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 39 articles , 1 standard article )

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  1. Bivand, Roger S.; Gómez-Rubio, Virgilio: Spatial survival modelling of business re-opening after Katrina: survival modelling compared to spatial probit modelling of re-opening within 3, 6 or 12 months (2021)
  2. Castro-Camilo, Daniela; Mhalla, Linda; Opitz, Thomas: Bayesian space-time gap filling for inference on extreme hot-spots: an application to Red Sea surface temperatures (2021)
  3. Jakob A. Dambon, Fabio Sigrist, Reinhard Furrer: varycoef: An R Package for Gaussian Process-based Spatially Varying Coefficient Models (2021) arXiv
  4. Lawson, Andrew B.: Using R for Bayesian spatial and spatio-temporal health modeling (2021)
  5. van Niekerk, Janet; Bakka, Haakon; Rue, Håvard: Competing risks joint models using R-INLA (2021)
  6. Andrew Finley, Abhirup Datta, Sudipto Banerjee: R package for Nearest Neighbor Gaussian Process models (2020) arXiv
  7. 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
  8. Borrajo, M. I.; González-Manteiga, W.; Martínez-Miranda, M. D.: Bootstrapping kernel intensity estimation for inhomogeneous point processes with spatial covariates (2020)
  9. Gianluca Baio: survHE: Survival Analysis for Health Economic Evaluation and Cost-Effectiveness Modeling (2020) not zbMATH
  10. Daniel Turek, Mark Risser: Bayesian nonstationary Gaussian process modeling: the BayesNSGP package for R (2019) arXiv
  11. Gilles Kratzer, Fraser Iain Lewis, Arianna Comin, Marta Pittavino, Reinhard Furrer: Additive Bayesian Network Modelling with the R Package abn (2019) arXiv
  12. Heaton, Matthew J.; Datta, Abhirup; Finley, Andrew O.; Furrer, Reinhard; Guinness, Joseph; Guhaniyogi, Rajarshi; Gerber, Florian; Gramacy, Robert B.; Hammerling, Dorit; Katzfuss, Matthias; Lindgren, Finn; Nychka, Douglas W.; Sun, Furong; Zammit-Mangion, Andrew: A case study competition among methods for analyzing large spatial data (2019)
  13. Hooten, Mevin B.; Hefley, Trevor J.: Bringing Bayesian models to life (2019)
  14. 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)
  15. McLean, M. W.; Wand, M. P.: Variational message passing for elaborate response regression models (2019)
  16. Micheas, Athanasios C.; Chen, Jiaxun: sppmix: Poisson point process modeling using normal mixture models (2018)
  17. Grilli, Leonardo; Innocenti, Francesco: Fitting logistic multilevel models with crossed random effects via Bayesian integrated nested Laplace approximations: a simulation study (2017)
  18. Illian, Janine B.; Burslem, David F. R. P.: Improving the usability of spatial point process methodology: an interdisciplinary dialogue between statistics and ecology (2017)
  19. 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)
  20. Shaddick, Gavin; Zidek, James V.: Spatio-temporal methods in environmental epidemiology (2016)

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