Simulates a midpoint displacement neutral landscape model.
Usage
nlm_mpd(
ncol,
nrow,
resolution = 1,
roughness = 0.5,
rand_dev = 1,
torus = FALSE,
rescale = TRUE,
verbose = TRUE
)Arguments
- 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.- roughness
[
numerical(1)]
Controls the level of spatial autocorrelation (!= Hurst exponent)- rand_dev
[
numerical(1)]
Initial standard deviation for the displacement step (default == 1), sets the scale of the overall variance in the resulting landscape.- torus
[
logical(1)]
Logical value indicating wether the algorithm should be simulated on a torus (default FALSE)- rescale
[
logical(1)]
IfTRUE(default), the values are rescaled between 0-1.- verbose
[
logical(1)]
IfTRUE(default), the user gets a warning that the functions changes the dimensions to an appropriate one for the algorithm.
Details
The algorithm is a direct implementation of the midpoint displacement algorithm. It performs the following steps:
Initialization: Determine the smallest fit of
max(ncol, nrow)in n^2 + 1 and assign value to n. Setup matrix of size (n^2 + 1)*(n^2 + 1). Afterwards, assign a random value to the four corners of the matrix.Square Step: For each square in the matrix, assign the average of the four corner points plus a random value to the midpoint of that square.
Diamond Step: For each diamond in the matrix, assign the average of the four corner points plus a random value to the midpoint of that diamond.
At each iteration the roughness, an approximation to common Hurst exponent, is reduced.
Examples
# simulate midpoint displacement
midpoint_displacememt <- nlm_mpd(ncol = 100,
nrow = 100,
roughness = 0.3)
#> Warning: nlm_mpd changes the dimensions of the RasterLayer if even ncols/nrows are choosen.
if (FALSE) {
# visualize the NLM
landscapetools::show_landscape(midpoint_displacememt)
}