R help - Jun 2011 - GLS models and variance explained

Dear list,

Inspecting residuals of my linear models, I detected spatial autocorrelation.
In order to take this into account, I decided to use the GLS method
with the correlation = corGaus ( ~ X + Y).

Then, I can sort my GLS models based on their AIC.
But ... how to know the proportion of the variance explained by the
best one (it can be best of the worst models) ?
R-squared value has not the same meaning for OLS and GLS ...


- Could the R2 value calculated with the OLS model (using lm)
constitute a potential proxy of the variance explained by the GLS
model ? (the answer is probably no)

- Is a R-squared based on sqrt(cor(obs, predicted)) a better approach ?

- What about pseudo R-squared like Nagelkerke's ?

Suggestions for any better approach are welcome !


Thanks in advance,

Arnaud