Hello Jon, thank you so much, it suppressed the verbose output now, and as
you suspected it's not the reason for the slowdown.

Do you know of anything I can do to speed up the execution of the code? I'm
already using the compiler package for the JIT compiling. My data is
lat/long, maybe the great circle distances can take longer than euclidean?
I have small grids (24x22 cells) but I have to interpolate 15 years of
daily data over them.

Also, do you know if the nmin, nmax and maxdist parameters are working
through the params list as well?

Best,
Pablo


2013/11/25 Jon Olav Skoien <jon.skoien@jrc.ec.europa.eu>

>  Hi Pablo,
>
> I guess you refer to the lines which are printed to show the progress of
> the cross-validation method used for finding the optimal idw-parameter?
> I dont think they are slowing down anything, but they are for most cases
> not adding much information either.
> There is now a new version on CRAN where debug.level = 0 (which is anyway
> the default) suppresses this output. For debug.level >=1, the lines will
> still be printed.
>
> Best wishes,
> Jon
>
>
> On 22-Nov-13 21:09, Pablo Alfaro wrote:
>
> Hi, how are you?
>
> I'm currently using createIntamapObject to create an interpolation object
> of class 'idw'. The method is working fine but I'm getting a lot of text
> output in the console which I suspect is slowing things down.
> I tried to remove it by setting the debug.level parameter in the params
> list to 0 like this:
>
>
> interpolacion <- *createIntamapObject(*observations=observaciones,
> formulaString=tendenciaEspacial,
> predictionLocations=coordsAInterpolar[sel,],
> intCRS=params$proj4StringAInterpolar,
> targetCRS=params$proj4StringAInterpolar, class='idw',
> *params=list(nclus=nCoresAUsar, nmin=params$nmin, nmax=params$nmax,
> maxdist=params$maxdist, debug.level = 0)*,
> outputWhat=outputWhat*)*
>
> Is this the right way to do it? I can try to make a reproducible example if
> it's needed.
> Thanks in advance!
> Pablo
>
> Ing. Pablo Alfaro Piñeyro
> MotionSoft Consulting S.R.L.
> Tel: 095 938 487
>
>
>
> 2013/11/19 Mark Payne <markpayneatwork@gmail.com> <markpayneatwork@gmail.com>
>
>  Hi,
>
> Consider a situation where I have a spatialpixelsdataframe, m.subset,
> that is a subset of a larger dataframe, m. Due to the large size of my
> dataset, its more efficient for me to work on just this subset, than
> it is to work with the entire data set. Now, the question is, once
> I've finished processing the subset, I want to add it back into the
> original object. But the question is... how?
>
> In terms of a code example, it might look like:
>
> library(sp)
> data(meuse.grid)
> coordinates(meuse.grid) <- ~ x + y
> m <- SpatialPixelsDataFrame(meuse.grid,meuse.grid@data)
> m <- m[,"dist"]
>
> #Split into a subset
> m.subset <- subset(m,m$y > 333000)
>
> #Do som fancy processing on m.subset e.g.
> m.subset$dist <- m.subset$dist * 10
>
> Now I want to combine the modified pixels in m.subset back into the
> main object, m. Does anyone have any ideas how this might be done?
>
> Best wishes,
>
> Mark
>
> _______________________________________________
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>
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>
>
>
>
> _______________________________________________
> R-sig-Geo mailing listR-sig-Geo@r-project.orghttps://stat.ethz.ch/mailman/listinfo/r-sig-geo
>
>
>
> --
> Jon Olav Skøien
> Joint Research Centre - European Commission
> Institute for Environment and Sustainability (IES)
> Land Resource Management Unit
>
> Via Fermi 2749, TP 440,  I-21027 Ispra (VA), ITALY
> jon.skoien@jrc.ec.europa.eu
> Tel:  +39 0332 789205
>
> Disclaimer: Views expressed in this email are those of the individual and do not necessarily represent official views of the European Commission.
>
>


-- 
Ing. Pablo Alfaro Piñeyro
MotionSoft Consulting S.R.L.
Tel: 095 938 487

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