Re: count simulations

Mats, Thanks for pointing out that R and 1-R are equivalent when R is a uniform 0-1 random deviate. There is an NM-TRAN example using 1-R in this paper: Frame B, Miller R, Lalonde RL. Evaluation of Mixture Modeling with Count Data using NONMEM. Journal of Pharmacokinetics and Pharmacodynamics. 2003;30(3):167-83. I have to admit to having cut and pasted this example and used it to show others how to simulate count data so it may have propogated that way too. Do you know of a clear explanation of why this simple algorithm produces Poisson distribution samples? Nick Mats Karlsson wrote: > Dear both, > > You have both simulated count data using the code below (or very similar). My question is why do you use LOG(1-R) rather than the simpler LOG(R)? If you’ve done it because you inherited the code, where did you get the code. > > *IF (ICALL.EQ.4) THEN* > > * T=0* > > * N=0* > > * DO WHILE (T.LT.1)* > > * CALL RANDOM (2,R)* > > * T=T-LOG(1-R)/LAMB* > > * IF (T.LT.1) N=N+1* > > * END DO* > > * DV=N* > > *ENDIF* > > Best regards, > > Mats > > Mats Karlsson, PhD > > Professor of Pharmacometrics > > Dept of Pharmaceutical Biosciences > > Uppsala University > > Box 591 > > 751 24 Uppsala Sweden > > phone: +46 18 4714105 > > fax: +46 18 471 4003 -- Nick Holford, Professor Clinical Pharmacology Dept Pharmacology & Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand [email protected] tel:+64(9)923-6730 fax:+64(9)373-7090 mobile: +64 21 46 23 53 http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford