sim.dpp.modal.nystrom {demu} | R Documentation |

## Draw samples from the conditional DPP design emulator using grid-based Nystrom approximation.

### Description

`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.

### Usage

```
sim.dpp.modal.nystrom(Xin,rho,n=0,ngrid=NULL,method="Nystrom")
```

### Arguments

`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”. |

### Details

For more details on the method, see Pratola et al. (2018). Detailed examples demonstrating the method are available at http://www.matthewpratola.com/software.

### Value

A list containing the candidate points constructed and the points selected as the design sites from this candidate set as well as their indices.

### References

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

### See Also

`demu-package`

`sim.dpp.modal`

`sim.dpp.modal.nystrom.kmeans`

### Examples

```
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)
```

*demu*version 0.3.0 Index]