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
Dear Monica
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
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