You can use gstat, as in: https://www.researchgate.net/publication/43279659_Behavior_of_Vegetation_Sampling_Methods_in_the_Presence_of_Spatial_Autocorrelation
If you need more detail, I can dig up the code. Sarah On Wed, Sep 9, 2015 at 8:49 AM, SH <empti...@gmail.com> wrote: > Hi R-users, > > I hope this is not redundant questions. I tried to search similar threads > relevant to my questions but could not find. Any input would be greatly > appreciated. > > I want to generate grid with binary values (1 or 0) in n1 by n2 (e.g., 100 > by 100 or 200 by 500, etc.) given proportions of 1 and 0 values (e.g., 1, > 5, or 10% of 1 from 100 by 100 grid). For clustered distributed grid, I > hope to be able to define cluster size if possible. Is there a simple way > to generate random/clustered grids with 1 and 0 values with a > pre-defined proportion? > > So far, the function "EVariogram" in the "CompRandFld" package generates > clustered grid with 1 and 0. Especially, the example #4 in the > "EVariogram" function description is a kind of what I want. Below is the > slightly modified code from the original one. However, the code below > can't control proportion of 1 and 0 values and complicated or I have no > idea how to do it. I believe there may be easies ways to > generate random/clustered grids with proportional 1 and 0 values. > > Thank you very much in advance, > > Steve > > > library(CompRandFld) > library(RandomFields) > > x0 <- seq(1, 50, length.out=50) > y0 <- seq(1, 60, length.out=60) > d <- expand.grid(x=x0, y=y0) > dim(d) > head(d) > x <- d$x > y <- d$y > # Set the model's parameters: > corrmodel <- 'exponential' > mean <- 0 > sill <- 1 > nugget <- 0 > scale <- 3 > set.seed(1221) > # Simulation of the Binary-Gaussian random field: > data <- RFsim(x, y, corrmodel="exponential", model="BinaryGauss", > param=list(mean=mean,sill=sill,scale=scale,nugget=nugget), > threshold=0)$data > # Empirical lorelogram estimation: > fit <- EVariogram(data, x, y, numbins=20, maxdist=7, type="lorelogram") > # Results: > plot(fit$centers, fit$variograms, xlab='Distance', ylab="Lorelogram", > ylim=c(min(fit$variograms), max(fit$variograms)), > xlim=c(0, max(fit$centers)), pch=20, main="Spatial Lorelogram") > # Plotting > plot(d, type='n') > text(d, label=data) > -- Sarah Goslee http://www.functionaldiversity.org ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
