DoSimulateRF
performs an already initialised simulation.
InitSimulateRF
internal function;
use DoSimulateRF(n=1, register=0, paired=FALSE, trend=NULL)InitSimulateRF(x, y=NULL, z=NULL, T=NULL, grid=!missing(gridtriple),
model, param, trend, method=NULL, register=0, gridtriple,
distribution=NA)
x
,
y
, and z
should be
interpreted as a grid definition, see Details.()
to get all optionsparam=c(mean, variance, nugget, scale, ...)
,
param=list(c(variance, scale,
...), ..., c(variance,scale,...))
,
param=matrix(...)
, or
param=list(list(variance, anisotropy,
NULL
or string; Method used for simulating,
see ()
to get all optiogridtriple=FALSE
ascending
sequences for the parameters
x
, y
, and z
are
expected; if gridtriple=TRUE
triples of form
c(start,end,step)
expectepaired=TRUE
then n
must be even.paired
may be TRUE
only for the simulation of
Gaussian random fields.
If TRUE
then every second simulation is obtained by
only changing the signs of the standard Gaussian random variables, the
trend
is a non-negative integer (monomials
up to order k as trend functions), a list of functions or a formula (the
summands are the trend functions); you have InitSimulateRF
returns 0 if no error has occurred during the
initialisation process, and a positive value if failed.
DoSimulateRF
returns NULL
if an error has occurred; otherwise the returned object
depends on the parameters n
and grid
:
n=1
:
* grid=FALSE
. A vector of simulated values is
returned (independent of the dimension of the random field)
* grid=TRUE
. An array of the dimension of the
random field is returned.
n>1
:
* grid=FALSE
. A matrix is returned. The columns
contain the realisations.
* grid=TRUE
. An array of dimension
$d+1$, where $d$ is the dimension of
the random field, is returned. The last
dimension contains the realisations.RandomFields