[R-sig-Geo] geostatistic memory issue

[Robert J. Hijmans]

Fernando,

You can use the raster package for dealing with memory issues like this:

library(fields)
fit <- Krig(ozone$x, ozone$y, theta=20)

library(raster)
# create a target RasterLayer
r <- raster(xmn=min(ozone$x), xmx=max(ozone$x), ymn=min(ozone$y),
ymx=max(ozone$y))
res(r) <- 1/6
x <- interpolate(r, fit)
plot(x)
x


in your case, that would be something like this:
...
r <- raster(xmn=723604,xmx=724904,ymn=9661020,ymx=9662348)
res(r) <- 4
...

Robert

On Mon, Nov 29, 2010 at 3:54 PM, Fernando Santo <fdbesanto at gmail.com> wrote:
> Dear All,
>
> I am running a geostatistical analysis using the package "fields" of
> n=980 and grid of approximately 1x1 km with 4 m of spatial resolution.
> Because the fine grid resolution of 4 meters, which I really need for my
> experiment, I am having problems with memory allocation.  I am using an
> OS linux 32 bit (i686-pc-linux-gnu) with 4 Gb of memory and 2 Gb of swap
> and R version 2.12.0.  Running the R command gc() in my computer, I have
> the following:
>
>  >gc()
>             used        (Mb)   gc      trigger      (Mb)      max used
> (Mb)
> Ncells   147153      4.0              350000      9.4           350000   9.4
> Vcells    86617       0.7              786432       6.0
> 434604   3.4
>
> I tried to work around of my memory issue (?Memory) for Linux, start R
> with the command *R --min-vsize=100M --max-vsize=3G --min-nsize=100M
> --max-nsize=3G*, but still I did not saved the problem of memory
> allocation.  I know that the solution could be: (1) install a OS 64bits
> or (2) reduce the spatial resolution of my grid (e.g. for 10x10 m).
> However, I can not format my computer right now and I need a grid of 4 x
> 4 m.
>
> I would like to ask you the following:
> 1 - How I could increase the memory allocation of my linux?  Is there
> something wrong with my work around of memory allocation showed above?
> 2 - Is possible to apply the use of the R package  e.g. "bigmemory" in
> my geostatistic script (please see bellow)?  If yes, how it could be?
> My problem is only in the last command of my script when I call the
> function "predict" of fields.
>
> My script is bellow:
>
> Any help will be very welcome.
> Thanks for your attention.
> Fernando
>
> ###########################################
> dat <- read.csv("/home/fernando/Desktop/data.csv")
> library(fields)
>
> # set  x-y  coordinates of image, considering the center of each pixel
> col_xcoord <- seq(723606,724902,by=4)         #  correct: 325
> row_ycoord <- seq(9661022,9662346,by=4)   #  correct: 332
> xmin = min(col_xcoord)
> ymin = min(row_ycoord)
> locs.x = col_xcoord-xmin
> locs.y = row_ycoord-ymin
> newlocs = expand.grid(locs.x,locs.y)
>
> locs = data.frame(dat$long-xmin,dat$lat-ymin)  # adjusted location as
> data frame
> sqrt.gf = sqrt(dat$gap.frac)                                   # The
> response variable
>
> #################################################
> # Produce the Kriging estimates using exponential variogram with scale
> parameter 24.15
> #################################################
>
> fit = Krig(locs,sqrt.gf,theta=24.15)
> # I used the range parameter 24.15 estimated earlier
>
> summary(fit) # summary of fit
>
> # HERE I HAD THE ERROR OF MEMORY ALLOCATION
> # "predict" the kriging values
> krig.out = predict(fit,newlocs)
> # Error: cannot allocate vector of size 806.7 Mb
>
> # This takes about 2 minutes on Mac of my friend
> # but it did not work in my linux
>
>
>
>
>
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>
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