Gibbs point process models with mixed effects

Environmetrics

Volumn 21, Issue 3-4, 2010, Pages 341-353

  Illian, Janine B ^a   Hendrichsen, Ditte K ^b,c  

^a UNIVERSITY OF ST ANDREWS   (United Kingdom)

^b UNIVERSITY OF COPENHAGEN   (Denmark)

^c NATIONAL ENVIRONMENTAL RESEARCH INSTITUTE   (Denmark)

Author keywords

Berman Turner device; glmm; Mixed effects; Replicated spatial patterns; Spatial point process models

Indexed keywords

DATA SET; INSTRUMENTATION; NUMERICAL MODEL; POISSON RATIO; SOFTWARE;

ARCTIC; GREENLAND;

OVIBOS;

EID: 77952415296     PISSN: 11804009     EISSN: 1099095X     Source Type: Journal    
DOI: 10.1002/env.1008     Document Type: Article

References (25)

1. Baddeley A, Turner R. 2000. Practical maximum pseudolikelihood for spatial point processes. New Zealand Journal of Statistics 42: 283-322.
2. Baddeley AJ, Turner R. 2005. Spatstat: an R package for analyzing spatial point patterns. Journal of Statistical Software 12: 1-42.
3. Bates D, Maechler M, Dai B. 2008. lme4: linear mixed-effects models using S4 classes. R package version 0.999375-25
4. Berman M, Turner R. 1992. Approximating point process likelihoods with GLIM. Applied Statistics 41: 31-38
5. Besag J. 1976. Some methods of statistical analysis for spatial data. Biometrika 47: 77-92.
6. Bresow NE, Clayton DG. 1993. Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 88: 9-25.
7. Daley DJ, Vere-Jones D. 2008. An Introduction to the Theory of Point Processes, Volume II: General Theory and Structure (2nd edn). Springer-Verlag: New York.
8. Diggle P. 2007. Spatio-temporal point processes: methods and applications. In Statistical Methods for Spatio-temporal Systems, Finkenstädt B, Held L, Isham V (eds). Chapman & Hall/CRC Press: Boca Raton, FL; 1-45.
9. Diggle PJ, Mateu J, Clough HE. 2000. A comparison between parametric and and non-parametric approaches to the analysis of replicated spatial point patterns. Advances in Applied Probability 32: 331-343.
10. Forchhammer MC, Boomsma J. 1995. Foraging strategies and seasonal diet optimization of muskoxen in West Greenland. Oecologia 104: 169-180.
11. Forchhammer MC, Boomsma J. 1998. Optimal mating strategies in nonterritorial ungulates: a general model tested on muskoxen. Behavioural Ecology, 9: 136-143.
12. Forchhammer MC, Post E, Berg TBB, Høye TT, Schmidt NM. 2005. Local-scale and short-term herbivore-plant spatial dynamics reflect influences of large-scale climate. Ecology 86: 2644-2651.
13. Green PJ. 1995. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Scandinavian Journal of Statistics 21: 359-373.
14. Illian JB, Møller J, Waagepetersen RP. 2007. Hierarchical spatial point process analysis for a plant community with high biodi- versity. Journal of Environmental and Ecological Statistics, first online, DOI: 10.1007/s10651-007-0070-8
15. Illian JB, Penttinen A, Stoyan H, Stoyan D. 2008. Statistical Analysis and Modelling of Spatial Point Patterns. Wiley: Chichester
16. Kallenberg O. 1984. An informal guide to the theory of conditioning in point processes. International Statistical Review 52: 151-164.
17. Krebs JR, Davies NB. 1997. Behavioural Ecology. An Evolutionary Approach. Wiley Blackwell: Oxford
18. McCullagh P, Nelder JA. 1989. Generalized Linear Models. Chapman & Hall: New York.
19. Meltofte H, Berg TBB. 2004. Zackenberg Ecological Research Operations. BioBasis. Conceptual Design and Sampling Procedures of the Biological Programme of Zackenberg Basic (7th edn). National Environmental Research Institute, Department of Arctic Environment: Roskilde, Denmark.
20. Meltofte H, Christensen TR, Elberling B, Forchhammer MC, Rasch M. 2008. High-Arctic Ecosystem Dynamics in a Changing Climate. Ten Years of Monitoring and Research at Zackenberg Research Station, Northeast Greenland. Advances in Ecological Research, 40, Academic Press: London.
21. Møller J, Waagepetersen R. 2003. An introduction to simulation-based inference for spatial point processes. In Lecture Notes in Statistics 137, Møller J (ed.). Springer-Verlag: New York, 143-198.
22. R Development Core Team. 2008. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing:Vienna, Austria; ISBN 3-900051-07-0.
23. Stoyan D, Kendall W, Mecke J. 1995. Stochastic Geometry and its Applications (2nd edn). John Wiley & Sons: London
24. Strauss D. 1975. A model for clustering. Biometrika 63: 467-475.
25. van Lieshout M. 2000. Markov Point Processes and Their Applications. Imperial College Press: London.