Hierarchical generalized additive models in ecology: an introduction with mgcv

PeerJ

article

Eric J. Pedersen¹  David L. Miller³  Gavin L. Simpson⁵  Noam Ross⁷ 

[1] Northwest Atlantic Fisheries Center;Department of Biology, Memorial University of Newfoundland;Centre for Research into Ecological and Environmental Modelling, University of St Andrews;School of Mathematics and Statistics, University of St Andrews;Institute of Environmental Change and Society, University of Regina;Department of Biology, University of Regina;EcoHealth Alliance

关键词: Generalized additive models;    Hierarchical models;    Time series;    Functional regression;    Smoothing;    Regression;    Community ecology;    Tutorial;    Nonlinear estimation;   

DOI  :  10.7717/peerj.6876

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 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】

CC BY