[R] grid with random or clustered distribution

[SH]

Hi Sarah,

Thanks for your prompt responding.  The methodology in the publication is
very similar to what I plan to do.  Yes, could you be willing to share the
code if you don't mind?

Thanks a lot again,

Steve

On Wed, Sep 9, 2015 at 9:11 AM, Sarah Goslee <sarah.goslee at gmail.com> wrote:

> 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 <emptican at 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
>

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