Hierarchical generalized additive models in ecology: an introduction with mgcv
2019/05/27 by Eric J. Pedersen, David L. Miller, Gavin L. Simpson +1 · 2 voices · 1,201 citations
Computer Science · Environmental Science · Mathematics · #Additive model #Biology #Code (set theory) #Computer science #Covariate #Data Analysis with R #Data mining #Ecology #Ecology and Vegetation Dynamics Studies #Extension (predicate logic) #Forest ecology and management #Function (biology) #Generalized additive model #Generalized linear mixed model #Generalized linear model #Hierarchical database model #Hierarchical generalized linear model #Machine learning #Mathematics #Mixed model #Multilevel model #Programming language #Theoretical computer science
paper · doi:10.7717/peerj.6876
published in PeerJ 7, e6876 (PeerJ, Inc.)
openalex publication_date 2019/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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 .
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