[R-sig-Geo] generate simulation data for a theoretical spatial model

[Edzer Pebesma]

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>
>
>   
>> 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.pebesma at wwu.de