A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA) This paper develops a methodology that provides a toolbox for routinely fitting complex models to realistic spatial point pattern data. We consider models that are based on log-Gaussian Cox processes and include local interaction in these by considering constructed covariates. This enables us to use integrated nested Laplace approximation and to considerably speed up the inferential task. In addition, methods for model comparison and model assessment facilitate the modelling process. The performance of the approach is assessed in a simulation study. To demonstrate the versatility of the approach, models are fitted to two rather different examples, a large rainforest data set with covariates and a point pattern with multiple marks.

References in zbMATH (referenced in 24 articles , 1 standard article )

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  1. Andrew Finley, Abhirup Datta, Sudipto Banerjee: R package for Nearest Neighbor Gaussian Process models (2020) arXiv
  2. Anita K. Nandi, Tim C. D. Lucas, Rohan Arambepola, Peter Gething, Daniel J. Weiss: disaggregation: An R Package for Bayesian Spatial Disaggregation Modelling (2020) arXiv
  3. Borrajo, M. I.; González-Manteiga, W.; Martínez-Miranda, M. D.: Bootstrapping kernel intensity estimation for inhomogeneous point processes with spatial covariates (2020)
  4. Gianluca Baio: survHE: Survival Analysis for Health Economic Evaluation and Cost-Effectiveness Modeling (2020) not zbMATH
  5. Daniel Turek, Mark Risser: Bayesian nonstationary Gaussian process modeling: the BayesNSGP package for R (2019) arXiv
  6. Gilles Kratzer, Fraser Iain Lewis, Arianna Comin, Marta Pittavino, Reinhard Furrer: Additive Bayesian Network Modelling with the R Package abn (2019) arXiv
  7. Hooten, Mevin B.; Hefley, Trevor J.: Bringing Bayesian models to life (2019)
  8. Krainski, Elias T.; Gómez-Rubio, Virgilio; Bakka, Haakon; Lenzi, Amanda; Castro-Camilo, Daniela; Simpson, Daniel; Lindgren, Finn; Rue, Håvard: Advanced spatial modeling with stochastic partial differential equations using R and INLA (2019)
  9. Micheas, Athanasios C.; Chen, Jiaxun: sppmix: Poisson point process modeling using normal mixture models (2018)
  10. Illian, Janine B.; Burslem, David F. R. P.: Improving the usability of spatial point process methodology: an interdisciplinary dialogue between statistics and ecology (2017)
  11. Altieri, L.; Cocchi, D.; Greco, F.; Illian, J. B.; Scott, E. M.: Bayesian P-splines and advanced computing in R for a changepoint analysis on spatio-temporal point processes (2016)
  12. Shaddick, Gavin; Zidek, James V.: Spatio-temporal methods in environmental epidemiology (2016)
  13. Benjamin M. Taylor, Tilman M. Davies, Barry S. Rowlingson, Peter J. Diggle: Bayesian Inference and Data Augmentation Schemes for Spatial, Spatiotemporal and Multivariate Log-Gaussian Cox Processes in R (2015) not zbMATH
  14. Blangiardo, Marta; Cameletti, Michela: Spatial and spatio-temporal Bayesian models with R-INLA (2015)
  15. Ferkingstad, Egil; Rue, Håvard: Improving the INLA approach for approximate Bayesian inference for latent Gaussian models (2015)
  16. Serhiyenko, Volodymyr; Ravishanker, Nalini; Venkatesan, Rajkumar: Approximate Bayesian estimation for multivariate count time series models (2015)
  17. Francesco Finazzi; Alessandro Fassò: D-STEM: A Software for the Analysis and Mapping of Environmental Space-Time Variables (2014) not zbMATH
  18. Micheas, Athanasios Christou: Hierarchical Bayesian modeling of marked non-homogeneous Poisson processes with finite mixtures and inclusion of covariate information (2014)
  19. Rajala, T.; Penttinen, A.: Bayesian analysis of a Gibbs hard-core point pattern model with varying repulsion range (2014)
  20. Martins, Thiago G.; Simpson, Daniel; Lindgren, Finn; Rue, Håvard: Bayesian computing with INLA: new features (2013)

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