The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Hierarchical generalized additive models in ecology: an introduction with mgcv
Open accessAbstract
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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Cites
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Fitting Linear Mixed-Effects Models Using lme4
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brms : An R Package for Bayesian Multilevel Models Using Stan
Bürkner, P.-C. (2017) · Journal of Statistical Software · doi:10.18637/jss.v080.i01 · Cited by 9,476 · cited 7×
“The second option is to fit models using one of two dedicated mixed effect model estimation packages, nlme and lme4. The mgcv package includes the function gamm(), which uses the nlme package to estimate the GAM, automatically handling the transformation of smooth terms into random effects (and back into basis functio…”
BackgroundMethodInfluentialRandom-Effects Models for Longitudinal Data
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“rces (Hastie and Tibshirani, 1990; Ramsay and Silverman, 2005; Wood, 2017) and has been applied in many spatio-temporal modelling contexts (recent examples include Wood et al. (2017); Simpson (2018); Pedersen et al. (2019)) There is a direct correspondence between smoothing splines and stochastic processes (Kimeldorf…”
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Metadata
- Authors
- Pedersen, E. J., Miller, D. L., Simpson, G. L., & Ross, N.
- Venue
- PeerJ
- Publisher
- PeerJ
- Published
- 2019-05-27
- Volume
- 7
- Pages
- e6876
- ISSN
- 2167-8359 (ISSN-L)
- OpenAlex
- W2947802409
- Cited by
- 1,193
- References
- 49
- FWCI
- 64.69
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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