nlive:一个R包,以方便应用西格形和随机变点混合模型
Ana W Capuano1, Maude Wagner2,3
1RUSH Alzheimer's Disease Center, Rush University Medical Center, 1750 Harrison, Chicago, 60612, IL, USA. ana_capuano@rush.edu.
BMC medical research methodology
|November 4, 2023
概括
nlive R 包简化了用于纵向数据分析的 Sigmoidal 和 Piecewise 混合模型的安装. 它提供了用于共变量包含和随机变化建模的增强功能,改进了参数解释.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 计算生物学 计算生物学
背景情况:
- 混合效应模型对于分析纵向数据轨迹至关重要.
- 现有的R包缺乏易于实现的Sigmoidal混合模型与共变量和先进的零碎混合模型.
研究的目的:
- 开发一个R包,nlive,可方便安装Sigmoidal和Piecewise混合模型.
- 纳入最近在零碎建模和高效估计技术方面的进展.
主要方法:
- 开发了nlive R包,其中包含函数,共变矩阵和初始生成.
- 实现了多项式平滑过渡和随机近似期望最大化 (SAEM).
- 包括用于输出解释的功能,如注释结果,警告和图表.
主要成果:
- 该nlive包成功地适应了Sigmoidal混合模型和零碎混合模型 (突然和平滑).
- 模拟证实了该包的功能,包括时间和趋同.
- 一个数据示例展示了该包的实用性和可解释性.
结论:
- nlive提供了一个直观的界面,适合复杂的混合效果模型.
- 该软件包方便对纵向数据进行分析,并增强了建模功能.
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