[R] problem with gls : combining weights and correlation structure

[ybas@ens-lyon.fr]

Dear R-users,

I hope you will have time to read me and I will try to be brief. I am also 
sorry for my poor english.

I used gls function from the package nlme to correct two types of bias in my 
database. At first, because my replicates are spatially aggregated, I would 
like to fit a corStruct function like corLin, corSpher, corRatio, corExp or 
corGaus in my gls model, and simultaneously, because my response variable is 
an estimate, I would like to use weights to take into account the accuracy of 
the estimation. I used a varFixed object corresponding to squared standard 
error.
Variograms all shows a weak but real spatial autocorrelation (nugget ~ 0.9 but 
they always increase with distance). 
My first problem was the estimation of the parameters of the corStruc function 
which were very far from their order of magnitude (range > 10E15, though the

maximum distance between observations is no more than 10E6).
I thought I had convergence problem that I could solve :
- with at first fitting corStruct functions to variograms with the solver of 
Excel
- and secondly binding corStruct parameters to the obtained value with the 
argument "fixed=TRUE"
But I obtained very unrealistic values for the parameters of the model even 
when the spatial autocorrelation was weak, so I am sure that the model fitting 
didn't work properly.
I had absolutely no problems in using the "corr" or the
"weight" arguments 
separately.

I thank you very much to read me and if you have a solution to my problem or 
if you know where I did a mistake, you would be very nice to answer me.

Sincerely yours,

Yves Bas