Hierarchical generalized additive models in ecology: an introduction with mgcv

PeerJ

David L. Miller¹  Noam Ross²  Gavin L. Simpson³  Eric J. Pedersen⁴ 

[1] Centre for Research into Ecological and Environmental Modelling, University of St Andrews, St Andrews, UK;EcoHealth Alliance, New York, NY, USA;Institute of Environmental Change and Society, University of Regina, Regina, SK, Canada;Northwest Atlantic Fisheries Center, Fisheries and Oceans Canada, St. John’s, NL, Canada;

关键词: Generalized additive models;    Hierarchical models;    Time series;    Functional regression;    Smoothing;    Regression;   

DOI  :  10.7717/peerj.6876

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown