nlm_mosaicgibbs: nlm_mosaicgibbs
In NLMR: Simulating Neutral Landscape Models

Description Usage Arguments Details Value References Examples

Simulate a neutral landscape model using the Gibbs algorithm introduced in Gaucherel (2008).

nlm_mosaicgibbs(
  ncol,
  nrow,
  resolution = 1,
  germs,
  R,
  patch_classes,
  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.
`germs`	[`numerical(1)`] Intensity parameter (non-negative integer).
`R`	[`numerical(1)`] Interaction radius (non-negative integer) for the fitting of the spatial point pattern process - the min. distance between germs in map units.
`patch_classes`	[`numerical(1)`] Number of classes for germs.
`rescale`	[`logical(1)`] If `TRUE` (default), the values are rescaled between 0-1.

nlm_mosaicgibbs offers the second option of simulating a neutral landscape model described in Gaucherel (2008). The method works in principal like the tessellation method (nlm_mosaictess), but instead of a random point pattern the algorithm fits a simulated realization of the Strauss process. The Strauss process starts with a given number of points and uses a minimization approach to fit a point pattern with a given interaction parameter (0 - hardcore process; 1 - Poisson process) and interaction radius (distance of points/germs being apart).

RasterLayer

Gaucherel, C. (2008) Neutral models for polygonal landscapes with linear networks. Ecological Modelling, 219, 39 - 48.

# simulate polygonal landscapes
mosaicgibbs <- nlm_mosaicgibbs(ncol = 40,
                              nrow = 30,
                              germs = 20,
                              R = 0.02,
                              patch_classes = 12)

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

## End(Not run)