对于分类数据的贝叶斯式Gower协议
1Lehigh University, Bethlehem, PA, 18015, USA. drjphughesjr@gmail.com.
Scientific reports
|February 24, 2025
概括
这项研究引入了基于Gower距离的新方法,用于在名义和顺序数据中测量评价者之间的一致性. 这些直观的技术很容易识别有影响力的单位或程序员,并由开源R包支持.
科学领域:
- 统计 统计 统计 统计
- 数据分析 数据分析
- 测量科学 测量科学 测量科学
背景情况:
- 准确测量协议在各种领域至关重要,包括医学研究和诊断.
- 评估名义和顺序数据一致性的现有方法可能是复杂的或范围有限的.
- 识别有影响力的单位或编码人员对于数据质量评估至关重要.
研究的目的:
- 开发和介绍新的,简单的,直观的方法来测量名义和顺序数据的一致性.
- 扩展这些方法以适应单向和双向随机抽样设计.
- 为协议措施提供贝叶斯推理的强有力的方法.
主要方法:
- 使用Gower类型距离来计算协议分数.
- 开发单向和双向随机抽样设计的贝叶斯推理方法.
- 将拟议的方法应用于模拟和现实世界的数据集,包括放射学和精神病学研究.
主要成果:
- 提出的基于Gower距离的方法被证明是简单,直观和计算效率高的.
- 这些方法有助于直接识别有影响力的单位和/或编码人员.
- 贝叶斯推理框架已成功开发用于单向和双向设计.
- 这项研究表明,高斯的相互信息作为一个潜在的更有用的协议规模.
结论:
- 本文所介绍的方法提供了一种灵活且易于使用的方法来量化分级协议.
- 开源R包"goweragreement"支持这些统计技术的实际应用.
- 进一步研究替代协议规模,如高斯式相互信息,是有必要的.
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