2024-03-29T13:52:05Zhttps://repositori.uji.es/oai/requestoai:repositori.uji.es:10234/1600722023-03-09T11:27:44Zcom_10234_7037com_10234_9col_10234_8635
00925njm 22002777a 4500
dc
Waagepetersen, Rasmus
author
Guan, Yongtao
author
Jalilian, Abdollah
author
Mateu, Jorge
author
2016
Multivariate log-Gaussian Cox processes are flexible models for multivariate point patterns. However, they have so far been applied in bivariate cases only. We move beyond the bivariate case to model multispecies point patterns of tree locations. In particular we address the problems of identifying parsimonious models and of extracting biologically relevant information from the models fitted. The latent multivariate Gaussian field is decomposed into components given in terms of random fields common to all species and components which are species specific. This allows a decomposition of variance that can be used to quantify to what extent the spatial variation of a species is governed by common or species-specific factors. Cross-validation is used to select the number of common latent fields to obtain a suitable trade-off between parsimony and fit of the data. The selected number of common latent fields provides an index of complexity of the multivariate covariance structure. Hierarchical clustering is used to identify groups of species with similar patterns of dependence on the common latent fields.
WAAGEPETERSEN, Rasmus, et al. Analysis of multispecies point patterns by using multivariate logāGaussian Cox processes. Journal of the Royal Statistical Society: Series C (Applied Statistics), 2016, vol. 65, no 1, p. 77-96.
0035-9254
1467-9876
http://hdl.handle.net/10234/160072
http://dx.doi.org/10.1111/rssc.12108
Cross-correlation
Cross-validation
Hierarchical clustering
Log-Gaussian Coxprocess
Multivariate point process
Proportions of variance
Analysis of multispecies point patterns by usingmultivariate log-Gaussian Cox processes