Hi, Gregoire,
what is the error of your measurements? Perhaps this is masking your data?
sometimes they can be very big!
Are you able to know it?
Hope this helps,
Carme
En/na Gregoire Dubois ha escrit:
Hi Edzer,
I thought about that (the nugget effect) but my variogram (and robust
variograms) did show a short scale structure (with a high nugget
effect).
Considering the possibility you mention, that I actually have a
phenomenon with a pure nugget effect although I see a structure, what do
I have to trust? My experimental variogram showing a structure or the
strong slope of my residuals (either + or -1 depending on the way you
define the residuals) plotted against the observed data telling me that
I actually may not have a structure? The correlation between my observed
and estimate values is poor indeed (0.40) but still..
For what concerns the definition of the residuals, Surfer and Isatis
used here defined the residuals = estimate - observed. I haven't
checked the other software.
I had a quick look in Isaaks & Srivastava, Deutsch & Journel, Chiles and
Delfiner... all use the same definition as above (i.e. residuals =
estimate - observed).
Many thanks again for your kind feedback!
Gregoire
__________________________________________
Gregoire Dubois (Ph.D.)
European Commission (EC)
Joint Research Centre (DG JRC)
WWW: http://rem.jrc.cec.eu.int/
"The views expressed are purely those of the writer and may not in any
circumstances be regarded as stating an official position of the
European Commission."
-----Original Message-----
From: Edzer Pebesma [mailto:[EMAIL PROTECTED]
Sent: 30 January 2008 14:13
To: Gregoire Dubois
Cc: [email protected]
Subject: Re: AI-GEOSTATS: Correlation between kriging residuals and
input data
Gregoire,
If you interpolate with a pure nugget effect, this is what you would
expect for cross validation residuals because the predictions are
constant, except that usually the residuals are defined as (observed -
predicted) which would give the perfect correlation positive (1).
Which software gave you the residuals computed the other way around, or
did you compute them yourself?
--
Edzer
Gregoire Dubois wrote:
Dear list,
Having fit a variogram to a dataset (indoor radon measurements) and
applied cross-validations, I noticed the perfect negative correlation
(-0.95) between my kriging residuals and my input data.
This means that I am overestimating as much the low values as I am
underestimating the high values, something I am expecting since the
mean of the residuals -> 0, a property of kriging. Fine so far.
What I am puzzled about is of the possible reasons of getting such a
strong slope (close to -1) of the plot of my residuals against my
input data?
This, I understand, highlights that I am doing a systematic error
somewhere which I want to avoid obviously. I thought I extracted
properly the spatially correlated component of my dataset (the
variogram of my residuals seems to show a pure nugget effect) but I
still can't find any reasonable explanation for the systematic errors.
Any hints? I must have missed something obvious here.
Many thanks for any feedback.
Best regards,
Gregoire
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--
****************************************************************
Carme Hervada i Sala
Dept. Física i Enginyeria Nuclear
L'Escola Industrial -EUETIT
Universitat Politècnica de Catalunya
Grup de recerca de Geoestadística i Riscos Naturals
tel. 34 93 739 82 68
fax. 34 93 739 82 25
[EMAIL PROTECTED]
Colom 1
08222 Terrassa (Spain)
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