Using INLA to fit a complex point process model with temporally varying effects – a case study

• Main Authors: Illian, Janine Baerbel, Soerbye, S, Rue, H, Hendrichsen, D
• Other Authors: University of St Andrews. School of Mathematics and Statistics, University of St Andrews. Scottish Oceans Institute, University of St Andrews. Centre for Research into Ecological & Environmental Modelling
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2012
• Subjects: Spatial point process; Spatial scale; Replicated patterns; QA Mathematics; QA; Greenland; Laplace; ovibos moschatus; Zackenberg
• Online Access: http://hdl.handle.net/10023/3306; http://www.math.ntnu.no/inla/r-inla.org/papers/S17-2010.pdf; http://www.jenvstat.org/v03/i07/paper

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. Publisher PDF Peer reviewed