Geostatistical model with uncertainty in response variable

Hello all,

I wonder whether someone could advise on my problem:

I want to use AIC to conduct model selection for a geostatistical model in 
which the response variable is roughly normally distributed. But each 
y-value is an estimate, and so I would like to take into account variation 
in the uncertainties of the estimates. Also, I want to use the Matern 
structure to model spatial correlations.

I have found several options, each of which has one or more drawbacks:

1. Use gls() in nlme. I think this can do all I want, except there is no 
Matern corStruct class.

2. Use likfit() or krige.bayes() in geoR. But these can't take into 
account uncertainty in the response variable

3. Use another way of direct ML estimation of parameters (Hoeting et al. 
2006. Ecological Applications 16:87-98). But this can't take into account 
uncertainty of the response, and also doesn't give variance estimates of 
the regression parameters.

Does anyone have any suggestions about how I might proceed? I'd like to 
avoid having to construct a new corStruct class if possible (don't really 
have the necessary expertise).

Many thanks!
  Suhel

Suhel Quader, PhD
Department of Zoology
University of Cambridge