Hierarchical generalized additive models in ecology : an introduction with mgcv
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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.
Pedersen , E J , Miller , D L , Simpson , G L & Ross , N 2019 , ' Hierarchical generalized additive models in ecology : an introduction with mgcv ' , PeerJ , vol. 7 , e6876 . https://doi.org/10.7717/peerj.6876 , https://doi.org/10.7717/peerj.6876
Copyright © 2019 Pedersen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
DescriptionThis work was funded by Fisheries and Oceans Canada, Natural Science and Engineering Research Council of Canada (NSERC) Discovery Grant (RGPIN-2014-04032), by OPNAV N45 and the SURTASS LFA Settlement Agreement, managed by the U.S. Navy’s Living Marine Resources Program under Contract No. N39430-17-C-1982, and by the USAID PREDICT-2 Program.
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