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Re: Fwd: st: Monte Carlo with preset spatial autocorrelation


From   "Susan Olivia" <olivia@primal.ucdavis.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: Fwd: st: Monte Carlo with preset spatial autocorrelation
Date   Mon, 11 May 2009 13:30:40 -0700

Thanks Austin. 

Glad to know that it's feasible to do this. 

My pre-determined weighting matrix will be an nxn positive
symmetric matrix in which for non-neighbors, w[i,j]= 0 while
w[i,j]=1 or a function of inverse distance w[i,j] = 1/d[i,j]
for neighbors, where d[i,j] is the distance between
observation i and j. 

Thanks in advance for the programming advice. 

Susan




> ---------- Forwarded message ----------
> From: Austin Nichols <austinnichols@gmail.com>
> Date: Mon, May 11, 2009 at 12:49 PM
> Subject: Re: st: Monte Carlo with preset spatial
> autocorrelation To: statalist@hsphsun2.harvard.edu
> 
> 
> Susan Olivia <olivia@primal.ucdavis.edu>:
> Note that the top of that page says the "FAQ is for users
> of Stata 6, an older version of Stata. It is not relevant
> for more recent versions." See -help drawnorm- for the
> modern equivalent.  If you can give the relevant matrix of
> correlations or covariances, the rest is easy.  What does
> your "pre-determined weighting matrix" look like?
> 
> On Mon, May 11, 2009 at 3:39 PM, Susan Olivia
> <olivia@primal.ucdavis.edu> wrote:
> > Dear Stata list,
> >
> > I am wondering whether it is possible to generate
> > artificial data with a given strength of spatial
> > autocorrelation (for a pre-determined weighting matrix)?
> >
> > I found on the STATA archive that Bill Gould wrote some
> > code about generating a random variable with a given
> > correlation structure. Here's the url:
> >
> > http://www.stata.com/support/faqs/stat/mvnorm.html
> >
> > But to do this in a spatial context would seem to be
> > more complicated given that the spatial autocorrelation
> > will depend not not only on the own and neighboring
> > values, but also how far apart they are place.
> >
> > If I can get any programming tips on this, much
> appreciated. >
> > Thanks,
> >
> > Susan
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