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st: RE: Generating random variates


From   "Steichen, Thomas" <STEICHT@RJRT.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: Generating random variates
Date   Thu, 22 Aug 2002 16:11:25 -0400

anirban basu writes:
> I was wondering if there is any easy way to generate random 
> variates for
> Poisson, Negative Binomial and Inverse Gaussian distributions  where
> E(y) = mu = exp(xb) in Stata. Thanks,


Check out:
 
STB-41  sg44.1 . . . . . . . . . . . .  Correction to random number generators
        (help rnd if installed)  . . . . . . . . J. Hilbe and W. Linde-Zwirble
        1/98    p.23; STB Reprints Vol 7, p.166
        faster version plus minor changes


Here is part of the help file:

help for random number generators                update from STB-28: sg44
.-                                                    Hilbe/Linde-Zwirble

                                                  Current as of 28Jan1999
Random number generators
------------------------

[noncentral] Student's t:   rndt obs df [delta]
                             Example: rndt 10000 10
                                      rndt 10000 10 3

 [noncentral] Chi-square:   rndchi obs df [lambda]
                             Example: rndchi 10000 4
                                      rndchi 10000 4 3
                             
          [noncentral] F:   rndf obs df_numer df_denom [lambda]
                             Example: rndf 10000 4 15
                                      rndf 10000 4 15 3
                             
              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 = variable with p values
                                   den = case denominator (1=binary)
                                  
       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)
 
    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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