Comparison of three expert elicitation methods for logistic regression on predicting the presence of the threatened brush-tailed rock-wallaby Petrogale penicillata
Article first published online: 9 JUL 2008
Copyright © 2008 John Wiley & Sons, Ltd.
Volume 20, Issue 4, pages 379–398, June 2009
How to Cite
O'Leary, R. A., Choy, S. L., Murray, J. V., Kynn, M., Denham, R., Martin, T. G. 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: 379–398. doi: 10.1002/env.935
- Issue published online: 11 MAY 2009
- Article first published online: 9 JUL 2008
- Manuscript Accepted: 15 MAY 2008
- Manuscript Received: 23 OCT 2007
- expert elicitation;
- Bayesian statistical modelling;
- logistic regression;
- habitat suitability modelling;
- threatened species
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. Copyright © 2008 John Wiley & Sons, Ltd.