Spatial Confounding and Spatial+ for Nonlinear Covariate Effects
Spatial Confounding and Spatial+ for Nonlinear Covariate Effects
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非线性协变量效应的空间混杂和空间
DOI：
10.1007/s13253-023-00586-7
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Dupont E
中科院分区：
文献类型：
--
作者：
Dupont E
空间变化数据的回归模型利用空间随机效应来反映空间相关结构。然而，这种随机效应可能会干扰协变量效应估计，使其不可靠。这个问题，被称为空间混淆，是复杂的，只研究了线性协变量效应的模型。然而，正如我们评估土壤、气候和地形变量对树木健康影响的林业例子所示，我们感兴趣的协变量效应在实践中往往是未知的和非线性的。我们首次考虑了在广义加性模型（GAMs）框架中实现的具有非线性效应的空间模型中的空间混淆。我们表明，最近开发的用于减轻线性情况下混淆的空间+方法可以适应这种设置。在实践中，空间+可以作为一种诊断工具，用于调查协变量效应估计是否受到空间混淆的影响，并在存在偏差时对估计进行校正。本文附带的补充资料出现在网上。
Regression models for spatially varying data use spatial random effects to reflect spatial correlation structure. Such random effects, however, may interfere with the covariate effect estimates and make them unreliable. This problem, known as spatial confounding, is complex and has only been studied for models with linear covariate effects. However, as illustrated by a forestry example in which we assess the effect of soil, climate, and topography variables on tree health, the covariate effects of interest are in practice often unknown and nonlinear. We consider, for the first time, spatial confounding in spatial models with nonlinear effects implemented in the generalised additive models (GAMs) framework. We show that spatial+, a recently developed method for alleviating confounding in the linear case, can be adapted to this setting. In practice, spatial+ can then be used both as a diagnostic tool for investigating whether covariate effect estimates are affected by spatial confounding and for correcting the estimates for the resulting bias when it is present. Supplementary materials accompanying this paper appear online.
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