探索最佳混合效应模型的方法以数据驱动的方式与多重处理:在公共卫生研究中的应用
1Educational Psychology Program, University of Alabama, Tuscaloosa, AL 35487, USA.
概括
一个新的R模块, explore.models,有效地使用Akaike信息标准 (AIC) 和贝叶斯信息标准 (BIC) 识别公共卫生研究的最佳多层模型. 这种经过验证的工具有助于比传统方法更快地选择优质模型.
科学领域:
- 公共卫生 公共卫生
- 健康心理学 心理健康心理学
- 教育研究教育研究
背景情况:
- 多级建模对于分析具有嵌套结构的复杂公共卫生数据至关重要.
- 在多层次分析中,从众多可能性中选择最佳候选模型是一个重大挑战.
- 现有的模型选择方法可能是计算密集型和耗时的.
研究的目的:
- 为了开发和验证一个R模块,探索模型,以实现高效的多层次模型选择.
- 将探索模型的性能与已建立的模型选择标准进行比较.
- 评估公共卫生研究模块的可靠性和可行性.
主要方法:
- 开发了R模块"explore.models"以比较使用Akaike信息标准 (AIC) 和贝叶斯信息标准 (BIC) 的候选多层模型.
- 在三个公共卫生数据集上测试了该模块,检查了心理健康,预防措施遵守和疫苗意图.
- 与模型贝叶斯因子交叉验证模块结果以确保准确性.
主要成果:
- explore.models模块使用AIC和BIC准确地提名了使用AIC和BIC的最佳候选模型.
- 提名结果始终得到模型贝叶斯因子的支持.
- 识别的模型优于基于贝叶斯因子模型的完整模型.
- 与模型贝叶斯因子计算相比,该模块的处理时间显著缩短.
结论:
- explore.models是一个可靠,有效和可行的R包,用于公共卫生,健康心理学和教育领域的数据驱动的多层次模型探索.
- 该模块为选择最佳模型提供了一个计算效率高的替代方案.
- 使用AIC和BIC与多处理提供了一个强大的方法,在复杂的数据集中选择模型.
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