Bayesian classification and regression trees for predicting incidence of cryptosporidiosis: quantiles of sensitivity, specificity and log posterior for training and validation datasets over all accepted trees, for Bayesian classification trees
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This dataset was gathered to predict the spatial distribution of the
cryptosporidiosis infection
using selected social-ecological factors and climate variables.
Predictions were completed using a Bayesian CART (Classification and
Regression Trees) model.
The dataset presents the quantiles of sensitivity, specificity and log posterior for training and validation datasets over all accepted trees, for Bayesian classification trees in the study.
The dataset presents the quantiles of sensitivity, specificity and log posterior for training and validation datasets over all accepted trees, for Bayesian classification trees in the study.
Location of data collection
kmlPolyCoords
153.552920,-9.929730 137.994575,-9.929730 137.994575,-29.178588 153.552920,-29.178588 153.552920,-9.929730
Publications
Hu, Wen, O'Leary, Rebecca, Mengersen, Kerrie, & Low-Choy, Sama (2011) Bayesian Classification and regression trees for predicting incidence of cryptosporidiosis. PLoS ONE, 6(8), pp. 1-8.
http://eprints.qut.edu.au/52459/
Research areas
Specificity
Cryptosporidium
MATHEMATICAL
SCIENCES
Decision trees
Bayes theorem
Probability distribution
Datasets
Bayesian
Validation
Infectious diseases
Crytposporidiosis
Posterior
Spatial distribution
Cite this collection
Hu, Wenbiao; O'Leary, Rebecca A.; Mengersen, Kerrie; Choy, Samantha Low (2013): Quantiles of sensitivity, specificity and log posterior for training and validation datasets over all accepted trees, for Bayesian classification trees. Table_3.xls. PLOS ONE.
Related information
Rebecca O'Leary, Research collaborator
http://goo.gl/LqhdR6
Access the data
Licence
Copyright
© 2011 Hu et al.
Dates of data collection
From 2001 to 2011
Connections
Contacts
Other
Date record created:
2014-07-03T14:29:46
Date record modified:
2019-07-04T11:22:45
Record status:
Published - Open Access