generateRF: Simulation of random fields with given marginals and...

In CoSMoS: Complete Stochastic Modelling Solution

Simulation of random fields with given marginals and spatiotemporal properties

Description

Generates a random field with given marginals and spatiotemporal properties. Provide (1) the output of fitVAR and (2) the number of time steps to simulate.

Usage

generateRF(n, STmodel)

Arguments

n	number of fields (time steps) to simulate
STmodel	list of arguments from fitVAR

Details

Referring to the documentation of fitVAR for details on computational complexity, here we report indicative simulation CPU times, assuming model parameters are already evaluated. CPU times refer to a Windows 10 Pro x64 laptop with Intel(R) Core(TM) i7-6700HQ CPU @ 2.60GHz, 4-core, 8 logical processors, and 32 GB RAM.
CPU time:
m = 30, p = 1, n = 1000: ~17s
m = 30, p = 1, n = 10000: ~75s
m = 30, p = 5, n = 100: ~280s
m = 30, p = 5, n = 1000: ~302s
m = 50, p = 1, n = 1000: ~160s
m = 50, p = 1, n = 10000: ~570s
where m denotes the side length of a square field (m x m).

Value

A matrix of class "matrix" with attribute STmodel. Rows correspond to spatial locations and columns to time steps.

See Also

fitVAR, checkRF, generateMTS

Examples

## The example below simulates few random fields of size 10x10 with AR(1)
## temporal correlation for illustration. For reliable performance assessment
## generate a larger number of fields (e.g. 100 or more) of size ~30x30.
## See 'Details' for running times with different settings.

fit <- fitVAR(
  spacepoints = 10,
  p = 1,
  margdist = "burrXII",
  margarg = list(scale = 3, shape1 = .9, shape2 = .2),
  p0 = 0.8,
  stcsid = "clayton",
  stcsarg = list(scfid = "weibull", tcfid = "weibull",
                 copulaarg = 2,
                 scfarg = list(scale = 20, shape = 0.7),
                 tcfarg = list(scale = 1.1, shape = 0.8))
)

sim <- generateRF(n = 12, STmodel = fit)
checkRF(sim, lags = 10, nfields = 12)

CoSMoS documentation built on May 8, 2026, 1:08 a.m.