condrgp: Conditional simulation of Gaussian random fields

In SpatialExtremes: Modelling Spatial Extremes

Conditional simulation of Gaussian random fields

Description

This function generates conditional simulation of Gaussian random fields from the simple kriging predictor.

Usage

condrgp(n, coord, data.coord, data, cov.mod = "powexp", mean = 0, sill =
1, range = 1, smooth = 1, grid = FALSE, control = list())

Arguments

n	Integer. The number of conditional simulations.
coord	A numeric vector or matrix specifying the coordinates where the process has to be generated. If coord is a matrix, each row specifies one location.
data.coord	A numeric vector or matrix specifying the coordinates where the process is conditioned.
data	A numeric vector giving the conditioning observations.
cov.mod	A character string specifying the covariance function family. Must be one of "whitmat", "powexp", "cauchy" or "bessel" for the Whittle-Mater, the powered exponential, the Cauchy or Bessel covariance families.
mean,sill,range,smooth	The mean, sill, range and smooth of the Gaussian process.
grid	Logical. Does coord specifies a grid?
control	A named list passing options to the simulation method of Gaussian processes — see rgp.

Value

A list with components:

coord	The coordinates at which the process was simulated;
cond.sim	The simulated process;
data.coord	The coordinates of the conditioning locations;
data	The conditioning observations;
cov.mod	The covariance function family;
grid	Does coord specifies a grid?

Author(s)

Mathieu Ribatet

See Also

kriging, rgp.

Examples

## Several conditional simulations
n.site <- 50
n.sim <- 512

x.obs <- runif(n.site, -100, 100)
x.sim <- seq(-100, 100, length = n.sim)

data <- rgp(1, x.obs, "whitmat", sill = 1, range = 10, smooth = 0.75)

sim <- condrgp(5, x.sim, x.obs, data, "whitmat", sill = 1, range =
10, smooth = 0.75)

matplot(x.sim, t(sim$cond.sim),  type = "l", lty = 1, xlab = "x", ylab =
expression(Y[cond](x)))
points(x.obs, data, pch = 21, bg = 1)
title("Five conditional simulations")

## Comparison between one conditional simulations and the kriging
## predictor on a grid
x.obs <- matrix(runif(2 * n.site, -100, 100), ncol = 2)
x <- y <- seq(-100, 100, length = 100)
x.sim <- cbind(x, y)

data <- rgp(1, x.obs, "whitmat", sill = 1, range = 50, smooth = 0.75)

krig <- kriging(data, x.obs, x.sim, "whitmat", sill = 1, range = 50,
smooth = 0.75, grid = TRUE)
sim <- condrgp(1, x.sim, x.obs, data, "whitmat", sill = 1, range = 50,
smooth = 0.75, grid = TRUE)

z.lim <- range(c(sim$cond.sim, data, krig$krig.est))
breaks <- seq(z.lim[1], z.lim[2], length = 65)
col <- heat.colors(64)
idx <- as.numeric(cut(data, breaks))

op <- par(mfrow = c(1,2))
image(x, y, krig$krig.est, col = col, breaks = breaks)
points(x.obs, bg = col[idx], pch = 21)
title("Kriging predictor")
image(x, y, sim$cond.sim, col = col, breaks = breaks)
points(x.obs, bg = col[idx], pch = 21)
title("Conditional simulation")
## Note how the background colors of the above points matches the ones
## returned by the image function
par(op)

SpatialExtremes documentation built on April 19, 2022, 5:06 p.m.