random.raster

From spatialEco v1.3-2 by Jeffrey S Evans

Random raster

Create a random raster or raster stack using specified distribution

Usage

random.raster(
  r = NULL,
  n.row = 50,
  n.col = 50,
  n.layers = 1,
  x = seq(1, 10),
  min = 0,
  max = 1,
  mean = 0,
  sd = 1,
  p = 0.5,
  s = 1.5,
  distribution = c("random", "normal", "seq", "binominal", "gaussian")
)

Arguments

r: Optional existing raster defining nrow/ncol
n.row: Number of rows
n.col: Number of columns
n.layers: Number of layers in resulting raster stack
x: A vector of values to sample if distribution is "sample"
min: Minimum value of raster
max: Maximum value of raster
mean: Mean of centered distribution
sd: Standard deviation of centered distribution
p: p-value for binominal distribution
s: sigma value for Gaussian distribution
distribution: Available distributions, c("random", "normal", "seq", "binominal", "gaussian", "sample")

Details

Options for distributions are for random, normal, seq, binominal, gaussian and sample raster(s)

Value

RasterLayer or RasterStack object with random rasters

Examples

# NOT RUN {
library(raster)

# Using existing raster to create random binominal  
r <- raster(system.file("external/rlogo.grd", package="raster")) 
r <- random.raster(r, distribution="binominal")

# default; random, nrows=50, ncols=50, nlayers=1
rr <- random.raster(n.layer=5)

# specified; binominal, nrows=20, ncols=20, nlayers=5
rr <- random.raster(n.layer=5, n.col=20, n.row=20,  
                    distribution="binominal")

# specified; gaussian, nrows=50, ncols=50, nlayers=1
rr <- random.raster(n.col=50, n.row=50, s=8,  
                    distribution="gaussian")

# specified; sample, nrows=50, ncols=50, nlayers=1
rr <- random.raster(n.layer=1, x=c(2,6,10,15), distribution="sample" )
  freq(rr)

# }

Documentation reproduced from package spatialEco, version 1.3-2, License: GPL-3

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