simulate.modelKriging {CEGO} | R Documentation |

## Kriging Simulation

### Description

(Conditional) Simulate at given locations, with a model fit resulting from `modelKriging`

.
In contrast to prediction or estimation, the goal is to reproduce the covariance
structure, rather than the data itself. Note, that the conditional simulation
also reproduces the training data, but
has a two times larger error than the Kriging predictor.

### Usage

```
## S3 method for class 'modelKriging'
simulate(
object,
nsim = 1,
seed = NA,
xsim,
conditionalSimulation = TRUE,
returnAll = FALSE,
...
)
```

### Arguments

`object` |
fit of the Kriging model (settings and parameters), of class |

`nsim` |
number of simulations |

`seed` |
random number generator seed. Defaults to NA, in which case no seed is set |

`xsim` |
list of samples in input space, to be simulated |

`conditionalSimulation` |
logical, if set to TRUE (default), the simulation is conditioned with the training data of the Kriging model. Else, the simulation is non-conditional. |

`returnAll` |
if set to TRUE, a list with the simulated values (y) and the corresponding covariance matrix (covar) of the simulated samples is returned. |

`...` |
further arguments, not used |

### Value

Returned value depends on the setting of `object$simulationReturnAll`

### References

N. A. Cressie. Statistics for Spatial Data. JOHN WILEY & SONS INC, 1993.

C. Lantuejoul. Geostatistical Simulation - Models and Algorithms. Springer-Verlag Berlin Heidelberg, 2002.

### See Also

`modelKriging`

, `predict.modelKriging`

*CEGO*version 2.4.3 Index]