Re: st: simulating random numbers from zero inflated negative binomial estimates

[Ari Samaranayaka <ari.samaranayaka@ipru.otago.ac.nz>]

Thank you Paul, all you said makes sense to me, and very helpful.
 Ari


On 4/06/2011 2:27 a.m., E. Paul Wileyto wrote:
> I've never used predict with the ir option, but I assume it predicts a 
> mean incidence rate GIVEN that class membership is not an inflated 
> zero.  I suspect that it will not include the natural variability of 
> the outcome, let alone zero-inflation.  What our simulation does is 
> take those predicted linear model, adds in the natural variability for 
> negative binomial, and then adds zero-inflation on top of it, all to 
> reflect the natural variation you would see.
>
> In order to gauge whether the estimate is working well, you should 
> simulate the data multiple times, and generate means for the point 
> estimates, and coverage probabilities.  What we did was to take our 
> original model, assumed the estimated parameters are true, and then 
> used them to simulate only one more data set.  Repeat that last step 
> 200x, and see how often your CI includes your true value.
>
> Looking at the script again.  This first part grabs the estimates from 
> fitting your data:
>
> zinb cignums drug  week, inf(drug  week)
> predict p1 , pr
> predict p2 , xb
> predict lnalpha , xb eq(#3)
> gen alph=exp(lnalpha)
>
>
> This next part simulates the data and should be repeated many times.
>
> gen xg=rgamma(1/alph, alph*p2)
> gen pg=rpoisson(xg)
> gen zi=runiform()>p1
> gen newcigs=zi*pg
>
> zinb newcigs drug  week, inf(drug  week)
>
> There are many ways to collect the parameter estimates and CI's from 
> the simulations.  I'll leave that to you.
>
> P
>
> On 6/3/2011 1:03 AM, Ari Samaranayaka wrote:
> > Dear Paul
> > Thank you very much for the great help. Your are the first person to 
> > answer my question. Your answer works, and I understood the logic you 
> > used in your codes. Simulated random variates goes quite closely with 
> > observed data. I interpret this as a reasonable model fit. Great. 
> > Thank you.
> >
> > I expected whenever the ZINB model fit is reasonably good, if we use 
> > the ZINB postestimation predict command to produce predicted numbers, 
> > those predicted numbers also should goes closely with observed data.
> > For example, if I use the command
> > predict expec, ir
> > then distribution of resultant values in "expec" should have similar 
> > distribution to observed data (because we do not specify an 
> > "exposure" in our model). However those 2 distributions quite 
> > different. Did I misinterpret the result from predict command.
> > Thank you again
> > Ari

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