[R-sig-Geo] Regression kriging

[G. Allegri]

Dear Edzer,
I've "medidated" on the answer you gave to Jose. Two considerations have raise:

 1 - when you say that the approach of GLM is a way to consider
spatial dependence. I'm not sure about this. GLM are a way to account
for link functions between the dependent variables and covariates (ex.
Poisson family for count datas), but they don't take account,
implicitly, of sptial correlation. Am I wrong?
Rather (generalized) mixed models are a counterpart to geostatical methods are.

2 - A task of my research is to find the "best" relations between a
set of covariates, to make a simple multicriteria analysis,
overlapping different map layers thorugh map algebra. In this case,
the common geostatistical methods don't help me much. I'm considering
to use multivariate regression, but keeping in count of spatial
correlation. What's the best approach? I've thought to Mixed Models,
but another way could be using GLS estimation, based on the residauls
covariance. What's your suggestion?

Giovanni

PS I think it could be an answer to Jose too...




2008/1/27, Edzer Pebesma <edzer.pebesma at uni-muenster.de>:
> Jose, is your model linear, or are you using a generalized linear model?
>
> The questions is not so much: model parameters before or after kriging
> residuals, but rather: model parameters under the assumption of
> independent observations (the usual regression approach), or model
> parameters under the assumption of spatially dependent observations (the
> geostatistical, or generalized linear regression approach). In the case
> you are using a GLM, the report mentioned earlier on regression kriging
> (another name for universal kriging) may not be very helpful, as it
> deals with linear models.
> --
> Edzer
>
> Jose Funes wrote:
> > Hi,
> >
> > I have used regression kriging to model abundance of an invasive
> > species. After performing a stepwise regression of the model, I fitted
> > a theoretical variogram to a empirical variogram of the residuals. My
> > question is how to obtain the parameter estimate of the model after
> > kriging the residuals. Do the parameters of the regression model
> > differ after kriging the residuals? if so, how can I get them from the
> > R output?
> >
> > Looking forward to hearing from you, thanks
> >
> > Jose Funes
> >
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