nlm_gaussianfield: nlm_gaussianfield
In NLMR: Simulating Neutral Landscape Models

Description Usage Arguments Details References Examples

Simulates a spatially correlated random fields (Gaussian random fields) neutral landscape model.

nlm_gaussianfield(
  ncol,
  nrow,
  resolution = 1,
  autocorr_range = 10,
  mag_var = 5,
  nug = 0.2,
  mean = 0.5,
  user_seed = NULL,
  rescale = TRUE
)

`ncol`	[`numerical(1)`] Number of columns forming the raster.
`nrow`	[`numerical(1)`] Number of rows forming the raster.
`resolution`	[`numerical(1)`] Resolution of the raster.
`autocorr_range`	[`numerical(1)`] Maximum range (raster units) of spatial autocorrelation.
`mag_var`	[`numerical(1)`] Magnitude of variation over the entire landscape.
`nug`	[`numerical(1)`] Magnitude of variation in the scale of `autocorr_range`, smaller values lead to more homogeneous landscapes.
`mean`	[`numerical(1)`] Mean value over the field.
`user_seed`	[`numerical(1)`] Set random seed for the simulation
`rescale`	[`numeric(1)`] If `TRUE` (default), the values are rescaled between 0-1.

Gaussian random fields are a collection of random numbers on a spatially discrete set of coordinates (landscape raster). Natural sciences often apply them with spatial autocorrelation, meaning that objects which distant are more distinct from one another than they are to closer objects.

Kéry & Royle (2016) Applied Hierarchical Modeling in Ecology Chapter 20

# simulate random gaussian field
gaussian_field <- nlm_gaussianfield(ncol = 90, nrow = 90,
                                    autocorr_range = 60,
                                    mag_var = 8,
                                    nug = 5)

## Not run: 
# visualize the NLM
landscapetools::show_landscape(gaussian_field)

## End(Not run)