使用Akaike信息标准对变量选择和报告的实用建议
Chris Sutherland1, Darragh Hare2,3, Paul J Johnson2
1Centre for Research into Ecological and Environmental Modelling, University of St Andrews, St Andrews, UK.
Proceedings. Biological sciences
|September 27, 2023
概括
这项研究澄清了关于生态建模中的Akaike信息标准 (AIC) 的常见误解. 它使用模拟来解释.
科学领域:
- 生态生态学 生态生态学
- 统计 统计 统计 统计
- 生态建模 生态建模
背景情况:
- 模型选择在生态研究中至关重要,Akaike信息标准 (AIC) 是主要的工具.
- 关于AIC应用,解释和报告,用户之间仍然存在常见的误解.
- 围绕"假装"变量和p值在基于AIC的模型选择中的作用存在特定的混乱.
研究的目的:
- 为了解决围绕Akaike信息标准 (AIC) 的普遍用户误解.
- 通过模拟提供对AIC应用和解释的直观理解.
- 促进改善生态模型选择和报告中的统计实践.
主要方法:
- 这项研究补充了AIC现有的技术文献.
- 模拟方法用于开发AIC概念周围的直觉.
- 专注于解释AIC模型表和p值与AIC之间的关系.
主要成果:
- 模拟提供了对AIC应用的实际见解.
- 阐明了模型选择中"假装"变量的概念.
- 在使用AIC时,提供了关于解释统计支持的指导.
结论:
- 对AIC的更好的理解可以导致更强大的生态建模.
- 基于模拟的直觉有助于克服常见的统计陷.
- 该研究倡导使用,解释和报告AIC选择的模型的更好实践.
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