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spind: an R Package to Account for Spatial Autocorrelation in the Analysis of Lattice Data

机译:spind:一个R软件包用于在晶格数据分析中说明空间自相关

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摘要

spind is an R package aiming to provide a useful toolkit to account for spatial dependence in the analysis of lattice data. Grid-based data sets in spatial modelling often exhibit spatial dependence, i.e. values sampled at nearby locations are more similar than those sampled further apart. spind methods, described here, take this kind of two-dimensional dependence into account and are sensitive to its variation across different spatial scales. Methods presented to account for spatial autocorrelation are based on the two fundamentally different approaches of generalised estimating equations as well as wavelet-revised methods. Both methods are extensions to generalised linear models. spind also provides functions for multi-model inference and scaling by wavelet multiresolution regression. Since model evaluation is essential for assessing prediction accuracy in species distribution modelling, spind additionally supplies users with spatial accuracy measures, i.e. measures that are sensitive to the spatial arrangement of the predictions.
机译:spind是一个R包,旨在提供一个有用的工具包,以解决晶格数据分析中的空间依赖性。在空间建模中基于网格的数据集通常表现出空间依赖性,即在附近位置采样的值比在更远处采样的值更相似。这里描述的旋转方法考虑了这种二维依赖性,并且对其在不同空间尺度上的变化很敏感。提出的用于解释空间自相关的方法是基于两种基本不同的广义估计方程以及小波修正方法。两种方法都是广义线性模型的扩展。 spind还提供了通过小波多分辨率回归进行多模型推理和缩放的功能。由于模型评估对于评估物种分布建模中的预测准确性至关重要,因此Spind还为用户提供了空间准确性度量,即对预测的空间排列敏感的度量。

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