Bayesian classification and regression trees for predicting incidence of cryptosporidiosis: quantiles of log residual sums of squares (RSS), deviance and log posterior for training and validation datasets over all accepted trees, for Bayesian regression 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 log residual sums of squares (RSS), deviance and log posterior for training and validation datasets over all accepted trees, for Bayesian regression trees of 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

Bayesian

Spatial distribution

Posterior

Cryptosporidium

Bayes theorem

Regression

Deviance

Sums

Accepted

Residual

Squares

Crytposporidiosis

Probability distribution

Decision trees

Infectious diseases

Datasets

Validation

Cite this collection

Hu, Wenbiao; O'Leary, Rebecca A.; Mengersen, Kerrie; Choy, Samantha Low (2013): Quantiles of log residual sums of squares (RSS), deviance and log posterior for training and validation datasets over all accepted trees, for Bayesian regression trees. Table_4.xls. PLOS ONE. 10.1371/journal.pone.0023903.t004.