Create sequential DOptimal design for a stationary Gaussian process model of fixed parameterization by subsampling from a list of candidates
1 
nn 
Number of new points in the design. Must
be less than or equal to the number of candidates contained in

X 

Xcand 

iter 
number of iterations of stochastic accent algorithm,
default 
verb 
positive integer indicating after how many rounds of
stochastic approximation to print each progress statement;
default 
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,]
.
X 
Input argument: 
nn 
Input argument: number new points in the design 
Xcand 
Input argument: 
ncand 
Number of rows in 
fi 
Vector of length 
XX 

Inputs X, Xcand
containing NaN, NA, Inf
are discarded with nonfatal
warnings. If nn > dim(Xcand)[1]
then a nonfatal 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 Doptimal designs is no speedy task
Robert B. Gramacy, rbgramacy@chicagobooth.edu, and Matt Taddy, taddy@chicagobooth.edu
Chaloner, K. and Verdinelli, I. (1995). Bayesian experimental design: A review. Statist. Sci., 10, (pp. 273–304).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  #
# 2d 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$XX
# find a treed sequential DOptimal design
# with 10 more points
dgp < dopt.gp(10, X, Xcand)
# plot the doptimally chosen locations
# Contrast with locations chosen via
# the tgp.design function
plot(X, pch=19, xlim=c(2,6), ylim=c(2,6))
points(dgp$XX)

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