RandomFields (version 3.1.12)

RMmodelsSpacetime: Space-time Covariance Models

Description

Here, a collection of implemented space-time models is given.

Details

Stationary space-time models

Here, most of the models are composed models (operators). Note that in space-time modelling the argument proj may take also the values "space" for the projection on the space and "time" for the projection onto the time axis.

ll{ separable models are easily constructed using +, *, and proj, see also the example below RMave space-time moving average model RMcoxisham Cox-Isham model RMcurlfree curlfree (spatial) field (stationary and anisotropic) RMdivfree divergence free (spatial) vector valued field, (stationary and anisotropic) RMgennsst generalization of Gneiting's non-separable space-time model RMiaco non-separabel space-time model RMmastein Ma-Stein model RMnsst Gneiting's non-separable space-time model RMstein Stein's non-separabel space-time model RMstp Single temporal process RMtbm Turning bands operator }

References

• Schlather, M. (2011) Construction of covariance functions and unconditional simulation of random fields. In Porcu, E., Montero, J.M. and Schlather, M.,Space-Time Processes and Challenges Related to Environmental Problems.New York: Springer.

See Also

RFformula, RM, RMmodels, RMmodelsAdvanced

Examples

RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
##                   RFoptions(seed=NA) to make them all random again
StartExample()
## separable model with expontential model in space and gaussian in time
model <- RMexp(proj = "space") * RMgauss(proj = "time")
xT <- seq(0, 10, 0.1)
z <- RFsimulate(model, x=xT, T=xT)
plot(z)

FinalizeExample()