simulate.kriging: Kriging Simulation

In SPOT: Sequential Parameter Optimization Toolbox

Kriging Simulation

Description

(Conditional) Simulation at given locations, with a model fit resulting from buildKriging. 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 'kriging'
simulate(
  object,
  nsim = 1,
  seed = NA,
  xsim,
  method = "decompose",
  conditionalSimulation = TRUE,
  Ncos = 10,
  returnAll = FALSE,
  ...
)

Arguments

object	fit of the Kriging model (settings and parameters), of class kriging.
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 at
method	"decompose" (default) or "spectral", specifying the method used for simulation. Note that "decompose" is can be preferable, since it is exact but may be computationally infeasible for high-dimensional xsim. On the other hand, "spectral" yields a function that can be evaluated at arbitrary sample locations.
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.
Ncos	number of cosine functions (used with method="spectral" only)
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

buildKriging, predict.kriging

SPOT documentation built on June 26, 2022, 1:06 a.m.