Hierarchical generalized additive models in ecology: an introduction with mgcv.
PeerJ
; 7: e6876, 2019.
Article
em En
| MEDLINE
| ID: mdl-31179172
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
PeerJ
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Canadá