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首页> 外文期刊>Ecology: A Publication of the Ecological Society of America >Optimizing the choice of a spatial weighting matrix in eigenvector-based methods
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Optimizing the choice of a spatial weighting matrix in eigenvector-based methods

机译：在基于特征向量的方法中优化空间加权矩阵的选择

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摘要

Eigenvector-mapping methods such as Moran's eigenvector maps (MEM) are derived from a spatial weighting matrix (SWM) that describes the relations among a set of sampled sites. The specification of the SWM is a crucial step, but the SWM is generally chosen arbitrarily, regardless of the sampling design characteristics. Here, we compare the statistical performances of different types of SWMs (distance-based or graph-based) in contrasted realistic simulation scenarios. Then, we present an optimization method and evaluate its performances compared to the arbitrary choice of the mostwidely used distance-based SWM. Results showed that the distance-based SWMs generally had lower power and accuracy than other specifications, and strongly underestimated spatial signals. The optimization method, using a correction procedure for multiple tests, had a correct type I error rate, and had higher power and accuracy than an arbitrary choice of the SWM. Nevertheless, the power decreased when too many SWMs were compared, resulting in a trade-off between the gain of accuracy and the loss of power. We advocate that future studies should optimize the choice of the SWM using a small set of appropriate candidates. R functions to implement the optimization are available in the adespatial package and are detailed in a tutorial.
机译：诸如Moran的特征向量映射（MEM）之类的特征向量映射方法源自用于描述一组采样站点之间的关系的空间加权矩阵（SWM）。 SWM的规格是关键步骤，但是，无论采样设计特征如何，通常都是任意选择的SWM。在这里，我们将不同类型的SWMS（基于距离或图形）的统计性能进行比较对比的逼真仿真方案。然后，我们介绍了优化方法，并与最多使用的基于距离的SWM的任意选择相比，评估其性能。结果表明，基于距离的SWMS通常具有比其他规格更低的功率和精度，以及强烈低估的空间信号。使用多个测试的校正过程的优化方法具有正确的I型错误率，并且具有比SWM的任意选择更高的功率和准确性。尽管如此，当比较太多的SWM时，功率降低，导致准确性和功率损失之间的权衡。我们倡导未来的研究应该使用一小部分合适的候选人来优化SWM的选择。 R函数用于实现优化在缺点包中可用，并在教程中详述。

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