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Re: st: RE: Negative predictions after trnbin0

From   [email protected]
To   [email protected]
Subject   Re: st: RE: Negative predictions after trnbin0
Date   Thu, 20 Jan 2005 14:37:15 EST

trpois0 and trnbin0 are simliar to other ML based programs in Stata. When  
you type predict after modeling, you get the linear predictor, XB.  To  obtain 
the fitted values, type
predict lp
gen mu=exp(lp)
Note that Stata's -poisson- and nbreg commands have been written so that  
using predict afterwards generates the fitted value rather than the standard ML  
linear predictor. 
You can check this out yourself by comparing output; e.g.
use cancer
tab drug, gen(drug)
poisson studytim age drug2 drug3
predict mu_p
trpois0 studytim age drug2 drug3
predict lp
gen mu_tr = exp(lp)
l mu_p lp mu_tr
You'll find that mu_p and mu_tr are the same. Type 
    gen lp_p = ln(mu_p) 
to obtain the poisson command linear predictors 
The same is the case with negative binomial: nbreg and trnbin0. 
I nearly always use the glm version of poisson since it displays BIC and  AIC 
GOF statistics, along with other GOF values and statistical diagnostics, as  
well as allowing you to immediately calculate lp, mu, and 11 types  of 
residuals. With the -glm- command you can also obtain standard errors  using jacknife 
and bootstrap -- and a variety of robust techniques. 
With the negative binomial it is a bit different. I use nbreg to obtain an  
estimate of the ancillary parameter and then use that value for alpha in the 
glm  version. I can then utilize the above mentioned modeling utilities that glm 
 offers, and obtain the many residual statistics for
model assessment. 
Joe Hilbe

I believe -trnbin0- is producing the linear prediction.   Exponentiate the
values to get the predicted number of  events.


> Dear all,
> I have used a  truncated negative binomial model (trbin0) for the
> conditional number of  visits to the doctor. I have found out that the
> predictions after the  estimation of the positive number of visits is
> sometimes negative. Is  there any one who could help me to understand why
> that happens?
> Many thanks in advance,
> Dolores
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