Conditional simulation of Gaussian random fields
This function generates conditional simulation of Gaussian random fields from the simple kriging predictor.
condrgp(n, coord, data.coord, data, cov.mod = "powexp", mean = 0, sill = 1, range = 1, smooth = 1, grid = FALSE, control = list())
n |
Integer. The number of conditional simulations. |
coord |
A numeric vector or matrix specifying the coordinates
where the process has to be generated. If |
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 |
control |
A named list passing options to the simulation method
of Gaussian processes — see |
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 |
Mathieu Ribatet
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.