Using INLA to fit a complex point process model with temporally varying effects – a case study
Abstract
Integrated nested Laplace approximation (INLA) provides a fast and yet quite exact approach to fitting complex latent Gaussian models which comprise many statistical models in a Bayesian context, including log Gaussian Cox processes. This paper discusses how a joint log Gaussian Cox process model may be fitted to independent replicated point patterns. We illustrate the approach by fitting a model to data on the locations of muskoxen (Ovibos moschatus) herds in Zackenberg valley, Northeast Greenland and by detailing how this model is specified within the R-interface R-INLA. The paper strongly focuses on practical problems involved in the modelling process, including issues of spatial scale, edge effects and prior choices, and finishes with a discussion on models with varying boundary conditions.
Citation
Illian , J B , Soerbye , S , Rue , H & Hendrichsen , D 2012 , ' Using INLA to fit a complex point process model with temporally varying effects – a case study ' , Journal of Environmental Statistics , vol. 3 , no. 7 .
Publication
Journal of Environmental Statistics
Status
Peer reviewed
ISSN
1945-1296Type
Journal article
Rights
(c) 2012 The authors. This is an open access article available under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) which permits anyone to download, reuse, reprint, modify, distribute, and/or copy articles in Journal of Environmental Statistics, so long as the original authors and source are credited.
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