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", 40000,
0.00001),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
makes the problem ill-conditioned. If you add argument
fit.ranges=FALSE it will not be singular anymore.
In the end, this problem is similar to e.g. regression of polynomials
on coordinates without standardizing the coordinates.
I'll add a warning message to fit.variogram to suggest the two
solutions, for this case.
--
Edzer
Zev Ross wrote:
Edzer (and all),
I don't think that it's related to an unrealistic range. I've tried a
lot of different realistic and non-realistic values and get singular
results each time. If I divide the X and Y coordinates by 10, 100,
1000 or 10000 I don't get singularity. Using Lat and Long works fine.
Code is below and I included a link to a workspace with the "pol"
data set at the bottom.
Zev
polA<-pol
coordinates(polA)<-~x+y
v<-variogram(pollutant~1, data=polA)
v.fit<-fit.variogram(v, vgm(0.0005, "Sph", 40000, 0.00001))
attributes(v.fit)$singular # TRUE
polB<-pol
polB$x<-polB$x/1000
polB$y<-polB$y/1000
coordinates(polB)<-~x+y
v<-variogram(pollutant~1, data=polB)
v.fit<-fit.variogram(v, vgm(0.0005, "Sph", 40, 0.00001))
attributes(v.fit)$singular #FALSE
polC<-pol
coordinates(polC)<-~longitude+latitude
v<-variogram(pollutant~1, data=polC)
v.fit<-fit.variogram(v, vgm(0.0005, "Sph", .4, 0.00001))
attributes(v.fit)$singular # FALSE
http://www.zevross.com/temp2/singular_or_not.RData
Edzer Pebesma wrote:
Hi Zev, it is hard to see what happens without seeing your data or R
commands.
Is it possible that you passed an unrealistic value for the range
parameter, as starting value for the variogram model argument of
fit.variogram?
--
Edzer
Zev Ross wrote:
Hi All,
I'm fitting variograms in GSTAT with fit.variogram and I was
surprised to find that all my fits were singular. I experimented
with converting the data to unprojected data (decimal degrees) and
with dividing my X and Y coordinates, which are in meters, by 1000
(to get KM). In both cases the fitting procedure worked with no
singularity. Based on the numbers of pairs the bins appeared to be
about the same so it appears to be a matter of the coordinates
themselves.
I'd prefer not to have to convert the coordinates back and forth
between meters and KM, any suggestions?
Zev
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