Hierarchical generalized additive models in ecology: an introduction with mgcv
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Open Access
- 27 May 2019
- Vol. 7, e6876
- https://doi.org/10.7717/peerj.6876
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.Keywords
Funding Information
- Fisheries and Oceans Canada, Natural Science and Engineering Research Council of Canada (NSERC) Discovery Grant (RGPIN-2014-04032)
- OPNAV N45 and the SURTASS LFA Settlement Agreement, managed by the U.S. Navy’s Living Marine Resources program (N39430-17-C-1982)
- USAID PREDICT-2 Program
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