# condrgp: Conditional simulation of Gaussian random fields In SpatialExtremes: Modelling Spatial Extremes

## Description

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

## Usage

 ```1 2``` ```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

`kriging`, `rgp`.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45``` ```## 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) ```

### Example output

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SpatialExtremes documentation built on May 2, 2019, 5:45 p.m.