sim.dpp.modal.nystrom {demu} | R Documentation |
sim.dpp.modal.nystrom()
uses 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 stationary 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. This function uses a grid-based Nystrom approximation based on the passed matrix X
to avoid constructing a large correlation matrix if dimension ngrid^p
and its subsequent eigendecomposition.
sim.dpp.modal.nystrom(Xin,rho,n=0,ngrid=NULL,method="Nystrom")
Xin |
A initial |
rho |
The |
n |
Size of the design to sample from the candidate grid. |
ngrid |
Size of the candidate grid will be |
method |
Type of approximation to use. Currently only supports “Nystrom”. |
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 list containing the candidate points constructed and the points selected as the design sites from this candidate set as well as their indices.
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.nystrom.kmeans
library(demu)
# Starting design
X=matrix(runif(10*2),ncol=2)
rho=rep(0.01,2)
n=10
ngrid=11
samp=sim.dpp.modal.nystrom(X,rho,n,ngrid)
samp$design
# Could plot the result:
# plot(samp$X,xlim=c(0,1),ylim=c(0,1))
# points(samp$X[samp$pts,],pch=20)