AI-GEOSTATS: Re: Unconditional simulation
Nick The simplest way would be to do a aussian simulation and then do a rank transfrom on the results, I think. Isobel http://www.kriging.com --- On Tue, 17/11/09, Nick Hamm n...@hamm.org wrote: From: Nick Hamm n...@hamm.org Subject: AI-GEOSTATS: Unconditional simulation To: r-sig-...@stat.math.ethz.ch, ai-geostats@jrc.it Date: Tuesday, 17 November, 2009, 9:06 Dear all I want to simulate a spatially-correlated random field which follows a uniform rather than than Gaussian distribution. Does anybody know a straight-forward way to do this? Nick + + To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu + To unsubscribe, send email to majordomo@ jrc.ec.europa.eu with no subject and unsubscribe ai-geostats in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/ + + To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu + To unsubscribe, send email to majordomo@ jrc.ec.europa.eu with no subject and unsubscribe ai-geostats in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/
AI-GEOSTATS: Random number seed and unconditional simulation
Dear all
I have a question about the random number seeding in R.
I want to simulate several random fields. Each RF should have zero
nugget, the same sill but a different range (e.g., 100x100, range: 1
- 30). Let's stick with the Gaussian case for now. I use the
following code
require(geoR)
sigma2 = 10 # Set the sill
s2 = data.frame(phi=1, s2=1, s=1) # phi is the range
for(phi in 1:30)
{
set.seed(234)
sim1 = grf(100*100, grid=reg, nx=100, ny=100, xlims=c(1,100),
ylims=c(1,100), cov.model=exponential, cov.pars=c(sigma2, phi),
messages=FALSE)
image(sim1)
s2[phi,] = c(phi, var(sim1$data), sd(sim1$data))
}
# Plot the range against the a priori variance.
plot(s2$phi, s2$s2, ylim=c(0,15))
abline(v=23)
Note that, for each simulation, I use the same random number seed.
This results in a series of images that look like they have the same
starting point (sorry, this is not very technical), but with
progressively more spatial structure. Note that ther is a sudden
change at phi=23. The logic for using the same random number seed is
that we want to simulate a series of RFs where the differentiating
factor is the range (phi) and not something else. Hence the observed
similar patterns, but with increasing spatial structure, is a useful
feature.
I have two questions
1) Is this last point true? What is the effect of fixing every
argument to the function (including the random number seed) and just
varying one (in this case the range (phi))? Note that Diggle and
Ribeiro do something similar in their examples at the end of the help
for GRF (see below).
2) Why do I get the sudden change at phi=23? This also occurs for
other random number seeds (e.g, 230, 231, 234, 456, 5683432). The
change occurs at the same point (phi=23). Note that there is no
sudden change if I choose the spherical model rather than the
exponential.
If anybody has any thoughts, I would be interested.
best wishes
Nick
Here is the example given in the grf(geoR) help.
## 1-D simulations using the same seed and different noise/signal ratios
##
set.seed(234)
sim11 - grf(100, ny=1, cov.pars=c(1, 0.25), nug=0)
set.seed(234)
sim12 - grf(100, ny=1, cov.pars=c(0.75, 0.25), nug=0.25)
set.seed(234)
sim13 - grf(100, ny=1, cov.pars=c(0.5, 0.25), nug=0.5)
##
par.ori - par(no.readonly = TRUE)
par(mfrow=c(3,1), mar=c(3,3,.5,.5))
yl - range(c(sim11$data, sim12$data, sim13$data))
image(sim11, type=l, ylim=yl)
image(sim12, type=l, ylim=yl)
image(sim13, type=l, ylim=yl)
par(par.ori)
+
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Re: AI-GEOSTATS: Unconditional simulation
Hi Nick One way is to use simulated annealing (see gslib) putting as objective function your desired variogram and histogram. (but I guess that by means of some data transformation you can do that with a simple sequential gaussian simulation approach) Bye Sebas At 10.06 17/11/2009, Nick Hamm wrote: Dear all I want to simulate a spatially-correlated random field which follows a uniform rather than than Gaussian distribution. Does anybody know a straight-forward way to do this? Nick + + To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu + To unsubscribe, send email to majordomo@ jrc.ec.europa.eu with no subject and unsubscribe ai-geostats in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/ + + To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu + To unsubscribe, send email to majordomo@ jrc.ec.europa.eu with no subject and unsubscribe ai-geostats in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/
