Thank you so much! I will try it!
On Wed, Sep 9, 2015 at 3:27 PM, Sarah Goslee <sarah.gos...@gmail.com> wrote:
> ########################################################
> ### simulate landscapes with spatial autocorrelation ###
> ### Sarah Goslee 2015-09-09 ###
> ### Goslee 2006 PLANT ECOLOGY 187(2):203-212 ###
> ########################################################
>
> library(gstat)
>
>
> ## parameters
> abun <- 0.2
> dim1 <- 20
> dim2 <- 50
>
>
> ## setup
> xy <- expand.grid(seq_len(dim1), seq_len(dim2))
> names(xy) <- c("x","y")
>
>
> ## three sample simulations
>
> # no spatial autocorrelation
> g.dummy <- gstat(formula = z~x+y, locations = ~x+y, dummy = TRUE, beta
> = 0, model = vgm(1,"Nug", 0), nmax = 50)
> sim <- predict(g.dummy, newdata = xy, nsim = 1)
> random.landscape.000 <- predict(g.dummy, newdata = xy, nsim = 1)
> random.landscape.000[,3] <- ifelse(random.landscape.000[,3] >
> quantile(random.landscape.000[,3], abun), 0, 1)
>
> # little spatial autocorrelation
> g.dummy <- gstat(formula = z~x+y, locations = ~x+y, dummy = TRUE, beta
> = 0, model = vgm(1,"Exp", 5), nmax = 50)
> random.landscape.005 <- predict(g.dummy, newdata = xy, nsim = 1)
> random.landscape.005[,3] <- ifelse(random.landscape.005[,3] >
> quantile(random.landscape.005[,3], abun), 0, 1)
>
> # much spatial autocorrelation
> g.dummy <- gstat(formula = z~x+y, locations = ~x+y, dummy = TRUE, beta
> = 0, model = vgm(1,"Exp", 250), nmax = 50)
> sim <- predict(g.dummy, newdata = xy, nsim = 1)
> random.landscape.250 <- predict(g.dummy, newdata = xy, nsim = 1)
> random.landscape.250[,3] <- ifelse(random.landscape.250[,3] >
> quantile(random.landscape.250[,3], abun), 0, 1)
>
>
> # plot the simulated landscapes
> par(mfrow=c(1,3))
> image(random.landscape.000, main="Null", xaxt="n", yaxt="n", bty="n",
> xlim=c(0,dim1), ylim=c(0, dim2), col=c("lightgray", "darkgray"))
> image(random.landscape.005, main="5", xaxt="n", yaxt="n", bty="n",
> xlim=c(0,dim1), ylim=c(0, dim2), col=c("lightgray", "blue"))
> image(random.landscape.250, main="250", sub=paste("abun =", abun),
> xaxt="n", yaxt="n", bty="n", xlim=c(0,dim1), ylim=c(0, dim2),
> col=c("lightgray", "darkblue"))
>
> ########################################################
> ### end ###
> ########################################################
>
> On Wed, Sep 9, 2015 at 9:27 AM, SH <empti...@gmail.com> wrote:
> > 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.gos...@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 <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
> >
> >
>
>
>
> --
> Sarah Goslee
> http://www.stringpage.com
> http://www.sarahgoslee.com
> http://www.functionaldiversity.org
>
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