 2. Here, we show that these datastream permutations typically do not represent the null hypothesis of interest to researchers interfacing animal social network analysis with regression modelling, and use simulations to demonstrate the potential pitfalls of using this methodology. 3. Our simulations show that, if used to indicate whether a relationship exists between network structure and a covariate, datastream permutations can result in extremely high type I error rates, in some cases approaching 50%. In the same set of simulations, traditional node-label permutations produced appropriate type I error rates (~ 5%). 4. Our analysis shows that datastream permutations do not represent the appropriate null hypothesis for these analyses. We suggest that potential alternatives to this procedure may be found in regarding the problems of non-independence of network data and unreliability of observations separately. If biases introduced during data collection can be corrected, either prior to model fitting or within the model itself, node-label permutations then serve as a useful test for interfacing animal social network analysis with regression modelling.