[Dataset] Flexible Distributed Lag Models for Count Data Using mgcv
In this tutorial we present the use of R package mgcv to implement Distributed Lag Non-Linear Models (DLNMs) in a flexible way. Interpretation of smoothing splines as random quantities enables approximate Bayesian inference, which in turn allows uncertainty quantification and comprehensive model che...
| Autores: | , , , , , , , , |
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| Tipo de recurso: | conjunto de datos |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/394947 |
| Acceso en línea: | http://hdl.handle.net/10261/394947 https://doi.org/10.20350/digitalCSIC/17416 https://digital.csic.es/handle/10261/394946 |
| Access Level: | acceso abierto |
| Palabra clave: | Penalized splines Bayesian inference DLNM Environmental epidemiology Heat-stress http://metadata.un.org/sdg/3 http://metadata.un.org/sdg/11 Ensure healthy lives and promote well-being for all at all ages Make cities and human settlements inclusive, safe, resilient and sustainable |
| Sumario: | In this tutorial we present the use of R package mgcv to implement Distributed Lag Non-Linear Models (DLNMs) in a flexible way. Interpretation of smoothing splines as random quantities enables approximate Bayesian inference, which in turn allows uncertainty quantification and comprehensive model checking. We illustrate various modeling situations using open-access epidemiological data in conjunction with simulation experiments. We demonstrate the inclusion of temporal structures and the use of mixture distributions to allow for extreme outliers. Moreover, we demonstrate interactions of the temporal lagged structures with other covariates with different lagged periods for different covariates. Spatial structures are also demonstrated, including smooth spatial variability and Markov random fields, in addition to hierarchical formulations to allow for non-structured dependency. Posterior predictive simulation is used to ensure models verify well against the data. |
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