simulate.modelKriging: Kriging Simulation

In CEGO: Combinatorial Efficient Global Optimization

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 modelKriging.
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 documentation built on May 14, 2021, 1:08 a.m.