(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.
# S3 method for kriging
simulate(
object,
nsim = 1,
seed = NA,
xsim,
method = "decompose",
conditionalSimulation = TRUE,
Ncos = 10,
returnAll = FALSE,
...
)fit of the Kriging model (settings and parameters), of class kriging.
number of simulations
random number generator seed. Defaults to NA, in which case no seed is set
list of samples in input space, to be simulated at
"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.
logical, if set to TRUE (default), the simulation is conditioned with the training data of the Kriging model. Else, the simulation is non-conditional.
number of cosine functions (used with method="spectral" only)
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
Returned value depends on the setting of object$simulationReturnAll
N. A. Cressie. Statistics for Spatial Data. JOHN WILEY & SONS INC, 1993.
C. Lantuejoul. Geostatistical Simulation - Models and Algorithms. Springer-Verlag Berlin Heidelberg, 2002.