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Identifying predictors of evolutionary dispersion with phylogeographic generalised linear models
Discrete phylogeographic models enable the inference of the geographic history of biological organisms along phylogenetic trees. Frequently applied in the context of epidemiological modelling, phylogeographic generalised linear models were developed to allow for the evaluation of multiple predictors...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2017
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| _version_ | 1867613172383350785 |
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| access_status_str | Open Access |
| author | Wolff-Piggott, Timothy |
| author2 | Lacerda, Miguel |
| author_browse | Lacerda, Miguel Wolff-Piggott, Timothy |
| author_facet | Lacerda, Miguel Wolff-Piggott, Timothy |
| author_sort | Wolff-Piggott, Timothy |
| collection | Thesis |
| description | Discrete phylogeographic models enable the inference of the geographic history of biological organisms along phylogenetic trees. Frequently applied in the context of epidemiological modelling, phylogeographic generalised linear models were developed to allow for the evaluation of multiple predictors of spatial diffusion. The standard phylogeographic generalised linear model formulation, however, assumes that rates of spatial diffusion are a noiseless deterministic function of the set of covariates, admitting no other unobserved sources of variation. Under a variety of simulation scenarios, we demonstrate that the lack of a term modelling stochastic noise results in high false positive rates for predictors of spatial diffusion. We further show that the false positive rate can be controlled by including a random effect term, thus allowing unobserved sources of rate variation. Finally, we apply this random effects model to three recently published datasets and contrast the results of analysing these datasets with those obtained using the standard model. Our study demonstrates the prevalence of false positive results for predictors under the standard phylogeographic model in multiple simulation scenarios and, using empirical data from the literature, highlights the importance of a model accounting for random variation. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/25651 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:31:54.917Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/25651 Identifying predictors of evolutionary dispersion with phylogeographic generalised linear models Wolff-Piggott, Timothy Lacerda, Miguel Murrell, Ben Advanced Analytics and Decision Sciences Discrete phylogeographic models enable the inference of the geographic history of biological organisms along phylogenetic trees. Frequently applied in the context of epidemiological modelling, phylogeographic generalised linear models were developed to allow for the evaluation of multiple predictors of spatial diffusion. The standard phylogeographic generalised linear model formulation, however, assumes that rates of spatial diffusion are a noiseless deterministic function of the set of covariates, admitting no other unobserved sources of variation. Under a variety of simulation scenarios, we demonstrate that the lack of a term modelling stochastic noise results in high false positive rates for predictors of spatial diffusion. We further show that the false positive rate can be controlled by including a random effect term, thus allowing unobserved sources of rate variation. Finally, we apply this random effects model to three recently published datasets and contrast the results of analysing these datasets with those obtained using the standard model. Our study demonstrates the prevalence of false positive results for predictors under the standard phylogeographic model in multiple simulation scenarios and, using empirical data from the literature, highlights the importance of a model accounting for random variation. 2017-10-12T14:05:31Z 2017-10-12T14:05:31Z 2017 Master Thesis Masters MSc http://hdl.handle.net/11427/25651 eng application/pdf Department of Statistical Sciences Faculty of Science University of Cape Town |
| spellingShingle | Advanced Analytics and Decision Sciences Wolff-Piggott, Timothy Identifying predictors of evolutionary dispersion with phylogeographic generalised linear models |
| thesis_degree_str | Master's |
| title | Identifying predictors of evolutionary dispersion with phylogeographic generalised linear models |
| title_full | Identifying predictors of evolutionary dispersion with phylogeographic generalised linear models |
| title_fullStr | Identifying predictors of evolutionary dispersion with phylogeographic generalised linear models |
| title_full_unstemmed | Identifying predictors of evolutionary dispersion with phylogeographic generalised linear models |
| title_short | Identifying predictors of evolutionary dispersion with phylogeographic generalised linear models |
| title_sort | identifying predictors of evolutionary dispersion with phylogeographic generalised linear models |
| topic | Advanced Analytics and Decision Sciences |
| url | http://hdl.handle.net/11427/25651 |
| work_keys_str_mv | AT wolffpiggotttimothy identifyingpredictorsofevolutionarydispersionwithphylogeographicgeneralisedlinearmodels |