I´m doing my diploma thesis on the spatial
distribution of weeds and I´m an absolute beginner with geostatistics. Please
take that into account when reading my question.
My data are weed counts with excess zeros and fit a
negative binomial distribution. But as far as I know
semivariagram
Yes. Generate a Gaussian random field, add a deterministic trend
surface, and take the exponent or a power transform of the sum.
--
Edzer
William Thayer wrote:
I am interested in comparing different estimators of spatial means. Any
suggestions or approaches on how to generate a 2-D,
Dear Sibylle,
I suspect your residuals will never become normal, because your data
are counts. Luckily, normality is not a requirement for variogram
calculation nor for kriging interpolation.
However, before calculating variograms it may be a good idea to
correct for non-stationarity in the
Dear all,
We
are trying to apply Universal Kriging to High
Plains Aquifer in Kansas (OLEA, 1999) for land surface elevation (LSE),
using its 317 data points. The purpose of this application is just for didactic
ends.
Our
first step was to filter a prominent 1st degree drift. The way we
Rubens
Your approach has been long used in hydrology and
similar fields with much success.
The problem with the standard deviation is that it
does not include the the 'error' on the estimation of
the true drift. To get a composite error you would
either have to
(a) add your kriging variance
