tgp (version 1.1-11)

tgp.design: Sequential Treed D-Optimal Design for Treed Gaussian Process Models

Description

Based on the maximum a' posteriori (MAP) treed partition extracted from a "tgp"-class object, calculate independant sequential treed D-Optimal designs in each of the regions.

Usage

tgp.design(howmany, Xcand, out)

Arguments

howmany

Number of new points in the design. Must be less than the number of candidates contained in Xcand, i.e., howmany <= dim(xcand)[1]<="" code="">

Xcand

data.frame, matrix or vector of candidates from which new design points are subsampled. Must have the same dimension as out$X

out

"tgp" class object which is the output of one of the model functions which has tree support, e.g., btgpllm, btgp,

Value

• Output is a list of data.frames containing XX design points for each region of the MAP tree in out

Details

This function partitions Xcand and out$X based on the MAP tree (obtained on "tgp"-class out with partition) and calls dopt.gp in order to obtain a D-optimal design under independent stationary Gaussian processes models defined in each region. The aim is to obtain a design where new points from Xcand are spaced out relative to themselves, and relative to the existing locations (out$X) in the region. The number of new points from each region is proportional to the number of candidates Xcand in the region.

References

Gramacy, R. B., Lee, H. K. H. (2006). Bayesian treed Gaussian process models. Available as UCSC Technical Report ams2006-01.

Gramacy, R. B., Lee, H. K. H. (2006). Adaptive design of supercomputer experiments. Available as UCSC Technical Report ams2006-02.

Gramacy, R. B., Lee, H. K. H., & Macready, W. (2004). Parameter space exploration with Gaussian process trees. ICML (pp. 353--360). Omnipress & ACM Digital Library.

http://www.ams.ucsc.edu/~rbgramacy/tgp.html

See Also

bgpllm, btlm, blm, bgp, btgpllm, tgp, plot.tgp, dopt.gp, lhs, partition

Examples

#
# 2-d Exponential data
# (This example is based on random data.  
# It might be fun to run it a few times)
#

# get the data
exp2d.data <- exp2d.rand()
X <- exp2d.data$X; Z <- exp2d.data$Z
Xcand <- exp2d.data$XX

# fit treed GP LLM model to data w/o prediction
# basically just to get MAP tree (and plot it)
out <- btgpllm(X=X, Z=Z, pred.n=FALSE, corr="exp")
tgp.trees(out)

# find a treed sequential D-Optimal design 
# with 10 more points.  It is interesting to 
# contrast this design with one obtained via
# the dopt.gp function
XX <- tgp.design(10, Xcand, out)

# now fit the model again in order to assess
# the predictive surface at those new design points
dout <- btgpllm(X=X, Z=Z, XX=XX, corr="exp")
plot(dout)