曼特尔测试的基准测试和衍生方法用于测试距离矩阵之间的关联.
Claudio S Quilodrán1, Mathias Currat1,2, Juan I Montoya-Burgos1,2
1Department of Genetics and Evolution, University of Geneva, Geneva, Switzerland.
Molecular ecology resources
|December 2, 2023
概括
曼特尔测试可靠地分析生态和进化中的距离变量,即使有空间自相对应. 避免转换变量类型,以保持数据完整性和假设准确性.
科学领域:
- 生态生态学 生态生态学
- 进化生物学 进化生物学
- 量化科学 量化科学
背景情况:
- 测试对象之间的关联是科学学科的基础.
- 点变量 (在物体上) 和距离变量 (在物体之间) 描述了这些关系.
- 曼特测试广泛用于距离变量,但在统计能力和空间自相关性I型错误方面面临批评.
研究的目的:
- 评估具有不同空间自关联强度的关联测试的统计能力和I型错误率.
- 评估单变量和多变量数据的性能,包括基因多样性的模拟.
- 为选择基于变量类型和假设的适当方法提供指导方针.
主要方法:
- 使用计算模拟来评估统计能力和I型错误.
- 分析涵盖了在一系列空间自相对应强度下的单变量和多变量数据.
- 用遗传多样性模拟来说明距离矩阵统计的性能.
主要成果:
- 蒙特尔测试没有显示膨胀的I型错误,当空间自相关性只影响相关性中的一个变量或因果关系的特定情况时.
- 通过调整显著性值,可以减轻具有更受影响变量的膨胀型I错误.
- 在使用距离变量来制定假设时,Mantle测试的统计能力仍然很强大.
结论:
- 由于潜在的信息丢失和假设改变,不建议转换变量类型.
- 蒙特尔测试是距离变量的一个强大的工具,在空间自关联下可管理的I型错误率.
- 提出了指导方针,以帮助研究人员根据他们的数据和研究问题选择最合适的统计方法.
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