Create a random raster with or without spatial dependence

rSpatialDepRast() creates a raster with random values in cells. Across the raster, values are approximately normally distributed, though a raster with a "true" normal distribution can be made with rnormRast(). Spatial dependence can be introduced, though all together the values will still be approximately normally distributed.

Usage

# S4 method for class 'GRaster'
rSpatialDepRast(
  x,
  n = 1,
  mu = 0,
  sigma = 1,
  dist = 0,
  exponent = 1,
  delay = 0,
  seed = NULL
)

Arguments

x

A GRaster: The output will have the same extent and dimensions as this raster.

n

An integer: Number of rasters to generate.

mu, sigma

Numeric: Mean and sample standard deviation of output. If creating more than one raster, you can provide one value per raster. If there are fewer, they will be recycled.

dist

Numeric: Maximum distance of spatial autocorrelation (in map units–typically meters). Default is 0 (no spatial autocorrelation). If creating more than one raster, you can provide one value per raster. If there are fewer, values will be recycled.

exponent

Numeric > 0: Distance decay exponent. If creating more than one raster, you can provide one value per raster. If there are fewer, values will be recycled.

delay

Numeric >= 0: Values >0 force the distance decay of similarity to remain constant up to this distance. Beyond this distance, the decay exponent takes effect. Default is 0. If creating more than one raster, you can provide one value per raster. If there are fewer, values will be recycled.

seed

Numeric integer or NULL: Random seed. If NULL, then a random seed is used for each raster. If provided, there should be one seed value per raster.

Value

A GRaster.

See also

rnormRast(), fractalRast(), runifRast(), and GRASS module r.random.surface

Examples

if (grassStarted()) {

# Setup
library(sf)
library(terra)

# Elevation raster
madElev <- fastData("madElev")

# Convert a SpatRaster to a GRaster:
elev <- fast(madElev)

### Create a raster with values drawn from a uniform distribution:
unif <- runifRast(elev)
plot(unif)

### Create a raster with values drawn from a normal distribution:
norms <- rnormRast(elev, n = 2, mu = c(5, 10), sigma = c(2, 1))
plot(norms)
hist(norms, bins = 100)

# Create a raster with random, seemingly normally-distributed values:
rand <- rSpatialDepRast(elev, dist = 1000)
plot(rand)

# Values appear normal on first inspection:
hist(rand)

# ... but actually are patterned:
hist(rand, bins = 100)

# Create a fractal raster:
fractal <- fractalRast(elev, n = 2, dimension = c(2.1, 2.8))
plot(fractal)
hist(fractal)

}