Comparing INLA and OpenBUGS for hierarchical Poisson modeling in disease mapping
Comparing INLA and OpenBUGS for hierarchical Poisson modeling in disease mapping
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DOI：
10.1016/j.sste.2015.08.001
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发表时间：
2015-07-01
影响因子：
3.4
通讯作者：
Watjou, K.
中科院分区：
文献类型：
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作者：
Carroll, R.;Lawson, A. B.;Watjou, K.
最近开发的R包INLA(集成嵌套拉普拉斯近似)正在成为用于贝叶斯推理的更广泛的包。INLA软件已被宣传为疾病地图应用程序中MCMC的快速替代品。在这里，我们通过R中的Brugs包(调用OpenBUGS)将INLA包与MCMC方法进行比较。我们关注的是通常用于疾病映射的泊松数据模型。归根结底，INLA是实现贝叶斯方法的一种计算高效的方法，与OpenBUGS相比，它为固定参数返回了几乎相同的估计，但在恢复默认设置下的随机效果、它们的精度和模型拟合好度度量方面做得不够。我们假设了基本事实参数的默认设置，通过在我们的模拟研究中更改这些默认设置，我们能够恢复与OpenBUGS在相同假设下产生的估计相当的估计。(C)2015爱思唯尔有限公司。保留所有权利。
The recently developed R package INLA (Integrated Nested Laplace Approximation) is becoming a more widely used package for Bayesian inference. The INLA software has been promoted as a fast alternative to MCMC for disease mapping applications. Here, we compare the INLA package to the MCMC approach by way of the BRugs package in R, which calls OpenBUGS. We focus on the Poisson data model commonly used for disease mapping. Ultimately, INLA is a computationally efficient way of implementing Bayesian methods and returns nearly identical estimates for fixed parameters in comparison to OpenBUGS, but falls short in recovering the true estimates for the random effects, their precisions, and model goodness of fit measures under the default settings. We assumed default settings for ground truth parameters, and through altering these default settings in our simulation study, we were able to recover estimates comparable to those produced in OpenBUGS under the same assumptions. (C) 2015 Elsevier Ltd. All rights reserved.