Hierarchical generalized additive models in ecology: an introduction with mgcv
Hierarchical generalized additive models in ecology: an introduction with mgcv
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DOI：
10.7717/peerj.6876
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发表时间：
2019-05-27
期刊：
影响因子：
2.7
通讯作者：
Ross, Noam
中科院分区：
文献类型：
--
作者：
Pedersen, Eric J.;Miller, David L.;Ross, Noam
在这篇文章中，我们讨论了两种流行的生态数据复杂结构建模方法的扩展：广义加性模型(GAM)和层次模型(HGLM)。层次GAM(HGAM)允许对协变量和结果之间的非线性函数关系进行建模，其中函数本身的形状在不同的分组级别之间不同。我们描述了HGAM、HGLMS和GAM之间的理论联系，解释了如何对功能反应中组间差异程度的不同假设进行建模，并展示了如何使用现有的GAM软件-R中的mgcv包来容易地拟合HGAM。我们还讨论了与这些模型拟合的计算和统计问题，并演示了如何在示例数据上拟合HGAM。用于生成这篇论文的所有代码和数据都可以在以下网站上找到：githorb.com/Eric-pedersen/Mixed-Effect-gams。
In this paper, we discuss an extension to two popular approaches to modeling complex structures in ecological data: the generalized additive model (GAM) and the hierarchical model (HGLM). The hierarchical GAM (HGAM), allows modeling of nonlinear functional relationships between covariates and outcomes where the shape of the function itself varies between different grouping levels. We describe the theoretical connection between HGAMs, HGLMs, and GAMs, explain how to model different assumptions about the degree of intergroup variability in functional response, and show how HGAMs can be readily fitted using existing GAM software, the mgcv package in R. We also discuss computational and statistical issues with fitting these models, and demonstrate how to fit HGAMs on example data. All code and data used to generate this paper are available at: github.com/eric-pedersen/mixed-effect-gams.