R: Space-time Covariance Models

RMmodelsSpacetime {RandomFields}

R Documentation

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.

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

Author(s)

Alexander Malinowski, malinowski@math.uni-mannheim.de

Martin Schlather, schlather@math.uni-mannheim.de

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.

Examples

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)