plgp (version 1.0)

data.GP: Supply GP data to PL

Description

Functions to supply data to PL for Gaussian process (GP) regression, classification, or combined unknown constraint models

Usage

data.GP(begin, end = NULL, X, Y)
data.GP.improv(begin, end = NULL, f, rect, prior,
               adapt = ei.adapt, cands = 40,
               save = TRUE, verb = 2)
data.CGP(begin, end = NULL, X, C)
data.CGP.adapt(begin, end = NULL, f, rect, prior,
               cands = 40, verb = 2)
data.ConstGP(begin, end = NULL, X, Y, C)
data.ConstGP.improv(begin, end = NULL, f, rect, prior,
                   cands = 40, save = TRUE, verb = 2)

Arguments

begin

positive integer starting time for data to be returned

end

positive integer (end >= begin) ending time for data being returned; may be NULL if only data at time begin is needed

X

data.frame with at least end rows containing covariates

Y

vector of length at least end containing real-valued responses

C

vector of length at least end containing class labels

f

function returning a responses when called as f(X) for matrix X; for data.GP.improv the responses must be real-valued returned as a vector; for data.CGP.adapt they must be class

rect

bounding rectangle for the inputs X to f(X) with two columns and rows equalling nrow(X)

prior

prior parameters passed from PL generated by one of the prior functions, e.g., prior.GP

adapt

function that evaluates a sequential design criterion on some candidate locations; the default ei.adapt EI about the minimum; ieci.adapt providing IECI is another possibility , which is hard coded into data.Con

cands

number of Latin Hypercube candidate locations used to choose the next adaptively sampled input design point

save

scalar logical indicating if the improvment information for chosen candidate should be saved in the psave global variable

verb

verbosity level for printing the progress of improv and other adaptive sampling calculations

Value

• The output are vectors or data.frames.

Details

These functions provide data to PL for Gaussian progress regression and classification methods in a variety of ways. The simplest, data.GP and data.CGP supply pre-recorded regression and classification data stored in data frames and vectors; data.ConstGP is a hybrid that does joint regression and classification. The other functions provide data by active learning/sequential design:

The data.GP.improv function uses expected improvement (EI); data.CGP.improv uses predictive entropy; data.ConstGP.improv uses integrated expected conditional improvement (IECI). In these cases, once the x-location(s) is/are chosen, the function f is used to provide the response(s)

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

Examples

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