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

[Tomislav Hengl]

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.
>