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.

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.