Comparison of three expert elicitation methods for logistic regression on predicting the presence of the threatened brush-tailed rock-wallaby Petrogale penicillata

O'Leary, RA, Choy, SL, Murray, JV, Kynn, M, Denham, R, Martin, TG and Mengersen, K (2009) Comparison of three expert elicitation methods for logistic regression on predicting the presence of the threatened brush-tailed rock-wallaby Petrogale penicillata. ENVIRONMETRICS, 20 4: 379-398. doi:10.1002/env.935


Author O'Leary, RA
Choy, SL
Murray, JV
Kynn, M
Denham, R
Martin, TG
Mengersen, K
Title Comparison of three expert elicitation methods for logistic regression on predicting the presence of the threatened brush-tailed rock-wallaby Petrogale penicillata
Journal name ENVIRONMETRICS   Check publisher's open access policy
ISSN 1180-4009
Publication date 2009-06-01
Year available 2008
Sub-type Article (original research)
DOI 10.1002/env.935
Volume 20
Issue 4
Start page 379
End page 398
Total pages 20
Place of publication UK
Publisher John Wiley & Sons
Language eng
Subject C1
9608 Flora, Fauna and Biodiversity
0104 Statistics
Abstract Numerous expert elicitation methods have been suggested for generalised linear models (GLMs). This paper compares three relatively new approaches to eliciting expert knowledge in a form suitable for Bayesian logistic regression. These methods were trialled on two experts in order to model the habitat suitability of the threatened Australian brush-tailed rock-wallaby (Petrogale penicillata). The first elicitation approach is a geographically assisted indirect predictive method with a geographic information system (GIS) interface. The second approach is a predictive indirect method which uses an interactive graphical tool. The third method uses a questionnaire to elicit expert knowledge directly about the impact of a habitat variable on the response. Two variables (slope and aspect) are used to examine prior and posterior distributions of the three methods. The results indicate that there are some similarities and dissimilarities between the expert informed priors of the two experts formulated from the different approaches. The choice of elicitation method depends on the statistical knowledge of the expert, their mapping skills, time constraints, accessibility to experts and funding available. This trial reveals that expert knowledge can be important when modelling rare event data, such as threatened species, because experts can provide additional information that may not be represented in the dataset. However care must be taken with the way in which this information is elicited and formulated.
Keyword expert elicitation
Bayesian statistical modelling
logistic regression
habitat suitability modelling
threatened species
Q-Index Code C1
Q-Index Status Provisional Code

Document type: Journal Article
Sub-type: Article (original research)
Collections: Spatial Ecology Lab Publications
School of Biological Sciences Publications
 
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Created: Thu, 03 Sep 2009, 17:54:02 EST by Mr Andrew Martlew on behalf of School of Biological Sciences