geostatistic memory issue

Dear Robert,

Thanks a lot for your attention. ?I downloaded the raster package and
manual. ?I am still digesting all this information...
I just tried to do a quick adaptation in my code and it did not worked. ?I
am doing something wrong...

Fernando

# read the data
dat <- read.csv("/home/fernando/Desktop/data.csv")

library(fields)

# I just left how as before to make the fitting between the range of x and y
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) ? ? # response variable

fit = Krig(locs,sqrt.gf,theta=24.15)

##### ?then I used your suggestion
library(raster)
# create a target RasterLayer
r <- raster(xmn=723604,xmx=724904,ymn=9661020,ymx=9662348)
res(r) <- 4

# Sure It seems not correct....
x <- interpolate(r, fit)
Error in if (dataclasses != "try-error") { : argument is of length zero

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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