plgp (version 1.0)

params.GP: Extract parameters from GP particles

Description

Extract parameters from particles for Gaussian process (GP) regression, classification, or combined unknown constraint models

Usage

params.GP()
params.CGP()
params.ConstGP()

Value

• returns a data.frame containing summaries for each parameter in its columns

Details

Collects the parameters from each of the particles (contained in the global variable peach) into a data.frame that can be used for quick summary and visualization, e.g., via hist. These functions are also called to make progress visualizations in PL

References

Gramacy, R. and Polson, N. (2010). Particle learning of Gaussian process models for sequential design and optimization. Tech. Rep. arXiv:0909.5262, University of Cambridge.

Gramacy, R. and Lee, H. (2010). Optimization under unknown constraints. Bayesian Statistics 9, J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith and M. West (Eds.); Oxford University Press

http://www.statslab.cam.ac.uk/~bobby/plgp.html

See Also

PL, lpredprob.GP, propagate.GP, init.GP, pred.GP

Examples

## See the demos via demo(package="plgp") and the examples
## section of ?plgp