# nlm_mpd

##### 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.

##### Value

RasterLayer

##### References

##### 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*