结构化贝叶斯回归树模型用于估计分布式滞后效应:R包 dlmtree
Seongwon Im1, Ander Wilson1, Daniel Mork2
1Department of Statistics, Colorado State University, United States of America.
概括
本研究介绍了dlmtree,这是树结构分布式滞后模型 (DLM) 的一个R包. 它简化了分析暴露-结果关系的时间延迟和平滑效应,帮助研究人员进行复杂的统计建模.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 计算统计学 计算统计学
背景情况:
- 暴露-结果关系往往涉及时间滞后.
- 分布式滞后模型 (DLM) 估计了这些滞后效应.
- 自主相关数据需要对滞后效应进行平滑的约束.
研究的目的:
- 介绍树结构 DLM 的 R 包 dlmtree.
- 提供先进的DLM技术的用户友好的实现.
- 促进对暴露-结果关系与时间滞后的分析.
主要方法:
- 使用树结构分布式滞后模型 (DLM) 框架.
- 集成扩展用于全面的统计建模.
- 在R包中提供了用户友好的实现.
主要成果:
- dlmtree包提供了树结构DLM的无集成.
- 展示了与模拟数据相匹配,推断和解释.
- 包括一个用于异质性分析的Shiny应用程序.
结论:
- dlmtree为研究人员提供了一个全面且易于使用的工具.
- 能够对时间滞后的曝光-结果关联进行可靠的分析.
- 方便先进的统计建模和数据可视化.
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