  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@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@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.
>>>>
>>>>
>>>>
>>>>
>>> _______________________________________________
>>> R-sig-Geo mailing list
>>> R-sig-Geo@stat.math.ethz.ch
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>>>
>>>
>>>
>>
>>
>>
>>
>>
>
> --
> 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@wwu.de
>
>


-- 
-----------------
Jane Chang
Queen's

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