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
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