On Wed, 6 Apr 2011, Robert Pazur wrote:
Dear all,
with usage of spdep package I tried to explore the impact of changing
location of occurrence of 1?s in binary fashion on Moran index by shifting
an artificial 9pixel- square (small square left below in the picture) in
all direction (based on
Barry Rowlingson wrote:
topocolours (I wrote that!) uses the same greedy algorithm once its
worked out the connectivity. I did make topocolours have the
flexibility to choose other algorithms but only implemented the greedy
one. The problem with real world maps is that some features have more
On Wed, 6 Apr 2011, Karl Ove Hufthammer wrote:
Barry Rowlingson wrote:
topocolours (I wrote that!) uses the same greedy algorithm once its
worked out the connectivity. I did make topocolours have the
flexibility to choose other algorithms but only implemented the greedy
one. The problem with
Hi,
You could try to (ab)use the standard lm function. First calculate the
regression using lm, than replace the fitted coefficients by the values
you want them to have and than use predict to estimate the values at
your measurement locations. Subtracting that value from the measurements
gives
On Wed, Apr 6, 2011 at 8:38 AM, Roger Bivand roger.biv...@nhh.no wrote:
I've asked Barry off-list about this, there was a case a little while ago
with unexpected results because of inserted sliver polygons causing
apparently separate observations to be neighbours (the slivers were only
Barry Rowlingson wrote:
I've asked Barry off-list about this, there was a case a little while ago
with unexpected results because of inserted sliver polygons causing
apparently separate observations to be neighbours (the slivers were only
visible when zooming right in). There are also
Thank you.
So that means that e.g. with ordinary cokriging, the one condition sum of
all coefficients equal 1 is used, and not e.g. the (n+1) nonbias conditions
by which the coefficients of the target variable sum to 1, whereas the
coefficients of the n secondary variables sum to 0 ?
I'm sorry,
Hi to everybody,
I'm new to this list.
I load a raster map in R thanks to spgrass6 package: no problem.
I would like to plot it: with image command no problem.
The problem is that I would like to mantain original map colours.
My map is catgorized and in particular:
category red green blue
Well, I am asking this because in An Introduction to Applied
Geostatistics, §17, they point out how these that are meant as usual
nonbias conditions are not believed to be the best ones, though they are
the most commonly used..
Using just one nonbias condition involving all the cokriging
Dear R-sig-geo followers:
I wonder if anyone can help me with the following problem. I'd like to
plot a series of rasters overlaid with polygons (or points), where the
polygon layer is specific to each panel. here's an example of my
nearest attempt using spplot on a Meuse-based dataset
Folks:
Does anyone know of a package (or a suggestion on how to implement) to
calculate, for two classified raster images of the same location but
different times, the relative probability of transitioning from one class to
the other? Additionally, once this is figured out, how to apply this
At least for the first part, if you already have a model (and a function),
you could calculate the probability of transition between classes using
raster::calc function.
s - stack(r, r*2, sqrt(r))
# return a RasterLayer
rs1 - calc(s, sum)
Cheers,
Roman
On Thu, Apr 7, 2011 at 12:07 AM,
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