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 > + + To post a message to the list, send it to [email protected] + To unsubscribe, send email to majordomo@ jrc.it with no subject and "unsubscribe ai-geostats" in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/
