A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Model selection in canonical correlation analysis (CCA) using Bayesian model averaging
10.1002/env.641.abs
Understanding the relationship between ecological indicators and environmental stressors is difficult. One problem is that the environmental data often consist of a large number of variables. The relationship between ecological data and environmental data is investigated using canonical correlation analysis with Bayesian model averaging used to identify important variables. A set of data from Ohio, U.S.A., is studied. Activation probabilities are used to indicate the important ecological indicators and environmental variables. A plot of model coefficients adjusted by activation probabilities is used to indicate variables that are important to canonical correlation axes. Copyright © 2004 John Wiley & Sons, Ltd.
Model selection in canonical correlation analysis (CCA) using Bayesian model averaging
10.1002/env.641.abs
Understanding the relationship between ecological indicators and environmental stressors is difficult. One problem is that the environmental data often consist of a large number of variables. The relationship between ecological data and environmental data is investigated using canonical correlation analysis with Bayesian model averaging used to identify important variables. A set of data from Ohio, U.S.A., is studied. Activation probabilities are used to indicate the important ecological indicators and environmental variables. A plot of model coefficients adjusted by activation probabilities is used to indicate variables that are important to canonical correlation axes. Copyright © 2004 John Wiley & Sons, Ltd.
Model selection in canonical correlation analysis (CCA) using Bayesian model averaging
Noble, Robert (author) / Smith, Eric P. (author) / Ye, Keying (author)
Environmetrics ; 15 ; 291-311
2004-06-01
21 pages
Article (Journal)
Electronic Resource
English
Model selection in canonical correlation analysis (CCA) using Bayesian model averaging
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