Analysis of multispecies point patterns by using multivariate log-Gaussian Cox processes

Rasmus Waagepetersen, Yongtao Guan, Abdollah Jalilian, Jorge Mateu

Research output: Contribution to journalArticle

7 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)77-96
Number of pages20
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume65
Issue number1
DOIs
StatePublished - Jan 1 2016

Keywords

  • Cross-correlation
  • Cross-validation
  • Hierarchical clustering
  • Log-Gaussian Cox process
  • Multivariate point process
  • Proportions of variance

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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