On Wed, 22 Jun 2011, Matthias Hinz wrote:
Hi,
I can give you an example for merging two of those data frames. It might not
be the best solution, but it should work for all your data. In general, i
separated Polygons - Objects and data, assigned new, unique ids to the
polygons and merged them to
> have a large number (several thousand) of asci files which were
> delivered to me without a NODATA_value -999.0 line in the header
If all your files are for the same area you could do something like
r <- raster(ncol=601, nrow=351, xmn=10, xmx=701000, ymax=25,
ymn=-101000)
filename
Apart from the multicore that Matteo mentioned, there are other things that
might speed things up.
first: this is a bad idea.
setOptions(chunksize = 1e+04, maxmemory = 1e+06)
By doing this you are giving the function very little memory to work this. I
would only do this if the function fails b
Hi,
I can give you an example for merging two of those data frames. It might not
be the best solution, but it should work for all your data. In general, i
separated Polygons - Objects and data, assigned new, unique ids to the
polygons and merged them together. Then i merged the data and changed th
Dear list,
Inspecting residuals of my linear models, I detected spatial autocorrelation.
In order to take this into account, I decided to use the GLS method
with the correlation = corGaus ( ~ X + Y).
Then, I can sort my GLS models based on their AIC.
But ... how to know the proportion of the vari
