krigeSTSimTB: conditional/unconditional spatio-temporal simulation

Description

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.

Examples

Run this code

# NOT RUN {
# see demo('circEmbeddingMeuse')
# }