nlm_randomcluster

0th

Percentile

nlm_randomcluster

Simulates a random cluster nearest-neighbour neutral landscape.

Usage
nlm_randomcluster(ncol, nrow, resolution = 1, p, ai = c(0.5, 0.5),
  neighbourhood = 4, rescale = TRUE)
Arguments
ncol

[integer(1)] Number of columns forming the raster.

nrow

[integer(1)] Number of rows forming the raster.

resolution

[numerical(1)] Resolution of the raster.

p

[numerical(1)] Defines the proportion of elements randomly selected to form clusters.

ai

Vector with the cluster type distribution (percentages of occupancy). This directly controls the number of types via the given length.

neighbourhood

[numerical(1)] Clusters are defined using a set of neighbourhood structures, 4 (Rook's or von Neumann neighbourhood) (default), 8 (Queen's or Moore neighbourhood).

rescale

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

Details

This is a direct implementation of steps A - D of the modified random clusters algorithm by Saura & Mart<U+00ED>nez-Mill<U+00E1>n (2000), which creates naturalistic patchy patterns.

Value

Raster with random values ranging from 0-1.

References

Saura, S. & Mart<U+00ED>nez-Mill<U+00E1>n, J. (2000) Landscape patterns simulation with a modified random clusters method. Landscape Ecology, 15, 661 <U+2013> 678.

Aliases
  • nlm_randomcluster
Examples
# NOT RUN {
# simulate random clustering
random_cluster <- nlm_randomcluster(ncol = 30, nrow = 30,
                                     p = 0.4,
                                     ai = c(0.25, 0.25, 0.5))
# }
# NOT RUN {
# visualize the NLM
landscapetools::show_landscape(random_cluster)
# }
# NOT RUN {
# }
Documentation reproduced from package NLMR, version 0.4.2, License: GPL-3

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