(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.
# S3 method for modelKriging
simulate(
object,
nsim = 1,
seed = NA,
xsim,
conditionalSimulation = TRUE,
returnAll = FALSE,
...
)Returned value depends on the setting of object$simulationReturnAll
fit of the Kriging model (settings and parameters), of class modelKriging.
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
logical, if set to TRUE (default), the simulation is conditioned with the training data of the Kriging model. Else, the simulation is non-conditional.
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
N. A. Cressie. Statistics for Spatial Data. JOHN WILEY & SONS INC, 1993.
C. Lantuejoul. Geostatistical Simulation - Models and Algorithms. Springer-Verlag Berlin Heidelberg, 2002.
modelKriging, predict.modelKriging