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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

Queensland University of Technology
Adjunct Associate Professor Sama Low Choy (Aggregated by) Distinguished Professor Kerrie Mengersen (Aggregated by) Professor Wenbiao Hu (Aggregated by)
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Open Licence view details
CC-BY

Creative Commons Attribution 3.0
http://creativecommons.org/licenses/by/3.0/au/

© 2011 Hu et al.

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Contact Information

Postal Address:
Dr Wenbiao Hu

w2.hu@qut.edu.au

Full description

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.

Data time period: 2001 to 2011

This dataset is part of a larger collection

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153.55292,-9.92973 137.99458,-9.92973 137.99458,-29.17859 153.55292,-29.17859 153.55292,-9.92973

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Subjects
Accepted | Bayes theorem | Bayesian | Cryptosporidium | Crytposporidiosis | Datasets | Decision trees | Deviance | Infectious diseases | Posterior | Probability distribution | Regression | Residual | Spatial distribution | Squares | Sums | Validation |

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  • Local : 10378.3/8085/1018.15732
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