Methods relying on square roots of the covariance matrix
Methods relying on square roots of the covariance matrix
RPdirect(phi, boxcox)
RPdirect
is based on the well-known method for simulating
any multivariate Gaussian distribution, using the square root of the
covariance matrix. The method is pretty slow and limited to
about 12000 points, i.e. a 20x20x20 grid in three dimensions.
This implementation can use the Cholesky decomposition and
the singular value decomposition.
It allows for arbitrary points and arbitrary grids.
Martin Schlather, schlather@math.uni-mannheim.de, https://www.wim.uni-mannheim.de/schlather/
Schlather, M. (1999) An introduction to positive definite functions and to unconditional simulation of random fields. Technical report ST 99-10, Dept. of Maths and Statistics, Lancaster University.
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set ## RFoptions(seed=NA) to make them all random again model <- RMgauss(var=10, s=10) + RMnugget(var=0.01) plot(model, xlim=c(-25, 25)) z <- RFsimulate(model=RPdirect(model), 0:10, 0:10, n=4) plot(z)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.