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)
# }
Community examples
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