# nlm_mpd

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##### nlm_mpd

Simulates a midpoint displacement neutral landscape model.

##### Usage
nlm_mpd(ncol, nrow, resolution = 1, roughness = 0.5, rand_dev = 1,
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.

rescale

[logical(1)] If TRUE (default), the values are rescaled between 0-1.

verbose

[logical(1)] If TRUE (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.

• Diamond 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.

RasterLayer

##### References

https://en.wikipedia.org/wiki/Diamond-square_algorithm

• nlm_mpd
##### Examples
# NOT RUN {
# simulate midpoint displacement
midpoint_displacememt <- nlm_mpd(ncol = 100,
nrow = 100,
roughness = 0.3)
# }
# NOT RUN {
# visualize the NLM
landscapetools::show_landscape(midpoint_displacememt)
# }

Documentation reproduced from package NLMR, version 0.4.2, License: GPL-3

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