Guess the reason for the problem you are seeing is that you are requiring simulations from the predictive distribution. geoR is doing this simulation in a joint step, simulating from the joint predictive distriubtion [as far as I know some other geostatistical software packages are doing such simulation in a sequential way, where a point the grid is added at a time]. For a relatively large grid the covariance matrix needed for this joint simulation is large [in your example a matrix of size 7500 by 7500].
Possible solutions :
* Do you really want simulations from the multivariate predictive distribution ? What do you want to do with them ?
Most summaries you would want of the predictive distribution are summaries of the univariate distributions at individual locations.
* Do you really need to predict in a 150 by 250 grid ? If possible, then reduce the size of your grid.
The error you are seeing is related to the cholesky factorisation of the covariance matrix, which is needed to do the joint simulations. If you do not require these simulations, the error will disappear.
As you write, the error is probably due to some locations being very close to data locations. As I remember there is an internal handling in the package of prediction locations close to data locations, but your locations are not sufficiently near to the data locations to be handled by this. Maybe you should change the prediction coordinates in question such that they actually do coincide with the data locations.
Hope this is helpful
Cheers
Ole
***
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 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???
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
-- Ole F. Christensen Center for Bioinformatik Aarhus Universitet Ny Munkegade, Bygning 540 8000 Aarhus C
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