[R-sig-Geo] spatial structure of residuals in nlme mixed model

[Javier Moreira]

hi,
im trying to asses about the diference in puting the spatial estructure in
a mixed linear model.
What i need to do is to compare the actual distribution of the residuals of
two models, one that consider the correlation and one that not.

model with and without:

*without*

modelo3_MM<-lme(REND_SE~1+TRATAMIENTO*AMBIENTE,

 random=list(BLOQUE=pdIdent(~1),AMBIENTE=pdIdent(~1),TRATAMIENTO=pdIdent(~1)),
                 data=data1,
                 control=lmeControl(niterEM=150,msMaxIter=200))
modelo33_MM<-update(modelo3_MM,
                    weights=varComb(varIdent(form=~1|TRATAMIENTO)))

*with*
modelo34_MM<-update(modelo33_MM,
                    correlation=corExp(form=~1|BLOQUE/AMBIENTE/TRATAMIENTO))

until now, what i could do is to cheq the existance of that structure by
computing the semivariogram to the residuals.

plot(Variogram(modelo33_MM))

plot((modelo34_MM,resType="n",robust=T))

the question is how can i do the next step and krige this residuals in
order to get a map of predicted values and variance values?
im used to do it wity gstat package but i cant figured out with an object
of class "lme"

thanks

-- 
Javier Moreira de Souza
Ingeniero Agrónomo
099 406 006

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