Description Usage Arguments Details Value References See Also Examples

`sim.dpp.modal.nystrom.kmeans()`

uses the kmeans-based Nystrom approximation of Zhang and Kwok (2010) to select `n`

design sites from the observational dataset `Xin`

using the DPP-based design emulator of Pratola et al. (2018). The design constructed assumes 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 set of landmarks chosen by the kmeans algorithm, and the resulting eigenvectors are projected into the higher dimensional space using the Nystrom approximation. Additional options for the `MiniBatchKmeans()`

algorithm from package `ClusterR`

can be passed to alter the construction of the landmark set.

1 2 3 |

`Xin` |
An |

`n` |
Size of the designed subsample to draw from |

`rho` |
The |

`m` |
Number of landmark points to use in constructing the kmeans-based Nystrom approximation. |

`method` |
Type of approximation to use. Currently only supports “KmeansNystrom”. |

`initializer` |
Initialization to use in the Kmeans algorithm, default is “kmeans++”. |

`...` |
Additional options to pass to |

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 a matrix which is the union of the observation matrix `Xin`

and selected landmark sites, the indices into this matrix of the selected design sites as well as matrix of the design sites.

Pratola, Matthew T., Lin, C. Devon, and Craigmile, Peter. (2018)
Optimal Design Emulators: A Point Process Approach.
*arXiv:1804.02089*.

Zhang, Kai and Kwok, James T. (2010)
Clustered Nystrom method for large scale manifold learning and dimension reduction.
*IEEE Transactions on Neural Networks*, **21.10**, 1576–1587.
doi: 10.1109/TNN.2010.2064786

`demu-package`

`sim.dpp.modal`

`sim.dpp.modal.nystrom`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
library(demu)
# Fake dataset in 5 dimensions
X=matrix(runif(500*5),ncol=5)
rho=rep(0.01,5)
n=50
samp=sim.dpp.modal.nystrom.kmeans(X,rho,n)
samp$design
# Could plot the result:
# pchvec=rep(1,nrow(samp$X))
# pchvec[samp$pts]=20
# cexvec=rep(0.1,nrow(samp$X))
# cexvec[samp$pts]=1
# colvec=rep("black",nrow(samp$X))
# colvec[samp$pts]="red"
# pairs(samp$X,pch=pchvec,cex=cexvec,col=colvec,xlim=c(0,1),ylim=c(0,1))
``` |

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