This is a wrapper for the `RFsimulate`

function in the `RandomFields`

package. The main addition is the `speedup`

argument which allows
for faster map generation. A `speedup`

of 1 is normal and will get
progressively faster as the number increases, at the expense of coarser
pixel resolution of the pattern generated.

gaussMap(x, scale = 10, var = 1, speedup = 1, method = "RMexp",
alpha = 1, inMemory = FALSE, ...)

## Arguments

x |
A spatial object (e.g., a `RasterLayer` ). |

scale |
The spatial scale in map units of the Gaussian pattern. |

var |
Spatial variance. |

speedup |
An numeric value indicating how much faster than 'normal'
to generate maps. It may be necessary to give a value larger
than 1 for large maps. Default is 1. |

method |
The type of model used to produce the Gaussian pattern.
Should be one of `"RMgauss"` (Gaussian covariance model),
`"RMstable"` (the stable powered exponential model),
or the default, `"RMexp"` (exponential covariance model). |

alpha |
A required parameter of the 'RMstable' model.
Should be in the interval [0,2] to provide a valid covariance function.
Default is 1. |

inMemory |
Should the RasterLayer be forced to be in memory?
Default `FALSE` . |

... |
Additional arguments to `raster` . |

## Value

A raster map with same extent as `x`

, with a Gaussian random pattern.

## See also

## Examples

# NOT RUN {
library(RandomFields)
library(raster)
nx <- ny <- 100L
r <- raster(nrows = ny, ncols = nx, xmn = -nx/2, xmx = nx/2, ymn = -ny/2, ymx = ny/2)
speedup <- max(1, nx/5e2)
map1 <- gaussMap(r, scale = 300, var = 0.03, speedup = speedup, inMemory = TRUE)
Plot(map1)
# with non-default method
map1 <- gaussMap(r, scale = 300, var = 0.03, method = "RMgauss")
# }