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st: RE: AW: RE: AW: Generating random numbers


From   "Barth Riley" <[email protected]>
To   <[email protected]>
Subject   st: RE: AW: RE: AW: Generating random numbers
Date   Wed, 25 Nov 2009 10:40:34 -0600

Hi Martin

This is the code I used:

local sd = int(real(subinstr("`c(current_time)'",":","",.)))
qui set seed `sd'

Barth


-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Martin Weiss
Sent: Wednesday, November 25, 2009 10:21 AM
To: [email protected]
Subject: st: AW: RE: AW: Generating random numbers


<> 

Just out of curiosity, does your call to -set seed- use


*************
di `=clock("`=subinstr("`c(current_time)'",":","",.)'", "hms")'
*************

then?



HTH
Martin


-----Ursprüngliche Nachricht-----
Von: [email protected]
[mailto:[email protected]] Im Auftrag von Barth Riley
Gesendet: Mittwoch, 25. November 2009 17:09
An: [email protected]
Betreff: st: RE: AW: Generating random numbers

Hi Martin

I took your advice and placed my set seed command at the top of my do file
(which contains several nested loops). And I used the c(current_time)
function, stripping the ":" out of the string and converting the resulting
string into a number for my seed value. This did the trick! Thanks

Barth


-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Martin Weiss
Sent: Wednesday, November 25, 2009 9:07 AM
To: [email protected]
Subject: st: AW: Generating random numbers


<> 

Your "seed generating process" for "sd" also uses -uniform()-, i.e. your
first line is itself dependent on a -seed-. 

Some Statalisters also use the current date, such as in -set seed
`=date("`c(current_date)'", "DMY")'- to -set seed-...


HTH
Martin

-----Ursprüngliche Nachricht-----
Von: [email protected]
[mailto:[email protected]] Im Auftrag von Barth Riley
Gesendet: Mittwoch, 25. November 2009 15:35
An: [email protected]
Betreff: st: Generating random numbers

Hello

I have written a program in Stata to create several datasets containing
simulated item response values, which are based on randomly generated item
response theory parameters. I am finding that the randomly generated
parameters for each dataset are following a predictable pattern (one set of
parameter values for odd-numbered datasets; another set of values for
even-numbered datasets). I have been using the set seed command to try to
avoid this repetitive pattern, for example:

local sd = `j' * int(uniform() * 1000)
set seed `sd'
...
local b = invnormal(uniform())

Where `j' is a forvalues counter variable. Is there a better way to do this?

Thanks

Barth


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