Dear G Allegri I'v e being running through old email here and gound this message from yours posted a while ago well, you could for instance, model the covariance matrix using a correlation function of the distances using nlme() with a spatial covariance structed or likfit() in package geoR. Both will estimate mean (regression) and covariance jointly (instead of alternating between OLS and veriogram fits) Paulo Justiniano Ribeiro Jr LEG (Laboratorio de Estatistica e Geoinformacao) Universidade Federal do Parana Caixa Postal 19.081 CEP 81.531-990 Curitiba, PR - Brasil Tel: (+55) 41 3361 3573 Fax: (+55) 41 3361 3141 e-mail: paulojus AT ufpr br http://www.leg.ufpr.br/~paulojus ------------------------------------------------------------------------- 53a Reuniao Anual da Regiao Brasileira da Soc. Internacional de Biometria 14 a 16/05/2008, UFLA, Lavras,MG http://www.rbras.org.br/rbras53 -------------------------------------------------------------------------
On Thu, 31 Jan 2008, G. Allegri wrote:
I have a question about using GLS estimation within the Regression Kriging framework. In Rossiter and Hengl texts it is stated that it makes not so much difference, in many practical situations, using OLS rather then GLS. I'd like to test it in my work. What's the more feasible way to adobt it in R? The RK method suggests to use iteratively the regression coefficient estimates using the covariance matrix derived from the residual covariance modelling. 1 - How to automate this iteration scheme? I'm not so expert in R scripting... 2 - Could gls, from the nlme package, be used? Giovanni
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