rusers.sh wrote:
Hi Tomislav,
Thanks for your info on unconditional simulation. For conditional
simulations, i still cannot find any useful information.
I searched the R site and didnot find the possible method to do
conditional simulations.
1. CondSimu(RandomField): trend: Not programmed yet. (used by universal
kriging)
2. grf(geoR): generates unconditional simulations of Gaussian random fields
3. sim.Krig(fields) #Conditonal simulation of a spatial process
It seems to be based on the actual dataset,not a theoretical model.
4. krige(gstat ):Simple, Ordinary or Universal, global or local, Point or
Block Kriging,or simulation
x <- krige(log(zinc)~x+y, meuse, meuse.grid, model = m, block =
c(40,40),nsim=1)
rusers.sh, please use
x <- krige(log(zinc)~x+y, meuse, meuse.grid, model = m, nmax=40, nsim=1)
both adding the block=c(40,40) as well as omitting the nmax=40 tremendously
increased the computing time you needed, the second even more (in an O(n^2)
manner) than the first.
--
Edzer
I used the above modified codes from krige(gstat ) example to see the
effect of "nsim", but unfortunately, it took a longer time and cannot get
the results. I guess it used the simulation method to test the model, not
what i want. (My system is XP, R2.10.0, gstat09.-64.)
Anybody can give me further information on generating the conditional
simulations from a theoretical model just like the unconditional examples
that Tomislav provided?
Thanks a lot.
2010/1/31 Tomislav Hengl <he...@spatial-analyst.net>
Dear rusers.sh,
Here are few simple examples of how to simulate (not-normal)
distributions and point processes using geoR and spatstat:
http://spatial-analyst.net/book/node/388
See also:
http://leg.ufpr.br/geoR/geoRdoc/vignette/geoRintro/geoRintrose8.html#x9-120008
I guess that covariates can be also included (I guess that you then need
to switch to conditional simulations - not sure).
This should also work for lattice (polygon) data so that you will have
jumps in values (but I guess you would still work in gridded systems?).
T. Hengl
http://home.medewerker.uva.nl/t.hengl/
rusers.sh wrote:
Hi all,
In classical statistics, we always need to generate a theoretical model
such as y=a+b1*x1+b2*x2+e to study some new estimation content. I am
wondering how to generate the similar spatial dataset for a theoretical
model.
Say y is response variable, x1 and x2 are explanatory variables.
1. If y is a continous variable, how should we generate the dataset for a
theoretical spatial point process model in R?
2. If y is a continous variable, how should we generate the dataset for a
theoretical spatial lattice data model in R?
3. If y is 0/1 binary variable, how should we generate the dataset for a
theoretical spatial point process model in R?
4. If y is 0/1 binary variable, how should we generate the dataset for a
ttheoretical spatial lattice data in R?
spatstat and other packages allow us to generate a dataset of a specified
point process and other models, but it seems that they donot allow us to
include possible explanatory variables into a theoretical model. Maybe i
missed some ideas in them.
Anybody can express some ideas or point out some useful resources on the
above four different situations? Small examples in R are preferred.
Thanks a lot.
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--
Edzer Pebesma
Institute for Geoinformatics (ifgi), University of Münster
Weseler Straße 253, 48151 Münster, Germany. Phone: +49 251
8333081, Fax: +49 251 8339763 http://ifgi.uni-muenster.de
http://www.52north.org/geostatistics e.pebe...@wwu.de
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