Bayesian hierarchical models for spatially misaligned data in R
Bayesian hierarchical models for spatially misaligned data in R
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DOI：
10.1111/2041-210x.12189
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发表时间：
2014-06-01
影响因子：
6.6
通讯作者：
Cook, Bruce D.
中科院分区：
文献类型：
--
作者：
Finley, Andrew O.;Banerjee, Sudipto;Cook, Bruce D.
当多个结果变量中的至少一个在观察位置处缺失时，发生空间未对准。对于空间数据，这些缺失观测的预测应通过结果之间的位置关联和记录测量的邻近位置来告知。这项研究详细说明了贝叶斯回归框架建模空间错位的多变量数据。特别注意开发有效的概率模型，能够估计参数后验分布和传播的不确定性，通过结果的预测分布在一些或所有的结果没有观察到的位置。模型和相关的软件都提出了高斯和非高斯的结果。模型参数和预测推理的框架内说明使用合成和森林资源清查数据集。所提出的马尔可夫链蒙特卡罗采样器是用C++编写的，并利用R的外语接口来调用Fortran函数库（基本线性代数子程序）和lapack（线性代数包）库，以进行有效的矩阵计算。这些模型在spBayes r包中的spMissile LM和spMissile GLM函数中实现，该包可通过Comprehensive R Archive Network（cran）（http：//www.example.com）获得。cran.r-project.org
Spatial misalignment occurs when at least one of multiple outcome variables is missing at an observed location. For spatial data, prediction of these missing observations should be informed by within location association among outcomes and by proximate locations where measurements were recorded. This study details and illustrates a Bayesian regression framework for modelling spatially misaligned multivariate data. Particular attention is paid to developing valid probability models capable of estimating parameter posterior distributions and propagating uncertainty through to outcomes' predictive distributions at locations where some or all of the outcomes are not observed. Models and associated software are presented for both Gaussian and non-Gaussian outcomes. Model parameter and predictive inference within the proposed framework is illustrated using a synthetic and forest inventory data set. The proposed Markov chain Monte carlo samplers were written in c++ and leverage R's Foreign Language Interface to call fortran blas (Basic Linear Algebra Subprograms) and lapack (Linear Algebra Package) libraries for efficient matrix computations. The models are implemented in the spMisalignLM and spMisalignGLM functions within the spBayes r package available via the Comprehensive R Archive Network (cran) (http://cran.r-project.org).