Bringing the analysis of animal orientation data full circle: model-based approaches with maximum likelihood
Bringing the analysis of animal orientation data full circle: model-based approaches with maximum likelihood
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DOI：
10.1242/jeb.167056
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发表时间：
2017-11-01
影响因子：
2.8
通讯作者：
Johnsen, Sonke
中科院分区：
文献类型：
--
作者：
Fitak, Robert R.;Johnsen, Sonke
在动物定向的研究中，数据通常表示为方向，可以使用循环统计方法进行分析。虽然存在几个圆形统计检验来检测平均方向的存在，但基于似然的方法可能在假设检验中提供优势-特别是当数据是多模态的时。不幸的是，基于似然性的动物取向推断仍然很少见。在这里，我们讨论了一些常见的循环测试的假设和限制，并报告了一个新的R包称为CircMLE实现循环数据的最大似然分析。我们说明了使用这个包的模拟数据集和经验的例子数据集在奇努克鲑鱼（Oncorhynchus tshawytscha）。我们的软件提供了一个方便的界面，便于在动物定向研究中使用基于模型的方法。
In studies of animal orientation, data are often represented as directions that can be analyzed using circular statistical methods. Although several circular statistical tests exist to detect the presence of a mean direction, likelihood-based approaches may offer advantages in hypothesis testing - especially when data are multimodal. Unfortunately, likelihood-based inference in animal orientation remains rare. Here, we discuss some of the assumptions and limitations of common circular tests and report a new R package called CircMLE to implement the maximum likelihood analysis of circular data. We illustrate the use of this package on both simulated datasets and an empirical example dataset in Chinook salmon (Oncorhynchus tshawytscha). Our software provides a convenient interface that facilitates the use of model-based approaches in animal orientation studies.