# RPdirect: Methods relying on square roots of the covariance matrix In RandomFields: Simulation and Analysis of Random Fields

## Description

Methods relying on square roots of the covariance matrix

## Usage

 `1` ```RPdirect(phi, boxcox) ```

## Arguments

 `phi` object of class `RMmodel`; specifies the covariance model to be simulated. `boxcox` the one or two parameters of the box cox transformation. If not given, the globally defined parameters are used. See `RFboxcox` for details.

## Details

`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.

## Value

`RPdirect` returns an object of class `RMmodel`.

## References

• 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.

Gaussian, RP, RPsequential.

## Examples

 ```1 2 3 4 5 6 7``` ```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) ```

RandomFields documentation built on Feb. 6, 2020, 5:13 p.m.