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
[[alternative HTML version deleted]]
_______________________________________________
R-sig-Geo mailing list
R-sig-Geo@r-project.org
https://stat.ethz.ch/mailman/listinfo/r-sig-geo
