[R-sig-Geo] generate simulation data for a theoretical spatial model
Edzer Pebesma
edzer.pebesma at uni-muenster.de
Wed Feb 3 07:54:45 CET 2010
rusers.sh,
demo(ugsim)
in package gstat gives an example how to generate unconditional Gaussian
simulations. Specifying the covariates in a formula and the parameter
vector beta will add a deterministic trend to that.
If, in addition to that, you want unconditionally simulated residuals
added to a trend effect that is simulated as well, look at rmvnorm in
package mvtnorm how to generate realisations from the multivariate
normal distribution with given mean and covariance; finally, combine the
two.
--
Edzer
rusers.sh wrote:
> 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.pebesma at uni-muenster.de
> <mailto:edzer.pebesma at 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 <hengl at spatial-analyst.net
> <mailto:hengl at 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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>
>
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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.pebesma at wwu.de
> <mailto:e.pebesma at wwu.de>
>
>
>
>
> --
> -----------------
> Jane Chang
> Queen's
--
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.pebesma at wwu.de
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