I have a raster stack, one of the raster layers is the mean prediction of a property for that pixel, the second layer is the variance of the prediction.
I would like to write a function for generating a sample (n=50) from the Gaussian distribution, for each pixel, 50 simulations. And store the output in a raster brick or similar.
When I try directly with the function rnorm I get error message. When I try with overlay (raster), using rnorm as input function, also.
Does anyone have a clue of how doing this?
I did not calculate the spatial variograms, the soil property was predicted simply with regression.
r <- raster(nrow=10, ncol=10) s1 <- setValues(r,runif(n = 100, min = 0, max=50)) s2 <- setValues(r,runif(n = 100, min = 1, max=5)) N <- setValues(r,50) test <- overlay(N,s1,s2,fun=rnorm) test class : RasterBrick dimensions : 10, 10, 100, 3 (nrow, ncol, ncell, nlayers) resolution : 36, 18 (x, y) extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax) coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0 data source : in memory names : layer.1, layer.2, layer.3 min values : -2.551580, -1.781912, -2.879981 max values : 2.533998, 2.245894, 2.828349 test <- rnorm(n=N, mean=s1,sd=s2) Error in rnorm(n = N, mean = s1, sd = s2) : arguments incorrects