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Hierarchical generalized additive models in ecology: an introduction with mgcv.

ABSTRACT: 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.

SUBMITTER: Pedersen EJ

PROVIDER: S-EPMC6542350 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Hierarchical generalized additive models in ecology: an introduction with mgcv.

Pedersen Eric J EJ Miller David L DL Simpson Gavin L GL Ross Noam N

PeerJ 20190527

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 ab ...[more]

PMID: 31179172

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