random.raster

0th

Percentile

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

Aliases
  • random.raster
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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