Efficient Bayesian analysis of occupancy models with logit link functions

dc.contributor.authorClark, Allan E
dc.contributor.authorAltwegg, Res
dc.date.accessioned2019-04-01T12:09:09Z
dc.date.available2019-04-01T12:09:09Z
dc.date.issued2018
dc.description.abstractOccupancy models (Ecology, 2002; 83: 2248) were developed to infer the probability that a species under investigation occupies a site. Bayesian analysis of these models can be undertaken using statistical packages such as WinBUGS, OpenBUGS, JAGS, and more recently Stan, however, since these packages were not developed specifically to fit occupancy models, one often experiences long run times when undertaking an analysis. Bayesian spatial single‐season occupancy models can also be fit using the R package stocc. The approach assumes that the detection and occupancy regression effects are modeled using probit link functions. The use of the logistic link function, however, is algebraically more tractable and allows one to easily interpret the coef‐ ficient effects of an estimated model by using odds ratios, which is not easily done for a probit link function for models that do not include spatial random effects. We de‐ velop a Gibbs sampler to obtain posterior samples from the posterior distribution of the parameters of various occupancy models (nonspatial and spatial) when logit link functions are used to model the regression effects of the detection and occupancy processes. We apply our methods to data extracted from the 2nd Southern African Bird Atlas Project to produce a species distribution map of the Cape weaver (Ploceus capensis) and helmeted guineafowl (Numida meleagris) for South Africa. We found that the Gibbs sampling algorithm developed produces posterior samples that are identical to those obtained when using JAGS and Stan and that in certain cases the posterior chains mix much faster than those obtained when using JAGS, stocc, and Stan. Our algorithms are implemented in the R package, Rcppocc. The software is freely available and stored on GitHub (https://github.com/AllanClark/Rcppocc).en_US
dc.identifier.apacitationClark, A. E., & Altwegg, R. (2018). Efficient Bayesian analysis of occupancy models with logit link functions. <i>Ecology and Evolution</i>, 9(2), 756-768. http://hdl.handle.net/11427/29960en_ZA
dc.identifier.chicagocitationClark, Allan E, and Res Altwegg "Efficient Bayesian analysis of occupancy models with logit link functions." <i>Ecology and Evolution</i> 9, 2. (2018): 756-768. http://hdl.handle.net/11427/29960en_ZA
dc.identifier.citationClark, A., Altwegg, R. 2018. Efficient Bayesian analysis of occupancy models with logit link functions. Ecology and Evolution. 9; 2; 756-768.en_ZA
dc.identifier.ris TY - Journal Article AU - Clark, Allan E AU - Altwegg, Res AB - Occupancy models (Ecology, 2002; 83: 2248) were developed to infer the probability that a species under investigation occupies a site. Bayesian analysis of these models can be undertaken using statistical packages such as WinBUGS, OpenBUGS, JAGS, and more recently Stan, however, since these packages were not developed specifically to fit occupancy models, one often experiences long run times when undertaking an analysis. Bayesian spatial single‐season occupancy models can also be fit using the R package stocc. The approach assumes that the detection and occupancy regression effects are modeled using probit link functions. The use of the logistic link function, however, is algebraically more tractable and allows one to easily interpret the coef‐ ficient effects of an estimated model by using odds ratios, which is not easily done for a probit link function for models that do not include spatial random effects. We de‐ velop a Gibbs sampler to obtain posterior samples from the posterior distribution of the parameters of various occupancy models (nonspatial and spatial) when logit link functions are used to model the regression effects of the detection and occupancy processes. We apply our methods to data extracted from the 2nd Southern African Bird Atlas Project to produce a species distribution map of the Cape weaver (Ploceus capensis) and helmeted guineafowl (Numida meleagris) for South Africa. We found that the Gibbs sampling algorithm developed produces posterior samples that are identical to those obtained when using JAGS and Stan and that in certain cases the posterior chains mix much faster than those obtained when using JAGS, stocc, and Stan. Our algorithms are implemented in the R package, Rcppocc. The software is freely available and stored on GitHub (https://github.com/AllanClark/Rcppocc). DA - 2018 DB - OpenUCT DP - University of Cape Town IS - 2 J1 - Ecology and Evolution KW - Bayesian spatial occupancy model LK - https://open.uct.ac.za PY - 2018 T1 - Efficient Bayesian analysis of occupancy models with logit link functions TI - Efficient Bayesian analysis of occupancy models with logit link functions UR - http://hdl.handle.net/11427/29960 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/29960
dc.identifier.vancouvercitationClark AE, Altwegg R. Efficient Bayesian analysis of occupancy models with logit link functions. Ecology and Evolution. 2018;9(2):756-768. http://hdl.handle.net/11427/29960.en_ZA
dc.language.isoenen_US
dc.publisher.departmentDepartment of Statistical Sciencesen_US
dc.publisher.facultyFaculty of Scienceen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.sourceEcology and Evolutionen_US
dc.source.journalissue2en_US
dc.source.journalvolume9en_US
dc.source.pagination756-768en_US
dc.source.urihttps://onlinelibrary.wiley.com/journal/20457758
dc.subjectBayesian spatial occupancy modelen_US
dc.subject.otherimperfect detection
dc.subject.otheroccupancy model
dc.subject.otherRcppocc
dc.subject.otherrestricted spatial regression
dc.titleEfficient Bayesian analysis of occupancy models with logit link functionsen_US
dc.typeJournal Articleen_US
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