Data from: Bayesian hierarchical models for spatially misaligned data in R

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).空间失配（Spatial misalignment）指在某一已观测位点上，多个结局变量中至少有一项存在缺失的情况。针对空间数据，对这类缺失观测值的预测，需结合结局变量在位点内的关联关系，以及已完成测量的邻近位点的信息进行推断。本研究详细阐述并演示了一种用于建模空间失配多变量数据的贝叶斯回归框架。本研究重点关注构建有效的概率模型，该模型可实现参数后验分布的估计，并将不确定性传播至部分或全部结局变量存在缺失的位点的结局预测分布中。本研究针对高斯型与非高斯型结局变量，分别提供了对应的模型及配套软件实现方案。本研究通过人工合成数据集与森林清查数据集，演示了所提框架内的模型参数推断与预测推断流程。本研究提出的马尔可夫链蒙特卡洛（Markov Chain Monte Carlo, MCMC）采样器采用C++编写，并借助R语言的外部语言接口调用Fortran实现的BLAS（Basic Linear Algebra Subprograms，基础线性代数子程序库）与LAPACK（Linear Algebra Package，线性代数包）库，以实现高效的矩阵运算。上述模型通过CRAN（Comprehensive R Archive Network，综合R档案网络）平台可获取的spBayes R包中的spMisalignLM与spMisalignGLM函数实现，相关包的下载地址为http://cran.r-project.org。