sim.dpp.modal.fast {demu} | R Documentation |
sim.dpp.modal.fast()
is similar to sim.dpp.modal
but is a C++ codepath that makes use of
SPAM's sparse matrices to enable faster computation. It implements the DPP-based design
emulator of Pratola et al. (2018) to draw a sample of the n
-run optimal design for a Gaussian
process regression model with compact correlation function r(x,x^\prime)
, where the entries
of R
are formed by evaluating r(x,x^\prime)
over a grid of candidate locations.
sim.dpp.modal.fast(R,n)
R |
A sparse correlation matrix evaluated over a grid of candidate design sites. The sparse matrix should be of type |
n |
Size of the design to sample. |
For more details on the method, see Pratola et al. (2018). Detailed examples demonstrating the method are available at http://www.matthewpratola.com/software.
A vector of indices to the sampled design sites.
Pratola, Matthew T., Lin, C. Devon, and Craigmile, Peter. (2018) Optimal Design Emulators: A Point Process Approach. arXiv:1804.02089.
demu-package
sim.dpp.modal
sim.dpp.modal.seq
library(demu)
library(fields)
library(spam)
library(Matrix)
library(Rcpp)
# candidate grid
ngrid=20
x=seq(0,1,length=ngrid)
X=as.matrix(expand.grid(x,x))
# draw design from DPP mode
n=21
theta=0.39
R.spam=wendland.cov(X,X,theta=theta,k=3)
R=as.dgCMatrix.spam(R.spam)
rm(R.spam)
pts=sim.dpp.modal.fast(R,n)
# Could plot the result:
# plot(X,xlim=c(0,1),ylim=c(0,1))
# points(X[pts,],pch=20)