Lawson Andrew B
Department of Public Health sciences, Medical University of South Carolina, Charleston, SC, USA.
Spat Spatiotemporal Epidemiol. 2020 Jun;33:100323. doi: 10.1016/j.sste.2020.100323. Epub 2020 Jan 27.
This tutorial describes the basic implementation of Bayesian hierarchical models for spatial health data using the R package nimble. To quote the nimble R description: A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, particle filtering, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. Examples of the use of the package for a small range of Bayesian Disease Mapping (BDM) models is explored and focus on different approaches to model fitting and analysis are discussed. Examples of publicly available small area health data is used throughout.
本教程介绍了使用R包nimble对空间健康数据进行贝叶斯层次模型的基本实现。引用nimble R描述的内容：一个用于编写层次统计模型的系统，在很大程度上与“BUGS”和“JAGS”兼容，编写nimble函数以操作模型并进行基本的R风格数学运算，并通过自定义生成的C++编译模型和nimble函数。“NIMBLE”包括用于MCMC、粒子滤波、蒙特卡罗期望最大化的默认方法以及其他一些工具。nimble函数系统使我们能够轻松地执行诸如从R实现新的MCMC采样器、从R自定义将采样器分配到模型的不同部分，以及与“NIMBLE”提供的采样器一起通过C++自动编译新采样器等操作。探讨了该包在一小部分贝叶斯疾病映射（BDM）模型中的使用示例，并讨论了专注于模型拟合和分析的不同方法。 throughout使用了公开可用的小区域健康数据示例。
