[R-sig-Geo] geoR Beta Estimates Not Equal to lm Coefficients: Why?

[Paulo Justiniano Ribeiro Jr]

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

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
>
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