On 07/17/2010 06:19 PM, Samuel Turgeon wrote:
Hi,
I'm a new user of gstat, so my question is maybe very simple.
I'm trying to fit a variogram with the function fit.variogram and I get this
error message:
Warning: singular model in variogram fit
[1] "a possible solution MIGHT be to scale semivariances and/or distances"
My data have values between 2.54e-10 et 2.56e-06. If I multiply my data by
a constant in the semivariogram:
vario=variogram(NASC*10000000~1,data,cutoff=1000)
When I do this multiplication I'm able to fit a variogram.
So my question is, Is it possible to this? Can I back transform (by a
division) my predited values when the ordinary kriging will be done?
Hi Sam,
It is possible, probably the covariance matrix has a lot of zeros
because of rounding of your small values. Could this be linked to using
single precision floats in gstat (edzer?)? I think there is no problem
in 'backtransforming' your values, i.e. you only change the unit of the
variable you are using (e.g. from milligram to microgram). Take care
though that when predicting whilst using the fitted variogram model,
your observations also need to be multiplied by this constant. My
suggestion would be to multiply the data at the very beginning of the
script, do all you analysis and at the very end do the division. You
could also skip the division and present your results in the new unit.
regards,
Paul
Thanks!
Sam
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