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

## Draw samples from the conditional DPP design emulator.

### Description

`sim.dpp.modal()`

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.

### Usage

```
sim.dpp.modal(R,n=0,eigs=NULL)
```

### Arguments

`R` |
A correlation matrix evaluated over a grid of candidate design sites. |

`n` |
Size of the design to sample. |

`eigs` |
One can alternatively pass the pre-computed eigendecomposition of the correlation matrix instead of |

### 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 vector of indices to the sampled design sites.

### 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.fast`

`sim.dpp.modal.seq`

### Examples

```
library(demu)
# candidate grid
ngrid=20
x=seq(0,1,length=ngrid)
X=as.matrix(expand.grid(x,x))
l.d=makedistlist(X)
# draw design from DPP mode
n=21
rho=0.01
R=rhomat(l.d,rep(rho,2))$R
pts=sim.dpp.modal(R,n)
# Could plot the result:
# plot(X,xlim=c(0,1),ylim=c(0,1))
# points(X[pts,],pch=20)
```

*demu*version 0.3.0 Index]