Re: st: Saving intermediate results (variables) when running -simulate-

["Rodrigo A. Alfaro" <raalfaroa@gmail.com>]

Rachel,

Maarten gave you the efficient answer: set seed. With that you can reproduce 
exactly the same simulations in the future and change whatever you want in 
the analysis of the simulated datasets.

Alternative, but inefficiently, you could divide the process in 2 steps: (1) 
generate a general dataset, (2) do the analysis using loops. It is 
inefficient for 2 reasons: you are storing simulated datasets (trash) in 
your hard drive and a similar process (generating the data, doing the 
analysis) will take much more time here than using -simulate-.

I don't know under what conditions the alternative is actually necessary. 
Maybe your model is so far from the standard uniform or multivariate normal, 
then you have to compute several transformations. For example, you are 
interested in running y against x, and doing analysis of that relationship 
(prediction, etc). But y and x must be generated in a very long procedure 
that it is time-consuming.

Rodrigo.



----- Original Message ----- 
From: "Maarten buis" <maartenbuis@yahoo.co.uk>
To: <statalist@hsphsun2.harvard.edu>
Sent: Thursday, August 23, 2007 3:04 AM
Subject: Re: st: Saving intermediate results (variables) when 
running -simulate-



> --- Rachel <academicgirl@gmail.com> wrote:
> > If I understand this correctly, the estimates resulting from the
> > simulation below should be the same for all 20 repetitions if the
> > same seed is used for each one.
> That would be true, but that is not what I have done in the example, or
> what I advised you to do. If you look at the example (and actually
> execute it, you will see it from the results) you will see I set the
> seed only twice: once for generating the original 20 simulations, and
> once for reproducing the 20 datasets. So I proposed that you set the
> seed, than create 20, or 100, 1000, or whatever number of different
> random datasets. In that case you can recreate all those datasets just
> by setting that original seed again. What I did not propose is that you
> set the seed for each random dataset is created, as that would indeed
> lead to 20 identical datasets. In other words, -set seed 12345- should
> appear outside the loop not inside the loop.
>
> > Would it make sense to set the seed itself to be a random number and
> > then return the seed as a scalar so that the rest of the dataset
> > could be reproduced?
> No, that is unnecesary.
>
>
>
> -----------------------------------------
> Maarten L. Buis
> Department of Social Research Methodology
> Vrije Universiteit Amsterdam
> Boelelaan 1081
> 1081 HV Amsterdam
> The Netherlands
>
> visiting address:
> Buitenveldertselaan 3 (Metropolitan), room Z434
>
> +31 20 5986715
>
> http://home.fsw.vu.nl/m.buis/
> -----------------------------------------
>
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