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I found I was able to solve the problem using the nncross() function in the package spatstat. I don't know if this was the simplest solution, since it required first converting my SpatialPoints to class ppp. In any case here it is: library('raster') library('spatstat') # Make the grid. Use raster() and then convert. # Not the most direct but it works. ...


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Could you write a script that takes each epicentre and creates a buffer. This buffer selects lightening strike points within that radius, then select a subset of this that is within an appropriate time envelope. You could then run this with different parameters ( increasing buffer size around the epicenter) and also time envelope definition (I.e. before the ...


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You could treat this as a (degenerate) interpolation problem, using a neighbourhood of one, try something along the lines of library(gstat) i = idw(var~1, old, ggg, nmax = 1, maxdist = 500) library(sp) spplot(i[1]) where var refers to the name of the variable you sampled.


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Great question you've written here. There are two problems with your code. The first and most crucial is shown below: states.spdf$frac.pop = states.spdf$total.pop / (states.spdf$ALAND10+states.spdf$AWATER10)*raster.res You are assuming that population is equally distributed in space, however you treat density wrong. Instead of using raster.res to ...


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This is caused by the matrix library used by gstat. Historically (gstat was released as open source code in 1997) it used the LDLfactor routine in the meschach library. Around 6 months ago I factored this out this code and replaced it with the BLAS/LAPACK which are native in R. LAPACK uses Choleski decomposition. LDLfactor allows for some non-positive ...


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Yes, this is correct. When you print the model by typing model.vari you'll see sill values, split up in a nugget component (the offset) and the exponential component. The sum of these two is usually indicated by "the sill value" (i.e., around 25).



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