R: Conditional simulation of Gaussian random fields

condrgp {SpatialExtremes} | R Documentation

## 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)

[Package SpatialExtremes version 2.0-5 Index]