R: Simulation of Random Fields

SimulateRF {RandomFields}

R Documentation

Simulation of Random Fields

Description

DoSimulateRF performs an already initialised simulation.

InitSimulateRF internal function; use InitGaussRF and InitMaxStableRF, instead.

Usage

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)

Arguments

`x`	matrix of coordinates, or vector of x coordinates
`y`	vector of y coordinates
`z`	vector of z coordinates
`T`	time instances
`grid`	logical; determines whether the vectors `x`, `y`, and `z` should be interpreted as a grid definition, see Details.
`model`	string; covariance or variogram model, see `CovarianceFct`, or type `PrintModelList()` to get all options
`param`	vector or list. `param=c(mean, variance, nugget, scale, ...)`, `param=list(c(variance, scale, ...), ..., c(variance,scale,...))`, `param=matrix(...)`, or `param=list(list(variance, anisotropy, kappa),..., list(variance, anisotropy, kappa))`; the parameters must be given in this order; further parameters are to be added in case of a parametrised class of models, see `CovarianceFct`

`method`	`NULL` or string; Method used for simulating, see `RFMethods`, or type `PrintMethodList()` to get all options
`register`	0:9; place where intermediate calculations are stored; the numbers are aliases for 10 internal registers
`gridtriple`	logical; if `gridtriple=FALSE` ascending sequences for the parameters `x`, `y`, and `z` are expected; if `gridtriple=TRUE` triples of form `c(start,end,step)` expected; this parameter is used only if `grid=TRUE`
`distribution`	marginal distribution: 'Gauss', 'Poisson', or 'MaxStable'
`n`	number of realisations to generate; if `paired=TRUE` then `n` must be even.
`paired`	logical. `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 simulation is based on (“antithetic pairs”).
`trend`	only used for universal and intrinsic kriging. In case of universal kriging `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 the choice of using either x, y, z or X1, X2, X3,... as spatial variables; in case of intrinsic kriging trend should be a nonnegative integer which is the order of the underlying model.

Value

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.

Author(s)