Roger,

I end splitting the data for the predictors, 3 sections and then imported
them to R. Your are right, I am calibrating the model using 163 obs but I do
not understand what you meant with four orders of magnitude more grid
cells?. However, I tried the suggested code but still the regression kriging
can not be completed for memory allocation. In some of the data had to be
splitted in smaller areas.

Thanks,

Jose

 But no use of maxdist= or nmax=? I would have said:
>
>  g1 <- gstat(formula=yststep$call$formula, data=ystdec3, model=vgf1yst)
>  ystrk <- predict(g1, newdata=predictors)
>
>  anyway, which shows exactly how to resolve the problem - subset ystdec3
>  into tiles and patch them together again afterwards (patching may need
>  thinking through clearly). I assume that you have now successfully read
in
>  the predictors? Do I understand that you are calibrating your model on
163
>  observations, and predicting for roughly four orders of magnitude more
>  grid cells?
>
>  gc() can help to clean out garbage sometimes - say in a loop, gctorture()
>  really only during development, not in regular use.
>
>  Roger

On Wed, Mar 5, 2008 at 2:08 AM, Roger Bivand <Roger.Bivand@nhh.no> wrote:
> On Tue, 4 Mar 2008, Jose Funes wrote:
>
>  > Roger,
>  >
>  > I am running it in Windows and is 8 bytes. I tried to use the gc() for
>  > the regression kriging but it did not work, instead I did employ the
>  > gctorture() that turn "R" to run extremely slow (it is still running,
>  > almost 8 hours so far, it may finish tomorrow). For the regression
>  > kriging, I am using 163 data points:
>  >
>  > R code: Regression kriging
>  > ystrk = krige(yststep$call$formula,ystdec3,predictors,vgf1yst)
>  >
>  > where:
>  > yststep: is the regression residual
>  > ystdec3: are the data points (163 points)
>  > predictors: are the asciigrid file containing the predictor variables
>  > (9 raster layers)
>  > vgf1yst: fitted variogram
>
>  But no use of maxdist= or nmax=? I would have said:
>
>  g1 <- gstat(formula=yststep$call$formula, data=ystdec3, model=vgf1yst)
>  ystrk <- predict(g1, newdata=predictors)
>
>  anyway, which shows exactly how to resolve the problem - subset ystdec3
>  into tiles and patch them together again afterwards (patching may need
>  thinking through clearly). I assume that you have now successfully read
in
>  the predictors? Do I understand that you are calibrating your model on
163
>  observations, and predicting for roughly four orders of magnitude more
>  grid cells?
>
>  gc() can help to clean out garbage sometimes - say in a loop, gctorture()
>  really only during development, not in regular use.
>
>  Roger
>
>
>
>  >
>  > error message:
>  > In addition: There were 12 warnings (use warnings() to see them)
>  >
>  > warning messages:
>  > 1: Reached total allocation of 1535Mb: see help(memory.size)
>  > ...
>  > 3: In slot(value, what) <- slot(from, what) :
>  > ...
>  > 8: In slot(value, what) <- slot(from, what) :
>  > Reached total allocation of 1535Mb: see help(memory.size)
>  > ...
>  > 12: In `slot<-`(`*tmp*`, what, value = structure(c(-293438.89990765,
 ... :
>  > Reached total allocation of 1535Mb: see help(memory.size)
>  >
>  >
>  > Is there another way to get around garbage collection than
gctorture().?
>  >
>  > Sincerely,
>  >
>  > Jose
>  >
>  >
>  >
>  >
>  > On Tue, Mar 4, 2008 at 3:48 AM, Roger Bivand <Roger.Bivand@nhh.no>
wrote:
>  >> On Mon, 3 Mar 2008, Jose Funes wrote:
>  >>
>  >> > Dear members,
>  >> >
>  >> > I would like to share some of the problems that I have run into R
when
>  >> > importing ascii files. I described the problem below, if any have
>  >> > experienced similar issues I will appreciate you support.
>  >> >
>  >> > I am working with raster data(maps) and importing it as ascii files
