semfindr:用于识别结构方程建模中的有影响力的案例的R包
Shu Fai Cheung1, Mark H C Lai2
1Department of Psychology, University of Macau, Taipa, Macao SAR, China.
Multivariate behavioral research
|March 2, 2026
概括
本研究介绍了semfindr,这是一个R包,用于识别结构方程建模 (SEM) 中具有影响力的案例. 它简化了敏感性分析,以获得可靠的研究结果.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 计算统计学 计算统计学
背景情况:
- 敏感性分析对于评估结构方程建模 (SEM) 结果的稳定性至关重要.
- 评估案例对参数估计和模型匹配的影响是SEM灵敏度分析的一个关键方面.
- 目前在SEM中识别有影响力的案例的方法往往有限或应用不当.
研究的目的:
- 开发一个可访问的R包,semfindr,用于识别在SEM有影响力的案例.
- 提供有效和全面的工具,用于在SEM敏感性分析.
- 为了促进对病例影响的适当评估,并提高SEM调查结果的可靠性.
主要方法:
- 开发的"semfindr"R套件使用离开一个-out (LOO) 方法.
- 通过分离重新装配和影响计算步骤来实现计算效率.
- 包含绘图函数,以便在复杂的SEM中有效地可视化案例影响.
主要成果:
- 据了解,semfindr包能够有效地识别在SEM中具有影响力的案例.
- 该软件包支持多组模型,并处理丢失的数据.
- semfindr提供已准备发布的结果和情形,用于案例影响评估.
结论:
- semfindr提供了一个用户友好和计算效率高的解决方案,用于在SEM中识别有影响力的案例.
- 该套件提高了SEM敏感性分析的质量和可靠性.
- semfindr有助于更好地理解和报告在SEM研究中具有影响力的案例.
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