It works. The problem is that it only generates the simulated data based
on our observed dataset,e.g. "meuse" here. I wonder if we can generate
the simulated dataset from the user-specified model with covariates
included, such as y~a1*x1+a2*x2+spatial effect. Y can be continuous or 0/1
variables. Something like this.
The idea is we first specify a theoretical model, and then generate the
simulated data based on this model. The coefficients and spatial effects
are fixed by users, so we may study some new methods.
Thanks.
2010/2/2 Edzer Pebesma <edzer.pebe...@uni-muenster.de
<mailto:edzer.pebe...@uni-muenster.de>>
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
<mailto: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
<mailto:e.pebe...@wwu.de>
--
-----------------
Jane Chang
Queen's
