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st: Re: RND problem and fix


From   Jhilbe@aol.com
To   statalist@hsphsun2.harvard.edu
Subject   st: Re: RND problem and fix
Date   Mon, 15 Aug 2005 13:38:14 EDT

List Members

Jon Deeks of the Centre for  Statistics in Medicine has pointed out a problem 
in my random number generators  which you should be aware of.  All of the 
related commands begin with  
-rnd-, such as -rndpoi-, which is the Poisson random number generator.   If 
the generators have been installed, type -hlp rnd- to see a listing of all  the 
files.

The problem occurs only when one is engaged in repeat uses of  a rnd program, 
which is set at a constant user defined seed number. This is  common in 
simulation studies.  The problem is that the used defined seed is  ignored after 
the first use. This problem did not arise with older versions of  Stata, but it 
does with versions 8 and 9. 

The simple fix is to change  the line indicating version number  to -version 
4.0-. That is,  
replace
version 3.1  with version 4.0

I will make the appropriate change on  all of teh rnd files and repost them 
to my Stata user Web site, which is  -jhilbe/rnd-.  I am also developing my own 
web site and will have them  posted there as well. I'll also have many other 
programs posted. The site is  tentatively to be called www.hilbestat.com, but 
please wait until it is ready  before accessing. I'll notify the list. 

I apologize for any  inconvenience that this problem may have caused. Thanks 
to Jon for finding it,  and determining, with the help of Stata technical 
support, how to fix the  problem. 

FYI to newer users, I have developed random number generators  for the 
following distributions (taken from part of rnd.hlp).

Joe  HiIbe

Student's  t:   ^rndt^ obs  df
Example: rndt 10000  10

Chi-square:   ^rndchi^ obs  df
Example: rndchi 10000  4

F:   ^rndf^  obs df_numer  df_denom
Example: rndf 10000 4  15

log  normal:   ^rndlgn^ obs mean  stddev
Example: rndlgn 10000 0  0.5

Poisson:    ^rndpoi^ obs  mean
^rndpoix^ [ mu  ]
Example: rndpoi 10000  4
rndpoix  mu

Poisson:    ^rndpod^ obs mean dispersion
(ovedispersed)   ^rndpodx^ [mu],  s(#)
Example: rndpod 10000 4  1.2
rndpodx mu,  s(1.2)

binomial:    ^rndbin^ obs prob  numb
^rndbinx^ [ prob ]  den
Example: rndbin 10000 0.5  1
rndbinx mu  den
Note: mu= varaiable with p  values
den = case denominator  (1=binary)
Use inverse links of logit, probit, cloglog, loglog to obtain  
simulated logit, probit, etc data  sets.                              

negative binomial:   ^rndnbx^  [mu] ,  k(#)
Example: rndnblx mu,  k(0.5)


Gamma:    ^rndgam^ obs shape  scale
^rndgamx^ [mu],  s(#)
Example: rndgam 10000 4  2
rndgamx mu,  s(1)
Note: s(1) specifies a shape parameter of  1;
the scale is calculated from  mu*shape

inverse Gaussian:   ^rndivg^  obs mean  sigma
^rndivgx^ [mu],  s(#)
Example: rndivg 10000 10  0.05
rndivgx mu,  s(0.05)
Note: mu =  1/sqrt(eta)
variance =  sigma^2*mu*3

exponential:   ^rndexp^ obs  shape
Example: rndexp 10000  3

Weibull:   ^rndwei^ obs shape  scale
Example: rndwei 10000 3  2

Beta binomial:   ^rndbb^ obs  denom prob  k
Example: 10000 200 0.2  0.05
Note: prob= p =  a1/(a1+a2)
k = dispersion =  1/(a1+a2+1)
This  generator will return beta-binomial  random
deviates within  the following constraints.  Although
k can take  any value from 0 to 1, in this  program
k is limited  because of the volotility of  the
distribution  outside this range of k. k must be  as
follows: k< the  lessor of p' and (1-p')/2 where  p'=p
if p<0.5, else  p'=1-p. This should work well  within
the physical  representation of an overdispersed binomial.

Generalized  logistic:   ^rndglog^ obs L A  T
(3 parameter)      Example: rndglog 10000 3.0 0.7  4.5
Note: L = (long) right hand  tail
A = (alpha) left hand  tail
T = (time) position  parameter
Based on Fit-Meister (W. Linde-Zwirble)  

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