generate simulation data for a theoretical spatial model

  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.