[R] geoR - help for bayesian modelling

[Monica Palaseanu-Lovejoy]

Hi,

I am trying to do a bayesian prediction for soil pollution data above 
a certain threshold, using geoR. 

Everything is working fine until i am doing the krig.bayes ( I am 
using as a guide the geoR tutorial from the web page 
http://www.est.ufpr.br/geoR/geoRdoc/geoRintro.html#starting). I 
tried to do the prediction on a grid 67 by 113 cells and my 
computer is freezing to death. At larger numbers of cells it tells me 
after a while that it reaches the max. memory of 511 Mb. My 
computer has only 512 Mb of RAM. What RAM capacity should i 
look for to do a 150 x 250 cell grid??? (I tried the modelling on a 1 
Gb RAM computer and it didn't work either). I am interested to do a 
modelling where my resolution is 5 m x 5 m (150 x 250 grid cell).

If i want to do the prediction on my initial data locations (well, 
actually the prediction points are shifted 1 m in X and respectively 
Y direction, so the raw data coordinates don't coincide with the 
prediction coordinates) i am getting the following error using the 
command:

zn.bayes <- krige.bayes(zn.gdata, loc = xy, model = 
model.control(cov.model = "exponential", lambda = 0), prior = 
prior.control(phi.prior ="exponential", phi = 89.1894), 
output=output.control(n.predictive=2, mean.var = TRUE, quantile = 
c(0.025,0.25, 0.5, 0.75, 0.975), threshold = c(300)))

Error in cond.sim(env.loc = base.env, env.iter = iter.env, 
loc.coincide = get("loc.coincide",  : 
        chol: matrix not pos def, diag[13]= -1.279220e-018

I will really appreciate any suggestion you may have.

Thank you so much,

Monica