Hello Yetta, hello all Yetta! i don't know whether you remeber; you sent me your tm-report in march and i wish to thank you very much! I tried to answer you but may be your e-mail does not work?! As i am not an expert in geostatistics i would like to ask some questions and would be glad if someone could bring me on the right way! What i got and want to do: I have data from different campaigns between 1980 and 2000 in a morphological heterogenous (elevation) region. I want to assess large scale (1:500.000) spatial variability of heavy metals in upper soil layers due to atmogenous depostion. Before making spatial estimations i have to homogenize the data (=> pattern free data!). i don't want to assess a spatial (coordinate dependend) drift! What i did up to now: i built up one deterministic (pattern-)model for each element via multiple linear regression analysis (SPSS). The pattern-models should explain variations in element-concentrations due to different binding/retention capacities of different soil types (c-content, pH), morphology (elevation at sample site) and (linear?) decreasing deposition rates since 1980 (year of taking the sample). As the data are "lognormal" i used a ln-transformation. Questions: 1. When calculating multiple linear regression (pattern-)model i did not include xy-coordinates of the sample sites as aditional predictor variables allthough there is considerable correlation between coordinates and heavy metal contents (but there is no trend in variograms!). is that ok? 2. (backtransformed) semivariograms of the pattern-models still have a "good shape" suggesting a spatial/random variability. shouldn't they behave like "white noise"? 3. how do i get backtransformed residuals from ln-transfortmed data ? when i backtransform ln-residuals of the regression/pattern-models negative ln-values are just interpreted as positive values below 1,0. is it possible to get residuals by substracting (backtransformed) predicted values (=exp(regression model)) from the original (= untransformed) values? 4. simultanous estimation of drift and semivariogram with a single realization is rigorously not possible. Are there some (simple) aproximations for the semivariogram? What about the iterative solution for the simultaneaous inference of drift and semivariogram, how does it work in detail? I know there are a lot of questions but i hope you can give me some answers regards Lenz Lorenz Dobler Bayerisches Geologisches Landesamt He�str. 128, D-80797 M�nchen Tel.: ++49-89-9214-2766 e-mail: [EMAIL PROTECTED] -- * To post a message to the list, send it to [EMAIL PROTECTED] * As a general service to the users, please remember to post a summary of any useful responses to your questions. * To unsubscribe, send an email to [EMAIL PROTECTED] with no subject and "unsubscribe ai-geostats" followed by "end" on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list * Support to the list is provided at http://www.ai-geostats.org
