tgp.design {tgp} | R Documentation |
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.
tgp.design(howmany, Xcand, out)
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] |
Xcand |
data.frame , matrix or vector of candidates
from which new design points are subsampled. Must have the same
dimension as out$X |
out |
"tgp" class object which is the output of one of the
model functions which has tree support, e.g., btgpllm ,
btgp , btlm , or tgp |
This function partitions Xcand
and out$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$X
) in the region.
The number of new points from each region is
proportional to the number of candidates Xcand
in the region.
Output is a list of data.frame
s containing XX
design
points for each region of the MAP tree in out
Input Xcand
containing NaN, NA, Inf
are discarded with non-fatal
warnings
D-Optimal computation in each region is preceded by a print statement
indicated the number of new locations to be chosen and the number of candidates
in the region. Other than that, there are no other indicators of progress.
You will have to be patient.
Creating treed sequential D-optimal designs is no speedy task. At least it
faster than the non-treed version (see dopt.gp
).
This function is still considered experimental. (Pardon the pun.)
The example below is also part of vignette("tgp")
Robert B. Gramacy rbgramacy@ams.ucsc.edu
Gramacy, R. B., Lee, H. K. H. (2006). Bayesian treed Gaussian process models. Available as UCSC Technical Report ams2006-01.
Gramacy, R. B., Lee, H. K. H. (2006). Adaptive design of supercomputer experiments. Available as UCSC Technical Report ams2006-02.
Gramacy, R. B., Lee, H. K. H., & Macready, W. (2004). Parameter space exploration with Gaussian process trees. ICML (pp. 353–360). Omnipress & ACM Digital Library.
http://www.ams.ucsc.edu/~rbgramacy/tgp.html
bgpllm
, btlm
, blm
,
bgp
, btgpllm
, tgp
, plot.tgp
,
dopt.gp
, lhs
, partition
# # 2-d Exponential data # (This example is based on random data. # It might be fun to run it a few times) # # get the data exp2d.data <- exp2d.rand() X <- exp2d.data$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)