Thank you very much Paulo and Edzer; between your responses and a little brushing up on my geostatistics it now makes perfect sense that geoR uses a GLS approach to removing the trend in likfit or krige.conv. Otherwise, by relying on OLS, I am not accounting for the spatial correlations in my dataset (as you have suggested Paulo). As Edzer has explained, if I were to rely on OLS I am assuming there is spatial independence between my measurements; therefore, my semivariogram model would be a pure nugget model with zero range and zero partial sill. My data clearly shows this is not the case, which is why I cannot match my coefficients obtained from OLS with those obtained by GLS.
Best Regards, Joshua Palmer Meteorologist Atlanta, GA, USA -----Original Message----- From: Paulo Justiniano Ribeiro Jr [mailto:[EMAIL PROTECTED] Sent: Monday, September 03, 2007 12:45 PM To: Joshua Palmer Cc: r-sig-geo@stat.math.ethz.ch Subject: Re: [R-sig-Geo] geoR Beta Estimates Not Equal to lm Coefficients: Why? Josha If I've got oyur question correctly the diference is because geoR is estimating both, regression coeficientes and covariance parameters and therefore the estimates ar not the same as lm() which uses ordinary least squares. In other words the regression parameters on geoR are estimated considering the spatial correlation between the sample points as described by the assumed model Hope this helps P.J. Paulo Justiniano Ribeiro Jr LEG (Laboratório de Estatística e Geoinformação) Universidade Federal do Paraná 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 -----Original Message----- From: Edzer J. Pebesma [mailto:[EMAIL PROTECTED] Sent: Monday, September 03, 2007 2:47 AM To: Joshua Palmer Cc: r-sig-geo@stat.math.ethz.ch Subject: Re: [R-sig-Geo] geoR Beta Estimates Not Equal to lm Coefficients: Why? Joshua, My guess is that to estimate beta geoR uses generalized least squares and lm and SAS ordinary least squares. If you use a pure nugget model, or some other model with a range parameter sufficiently close to zero, i.e. model the observations as independent, the estimates should be the same. -- Edzer On Mon, 3 Sep 2007, Joshua Palmer wrote: > Hello everyone, > > I am using the excellent geoR package to perform Kriging with External > Drift. As to be expected, for any given set of covariates, trend > coefficients I obtain performing least-squares regression in SAS equal > those coefficients obtained by using R's lm function. However, those > trend coefficients do NOT match the estimates for the beta values when > I run krige.conv (or likfit or variofit) in geoR. The coefficients > are often similar, but they are never nearly or exactly the same. I > was under the assumption that geoR utilizes least-squares regression > (on the trend.d > matrix) a la R's lm function to derive beta estimates. This appears > to be incorrect. > > Would anyone be able to explain to me why I am noting this difference > or should I be noting a difference? I have researched R mailing lists > and geoR documentation but have been unable to find an answer. Please > let me know if additional specificity is needed. This dilemma is > universal across different sets of covariates and covariance models or parameters. > > Any assistance is greatly appreciated and I thank you for your time. > Joshua Palmer > Meteorologist > Atlanta, GA, USA > > _______________________________________________ > R-sig-Geo mailing list > R-sig-Geo@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/r-sig-geo > _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo
