conditional/unconditional spatio-temporal simulation

conditional/unconditional spatio-temporal simulation based on turning bands

Usage

krigeSTSimTB(formula, data, newdata, modelList, nsim, progress = TRUE, 
             nLyrs = 500, tGrid = NULL, sGrid = NULL, ceExt = 2, nmax = Inf)

Arguments

formula: the formula of the kriging predictor
data: conditioning data
newdata: locations in space and time where the simulation is carried out
modelList: the spatio-temporal variogram (from vgmST) defining the spatio-temporal covariance structure of the simulated Gaussian random field
nsim: number of simulations
progress: boolean; whether the progress should be shown in progress bar
nLyrs: number of layers used in the turning bands approach (default = 500)
tGrid: optional explicit temporal griding that shall be used
sGrid: optional explicit spatial griding that shall be used
ceExt: expansion in the circulant embedding, defaults to 2
nmax: number of nearest neighbours that shall e used, defaults to 'Inf' meaning all available points are used

Value

a spatio-temporal data frame with nSim simulations

References

Turning bands

Lantuejoul, C. (2002) Geostatistical Simulation: Models and Algorithms. Springer.

Matheron, G. (1973). The intrinsic random functions and their applications. Adv. Appl. Probab., 5, 439-468.

Strokorb, K., Ballani, F., and Schlather, M. (2014) Tail correlation functions of max-stable processes: Construction principles, recovery and diversity of some mixing max-stable processes with identical TCF. Extremes, Submitted.

Turning layers

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.

Author

Benedikt Graeler

Examples

# see demo('circEmbeddingMeuse')