to
>  >> > R. I am stacking 9 layers (10MB~each, total 90MB). However, when
>  >> > stacking the eight layer I get the following message "Error: cannot
>  >> > allocate vector of size 10.8 Mb". I did a little of bit of reading
>  >> > about this, and some suggestions are to increase the memory
>  >> > allocation. I increased it to 3GB  using the following command:
>  >> > memory.limit(size=3000) but the problem still persist.
>  >> >
>  >>
>  >>  Which platform? Windows? Its performance is systematically worse than
>  >>  other OS on the same hardware. When you say 10Mb, is this 8 bytes *
>  >>  roughly 1.2 million cells? Are you running gc() between calls to
>  >>  readAsciiGrid() to force garbage collection? While readAsciiGrid() is
>  >>  working, I think that at least three copies of the data are in
memory.
>  >>
>  >>  Have you tried using rgdal? Maybe readGDAL() keeps fewer internal
copies?
>  >>  (in readGDAL, that may be at least two copies, but I'm not sure (NAs
are
>  >>  handled by GDAL)). Because input needs to be converted from
representation
>  >>  to representation, it is not possible to avoid multiple copies.
>  >>
>  >>
>  >> > code:
>  >> > 1. predictorslide2 <- readAsciiGrid("sol_spr_lide2.asc")
>  >> > ...
>  >> > ...
>  >> > 8. predictorslide2 <-
cbind(predictorslide2,readAsciiGrid("umca_pr_lide2.asc"))
>  >> >
>  >>
>  >>  It may be possible to get round cbind() copying if that turns out to
be
>  >>  the problem, by taking the SpatialGrid from the first read, putting
it
>  >>  aside, and storing the single columns of the data slots of read data
in a
>  >>  single, pre-allocated data frame. Re-allocating to predictorslide2
>  >>  successively is not a good idea for large objects - Braun & Murdoch
in
>  >>  their nice book on R programming treat it as a worst case on speed
and
>  >>  memory usage.
>  >>
>  >>
>  >>
>  >> > Also I got a similar error when running regression kriging:
>  >> > Error: cannot allocate vector of size 6.7 Mb
>  >>
>  >>  Without the command, this isn't informative. Were you using local
areas
>  >>  (maximum distance or # data points)? How large were the data= and
newdata=
>  >>  objects?
>  >>
>  >>  Roger
>  >>
>  >>
>  >> > In addition: There were 12 warnings (use warnings() to see them)
>  >> >
>  >> > warning messages:
>  >> > 1: Reached total allocation of 1535Mb: see help(memory.size)
>  >> > ...
>  >> > 3: In slot(value, what) <- slot(from, what) :
>  >> > ...
>  >> > 8: In slot(value, what) <- slot(from, what) :
>  >> >  Reached total allocation of 1535Mb: see help(memory.size)
>  >> > ...
>  >> > 12: In `slot<-`(`*tmp*`, what, value = structure(c(-293438.89990765,
 ... :
>  >> >  Reached total allocation of 1535Mb: see help(memory.size)
>  >> >
>  >> > I will appreciate your suggestions. I have also considered splitting
>  >> > the data  but would like to explore other solutions.
>  >> >
>  >> > Regards,
>  >> >
>  >> > Jose Funes
>  >> >
>  >> > _______________________________________________
>  >> > R-sig-Geo mailing list
>  >> > R-sig-Geo@stat.math.ethz.ch
>  >> > https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>  >> >
>  >>
>  >>  --
>  >>  Roger Bivand
>  >>  Economic Geography Section, Department of Economics, Norwegian School
of
>  >>  Economics and Business Administration, Helleveien 30, N-5045 Bergen,
>  >>  Norway. voice: +47 55 95 93 55; fax +47 55 95 95 43
>  >>  e-mail: Roger.Bivand@nhh.no
>  >>
>  >>
>  >
>
>  --
>
>
> Roger Bivand
>  Economic Geography Section, Department of Economics, Norwegian School of
>  Economics and Business Administration, Helleveien 30, N-5045 Bergen,
>  Norway. voice: +47 55 95 93 55; fax +47 55 95 95 43
>  e-mail: Roger.Bivand@nhh.no
>
>

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