Re: AI-GEOSTATS: Random number seed and unconditional simulation
Nick Hamm : Dear all I have a question about the random number seeding in R. Note that, for each simulation, I use the same random number seed. This results in a series of images that look like they have the same starting point (sorry, this is not very technical), but with progressively more spatial structure. Note that ther is a sudden change at phi=23. The logic for using the same random number seed is that we want to simulate a series of RFs where the differentiating factor is the range (phi) and not something else. Hence the observed similar patterns, but with increasing spatial structure, is a useful feature. I have two questions 1) Is this last point true? What is the effect of fixing every argument to the function (including the random number seed) and just varying one (in this case the range (phi))? Note that Diggle and Ribeiro do something similar in their examples at the end of the help for GRF (see below). no - for some unknown reason simulation depends on the sigma and switch to another spatial pattern at some points, I tryed variaos sigma and got 3 patterns 2) Why do I get the sudden change at phi=23? This also occurs for other random number seeds (e.g, 230, 231, 234, 456, 5683432). The change occurs at the same point (phi=23). Note that there is no sudden change if I choose the spherical model rather than the exponential. it depends on the sigma and you'll get the same change at some other sigmas. If you want to know the truth you should read the sources :-) Anatoly Saveliev + + To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu + To unsubscribe, send email to majordomo@ jrc.ec.europa.eu with no subject and unsubscribe ai-geostats in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/
AI-GEOSTATS: typos
typos: it was phi, not sigma ... you'll get change at some phi Anatoly Saveliev + + To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu + To unsubscribe, send email to majordomo@ jrc.ec.europa.eu with no subject and unsubscribe ai-geostats in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/
Re: AI-GEOSTATS: Unconditional simulation
well Isobel explained how to go from Gaussian to uniform... On Tue, Nov 17, 2009 at 1:59 PM, Anatoly Saveliev s...@ksu.ru wrote: Pierre Goovaerts : For once, I agree with Isobel. sGs is the way to go... sGs - Gaussian by definition; he wants uniform :-) Anatoly Saveliev Pierre On Tue, Nov 17, 2009 at 6:00 AM, seba sebastiano.trevis...@libero.itmailto: sebastiano.trevis...@libero.it wrote: Hi Nick One way is to use simulated annealing (see gslib) putting as objective function your desired variogram and histogram. (but I guess that by means of some data transformation you can do that with a simple sequential gaussian simulation approach) Bye Sebas At 10.06 17/11/2009, Nick Hamm wrote: Dear all I want to simulate a spatially-correlated random field which follows a uniform rather than than Gaussian distribution. Does anybody know a straight-forward way to do this? Nick + + To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu mailto:ai-geost...@jrc.ec.europa.eu + To unsubscribe, send email to majordomo@ jrc.ec.europa.eu http://jrc.ec.europa.eu with no subject and unsubscribe ai-geostats in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/ + + To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu mailto:ai-geost...@jrc.ec.europa.eu + To unsubscribe, send email to majordomo@ jrc.ec.europa.eu http://jrc.ec.europa.eu with no subject and unsubscribe ai-geostats in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/ -- Pierre Goovaerts Chief Scientist at BioMedware Inc. 3526 W Liberty, Suite 100 Ann Arbor, MI 48103 Voice: (734) 913-1098 (ext. 202) Fax: (734) 913-2201 Courtesy Associate Professor, University of Florida Associate Editor, Mathematical Geosciences Geostatistician, Computer Sciences Corporation President, PGeostat LLC 710 Ridgemont Lane Ann Arbor, MI 48103 Voice: (734) 668-9900 Fax: (734) 668-7788 http://goovaerts.pierre.googlepages.com/ -- Pierre Goovaerts Chief Scientist at BioMedware Inc. 3526 W Liberty, Suite 100 Ann Arbor, MI 48103 Voice: (734) 913-1098 (ext. 202) Fax: (734) 913-2201 Courtesy Associate Professor, University of Florida Associate Editor, Mathematical Geosciences Geostatistician, Computer Sciences Corporation President, PGeostat LLC 710 Ridgemont Lane Ann Arbor, MI 48103 Voice: (734) 668-9900 Fax: (734) 668-7788 http://goovaerts.pierre.googlepages.com/
