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,
0.1),debug.leve
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
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 p
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 1 I don't get singularity. Using Lat and Long works fine. C
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
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
case
