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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