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Analysis of multispecies point patterns by usingmultivariate log-Gaussian Cox processes
dc.contributor.author | Waagepetersen, Rasmus | |
dc.contributor.author | Guan, Yongtao | |
dc.contributor.author | Jalilian, Abdollah | |
dc.contributor.author | Mateu, Jorge | |
dc.date.accessioned | 2016-05-30T09:39:42Z | |
dc.date.available | 2016-05-30T09:39:42Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | WAAGEPETERSEN, Rasmus, et al. Analysis of multispecies point patterns by using multivariate log‐Gaussian Cox processes. Journal of the Royal Statistical Society: Series C (Applied Statistics), 2016, vol. 65, no 1, p. 77-96. | ca_CA |
dc.identifier.issn | 0035-9254 | |
dc.identifier.issn | 1467-9876 | |
dc.identifier.uri | http://hdl.handle.net/10234/160072 | |
dc.description.abstract | 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. | ca_CA |
dc.description.sponsorShip | We thank the Joint Editor, the Associate Editor and the two referees for constructive comments that helped to improve both content and exposition of this paper. Abdollah Jalilian and Rasmus Waagepetersen's research was supported by the Danish Natural Science Research Council, grant 09-072331 ‘Point process modelling and statistical inference’, Danish Council for Independent Research—Natural Sciences, grant 12-124675, ‘Mathematical and statistical analysis of spatial data’, and by Centre for Stochastic Geometry and Advanced Bioimaging, funded by a grant from the Villum Foundation. Yongtao Guan's research was supported by National Science Foundation grant DMS-0845368, by National Institutes of Health grant 1R01CA169043 and by the VELUX Visiting Professor programme. Jorge Mateu's research was supported by grants P1-1B2012-52 and MTM2013-43917-P. The BCI forest dynamics research project was made possible by National Science Foundation grants to Stephen P. Hubbell: DEB-0640386, DEB-0425651, DEB-0346488, DEB-0129874, DEB-00753102, DEB-9909347, DEB-9615226, DEB-9405933, DEB-9221033, DEB-9100058, DEB-8906869, DEB-8605042, DEB-8206992 and DEB-7922197, support from the Center for Tropical Forest Science, the Smithsonian Tropical Research Institute, the John D. and Catherine T. MacArthur Foundation, the Mellon Foundation, the Celera Foundation and numerous private individuals, and through the hard work of over 100 people from 10 countries over the past two decades. The plot project is part of the Center for Tropical Forest Science, a global network of large-scale demographic tree plots. The BCI soils data set was collected and analysed by J. Dalling, R. John, K. Harms, R. Stallard and J. Yavitt with support from National Science Foudation grants DEB021104, DEB021115, DEB0212284, DEB0212818 and Office of International Science and Engineering grant 0314581, Smithsonian Tropical Research Institute and Center for Tropical Forest Science. Paolo Segre and Juan Di Trani provided assistance in the field. The covariates dem, grad, mrvbf, solar and twi were computed in SAGA GIS by Tomislav Hengl (http://spatial-analyst.net/). We thank Dr Joseph Wright for sharing data on dispersal modes and life forms for the BCI tree species | ca_CA |
dc.format.extent | 20 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Royal Statistical Society | ca_CA |
dc.publisher | Wiley | ca_CA |
dc.relation.isPartOf | Journal of the Royal Statistical Society: Series C (Applied Statistics), 2016, vol. 65, no 1 | ca_CA |
dc.rights | © Royal Statistical Society | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | * |
dc.subject | Cross-correlation | ca_CA |
dc.subject | Cross-validation | ca_CA |
dc.subject | Hierarchical clustering | ca_CA |
dc.subject | Log-Gaussian Coxprocess | ca_CA |
dc.subject | Multivariate point process | ca_CA |
dc.subject | Proportions of variance | ca_CA |
dc.title | Analysis of multispecies point patterns by usingmultivariate log-Gaussian Cox processes | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | http://dx.doi.org/10.1111/rssc.12108 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | http://onlinelibrary.wiley.com/enhanced/doi/10.1111/rssc.12108/ | ca_CA |
dc.type.version | info:eu-repo/semantics/sumittedVersion |
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