A quick way to rescale a set of maps is to use the grid tools of SAGA GIS (see
rsaga.get.usage(grid_tools, 0)). First, download all ASCII file maps to a
working directory, then
obtain the list of maps in the folder, and then automate:
ascmaps - list.files(getwd(), pattern=\\.asc$, full=F)
Dear R gurus,
I have some climatic data for a region of the world. They are monthly averages
1950 -2000 of precipitation (12 months), minimum temperature (12 months),
maximum temperature (12 months). I have scaled them to 2 km x 2km cells, and
I have around 75,000 cells.
I need to feed them
Thanks for the reproducalbe example, Zev;
the whole thing looks very strange to me; it seems to be the combination
of very large distance values and very small semivariance values that
triggers this -- when I multiply v$gamma with 1000, many different
initial variogram values are fit without
Hi Corrado,
I run the PCA using prcomp, quite successfully. Now I need to use a
criteria to select the right number of PC. (that is: is it 1,2,3,4?)
What criteria would you suggest?
that's an interesting and probably controversy-generating question. It's
probably not an R-sig-geo question,
Zev, if you do a
v.fit-fit.variogram(v, vgm(0.0005, Sph, 4, 0.1),debug.level=32)
you'll see that the X matrix of the Gauss-Newton iteration with the
derivatives of the parameters to the error sum of squares is nearly
singular. The condition number of this matrix is so large that it
Hi all,
I agree with Ashton. The issue is very complex and far from resolved.
But sometimes we have to go down the PCA path. Among the many possible
solutions is the broken stick approach, for which you find an R solution
(bstick()) in the package vegan. Technically the broken stick randomly
Principle components don't search for directions that best explain your
dependent variable, but rather try to capture variability and/or
correlation in the predictors. Methods that look for subspaces that best
predict the dependent are for instance are partial least squares and
ridge
Hi all,
I used a rule of thumb as reported by the book quoted, but I am not completely
happy with it, because it is not really a statistical justification.
I will try the broken stick approach, thanks!
Concerning the interpretation, luckily enough PC1 has a clear interpretation.
PC2 a bit
Hi Corrado,
A useful reference is: Diniz-Filho J.A.F. Bini L.M. (2005). Modelling
geographical patterns in species richness using eigenvector-based
spatial filters. Global Ecology and Biogeography, 14, 177-185.
So what they do basically is using multidimensional scaling to derive
from the
Edzer,
Glad to hear that I wasn't crazy -- thanks so much for looking into this
(and so quickly). For now I'll divide by 1000 and use KM which is an
easy and reasonable solution. Zev
Edzer Pebesma wrote:
Zev, if you do a
v.fit-fit.variogram(v, vgm(0.0005, Sph, 4,
For SpatialGridDataFrames, row and col selection can be done by
object[firstrow:lastrow, firstcol:lastcol,]
after the last , you can optionally select attributes.
--
Edzer
Rob Robinson wrote:
Help - please! :-)
I have what I thought was a really simple problem. I have a raster image of
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