dopt.gp {tgp} | R Documentation |
Create sequential D-Optimal design for a stationary Gaussian process model of fixed parameterization by subsampling from a list of candidates
dopt.gp(nn, X, Xcand)
nn |
Number of new points in the design. Must
be less than or equal to the number of candidates contained in
Xcand , i.e., nn <= dim(Xcand)[1] |
X |
data.frame , matrix or vector of input locations
which are forced into (already in) the design |
Xcand |
data.frame , matrix or vector of candidates
from which new design points are subsampled. Must have the same
dimension as X , i.e., dim(X)[2] == dim(Xcand)[2] |
Design is based on a stationary Gaussian process model with stationary isotropic
exponential correlation function with parameterization fixed as a function
of the dimension of the inputs. The algorithm implemented is a simple stochastic
ascent which maximizes det(K)
– the covariance matrix constructed
with locations X
and a subset of Xcand
of size nn
.
The selected design is locally optimal
The output is a list which contains the inputs to, and outputs of, the C code
used to find the optimal design. The chosen design locations can be
accessed as list members XX
or equivalently Xcand[fi,]
.
state |
unsigned short[3] random number seed to C |
X |
Input argument: data.frame of inputs X , can be NULL |
nn |
Input argument: number new points in the design |
n |
Number of rows in X , i.e., n = dim(X)[1] |
m |
Number of cols in X , i.e., m = dim(X)[2] |
Xcand |
Input argument: data.frame of candidate locations Xcand |
ncand |
Number of rows in Xcand , i.e., nncand = dim(Xcand)[1] |
fi |
Vector of length nn describing the selected new design locations
as indices into XXcand |
XX |
data.frame of selected new design locations, i.e.,
XX = Xcand[fi,] |
Inputs X, Xcand
containing NaN, NA, Inf
are discarded with non-fatal
warnings. If nn > dim(Xcand)[1]
then a non-fatal warning is displayed
and execution commences with nn = dim(Xcand)[1]
In the current version there is no progress indicator. You will have to be patient. Creating D-optimal designs is no speedy task.
This function is still considered experimental. (Pardon the pun.)
Robert B. Gramacy rbgramacy@ams.ucsc.edu
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.
Gramacy, R. B., Lee, H. K. H., & Macready, W. (2005). Adaptive Exploration of Computer Experiment Parameter Spaces. submitted to JCGS, available as UCSC Technical Report ams2005-16
Gramacy, R. B. & Lee, H. K. H. (2005). Gaussian Processes and Limiting Linear Models. available as UCSC Technical Report ams2005-17
http://www.ams.ucsc.edu/~rbgramacy/tgp.php
# # 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$XX # find a treed sequential D-Optimal design # with 10 more points dgp <- dopt.gp(10, X, Xcand) # plot the d-optimally chosen locations # Contrast with locations chosen via # the tgp.design function plot(X, pch=19, xlim=c(-2,6), ylim=c(-2,6)) points(dgp$XX)