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RandomFields (version 3.1.16)

RMmodelsSpacetime: Space-time Covariance Models

Description

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

Arguments

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.

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

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

Run this code
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
##                   RFoptions(seed=NA) to make them all random again

## 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)


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