Dear Edzer,
Am 16.03.2012 15:12, schrieb Edzer Pebesma:
Tom, right, it is. If you specify nmax=9 for the second variable, you
see the dist values for the second as well. They are indeed not computed
in your case, as the selection can be copied. Apparently these things
mattered when I wrote the code 20 years ago!
the following modified example gives "dist: 0" for "data id: 0", which
is non-colocated with the second covariate. As far as I understand the
selection cannot be copied here:
library(gstat)
data(meuse)
coordinates(meuse) <- ~x+y
data(meuse.grid)
coordinates(meuse.grid) <- ~x+y
i <- sample(1:nrow(meuse), 1)
g <- gstat(NULL, "z", zinc~1, meuse[-i,], model=varmod)
g <- gstat(g, "dist", I(dist*1000)~1, meuse[-i,])
variog.g <- variogram(g)
g <- fit.lmc(variog.g, g, vgm(100000, "Exp", 500, 1000))
varmod.dist <- g$model$dist
g <- gstat(g, "dist", I(dist*1000)~1, data=meuse.grid,
model=varmod.dist, maxdist=100)
meuse.point <- meuse[i,]
predict(g, newdata=meuse.point, debug.level=16)
When you say, "Is there a way to get the distances for the second
covariate?", do you mean, as R object?
Well, that would be luxurious. Currently I copy-paste those data into
Calc for further analysis. Of course I can compute the distances somehow
outside from gstat, but is there a way to get gstat doing this?
Thanks!
Tom
On 03/16/2012 02:53 PM, Tom Gottfried wrote:
Dear list(en)ers,
I wonder what the "dist" is, which is in the output of predict.gstat()
with debug.level=16. I expect it to be the distance between the
coordinate pair given in the same line and the prediction location. I
verified this for the coordinates and distances given for "data id: 0"
in the below example (and its true), but for "data id: 1" it's always
"dist: 0". Is there a way to get the distances for the second covariate?
I see it's simple in the example, because data are colocated, but the
problem arose in a case with non-colocated covariates.
Here's the example:
library(gstat)
data(meuse)
coordinates(meuse)<- ~x+y
variog<- variogram(zinc~1, meuse)
varmod<- fit.variogram(variog, vgm(150000, "Exp", 1000))
data(meuse.grid)
coordinates(meuse.grid)<- ~x+y
g<- gstat(NULL, "z", zinc~1, meuse, model=varmod, nmax=10)
g<- gstat(g, "dist", I(dist*1000)~1, meuse, nmax=10)
variog.g<- variogram(g)
g<- fit.lmc(variog.g, g, vgm(100000, "Exp", 500, 1000))
meuse.point<- meuse.grid[sample(1:nrow(meuse.grid), 1),]
predict(g, newdata=meuse.point, debug.level=16)
Thanks!
Tom
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