# grf: Simulation of Gaussian Random Fields In geoR: Analysis of Geostatistical Data

 grf R Documentation

## Simulation of Gaussian Random Fields

### Description

`grf()` generates (unconditional) simulations of Gaussian random fields for given covariance parameters.

### Usage

```grf(n, grid = "irreg", nx, ny, xlims = c(0, 1), ylims = c(0, 1),
borders, nsim = 1, cov.model = "matern",
cov.pars = stop("missing covariance parameters sigmasq and phi"),
kappa = 0.5, nugget = 0, lambda = 1, aniso.pars,
mean = 0, method, messages)

```

### Arguments

 `n` number of points (spatial locations) in each simulations. `grid` optional. An n x 2 matrix with coordinates of the simulated data. `nx` optional. Number of points in the X direction. `ny` optional. Number of points in the Y direction. `xlims` optional. Limits of the area in the X direction. Defaults to [0,1]. `ylims` optional. Limits of the area in the Y direction. Defaults to [0,1]. `borders` optional. Typically a two coluns matrix especifying a polygon. See DETAILS below. `nsim` Number of simulations. Defaults to 1. `cov.model` correlation function. See `cov.spatial` for further details. Defaults to the exponential model. `cov.pars` a vector with 2 elements or an n x 2 matrix with values of the covariance parameters sigma^2 (partial sill) and phi (range parameter). If a vector, the elements are the values of sigma^2 and phi, respectively. If a matrix, corresponding to a model with several structures, the values of sigma^2 are in the first column and the values of phi are in the second. `kappa` additional smoothness parameter required only for the following correlation functions: `"matern"`, `"powered.exponential"`, `"cauchy"` and `"gneiting.matern"`. More details on the documentation for the function `cov.spatial`. `nugget` the value of the nugget effect parameter tau^2. `lambda` value of the Box-Cox transformation parameter. The value lambda = 1 corresponds to no transformation, the default. For any other value of lambda Gaussian data is simulated and then transformed. `aniso.pars` geometric anisotropy parameters. By default an isotropic field is assumed and this argument is ignored. If a vector with 2 values is provided, with values for the anisotropy angle psi_A (in radians) and anisotropy ratio psi_A, the coordinates are transformed, the simulation is performed on the isotropic (transformed) space and then the coordinates are back-transformed such that the resulting field is anisotropic. Coordinates transformation is performed by the function `coords.aniso`. `mean` a numerical vector, scalar or the same length of the data to be simulated. Defaults to zero. `method` simulation method with options for `"cholesky"`, `"svd"`, `"eigen"`. Defaults to the Cholesky decomposition. See section `DETAILS` below.
 `messages` logical, indicating whether or not status messages are printed on the screen (or output device) while the function is running. Defaults to `TRUE`.

### Details

For the methods `"cholesky"`, `"svd"` and `"eigen"` the simulation consists of multiplying a vector of standardized normal deviates by a square root of the covariance matrix. The square root of a matrix is not uniquely defined. These three methods differs in the way they compute the square root of the (positive definite) covariance matrix.

The argument `borders`, if provides takes a polygon data set following argument `poly` for the splancs' function `csr`, in case of `grid="reg"` or `gridpts`, in case of `grid="irreg"`. For the latter the simulation will have approximately “n” points.

### Value

`grf` returns a list with the components:

 `coords` an n x 2 matrix with the coordinates of the simulated data. `data` a vector (if `nsim = 1`) or a matrix with the simulated values. For the latter each column corresponds to one simulation. `cov.model` a string with the name of the correlation function. `nugget` the value of the nugget parameter. `cov.pars` a vector with the values of sigma^2 and phi, respectively. `kappa` value of the parameter kappa. `lambda` value of the Box-Cox transformation parameter lambda. `aniso.pars` a vector with values of the anisotropy parameters, if provided in the function call. `method` a string with the name of the simulation method used. `sim.dim` a string "1d" or "2d" indicating the spatial dimension of the simulation. `.Random.seed` the random seed by the time the function was called. `messages` messages produced by the function describing the simulation. `call` the function call.

### Author(s)

Paulo Justiniano Ribeiro Jr. paulojus@leg.ufpr.br,
Peter J. Diggle p.diggle@lancaster.ac.uk.

### References

Wood, A.T.A. and Chan, G. (1994) Simulation of stationary Gaussian process in [0,1]^d. Journal of Computatinal and Graphical Statistics, 3, 409–432.

Schlather, M. (1999) Introduction to positive definite functions and to unconditional simulation of random fields. Tech. Report ST–99–10, Dept Maths and Stats, Lancaster University.

Schlather, M. (2001) Simulation and Analysis of Random Fields. R-News 1 (2), p. 18-20.

Further information on the package geoR can be found at:
http://www.leg.ufpr.br/geoR/.

`plot.grf` and `image.grf` for graphical output, `coords.aniso` for anisotropy coordinates transformation and `chol`, `svd` and `eigen` for methods of matrix decomposition.

### Examples

```sim1 <- grf(100, cov.pars = c(1, .25))
# a display of simulated locations and values
points(sim1)
# empirical and theoretical variograms
plot(sim1)
## alternative way
plot(variog(sim1, max.dist=1))
lines.variomodel(sim1)
#
# a "smallish" simulation
sim2 <- grf(441, grid = "reg", cov.pars = c(1, .25))
image(sim2)
##
## 1-D simulations using the same seed and different noise/signal ratios
##
set.seed(234)
sim11 <- grf(100, ny=1, cov.pars=c(1, 0.25), nug=0)
set.seed(234)
sim12 <- grf(100, ny=1, cov.pars=c(0.75, 0.25), nug=0.25)
set.seed(234)
sim13 <- grf(100, ny=1, cov.pars=c(0.5, 0.25), nug=0.5)
##
par(mfrow=c(3,1), mar=c(3,3,.5,.5))
yl <- range(c(sim11\$data, sim12\$data, sim13\$data))
image(sim11, type="l", ylim=yl)
image(sim12, type="l", ylim=yl)
image(sim13, type="l", ylim=yl)
par(par.ori)

## simulating within borders
data(parana)
pr1 <- grf(100, cov.pars=c(200, 40), borders=parana\$borders, mean=500)
points(pr1)
pr1 <- grf(100, grid="reg", cov.pars=c(200, 40), borders=parana\$borders)
points(pr1)
pr1 <- grf(100, grid="reg", nx=10, ny=5, cov.pars=c(200, 40), borders=parana\$borders)
points(pr1)
```

geoR documentation built on Aug. 9, 2022, 5:11 p.m.