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Re: st: RE: Negative predictions after trnbin0 |

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Thu, 20 Jan 2005 16:16:49 EST |

Someone asked me privately if exponentiating the linear predictor produces the fitted value for all maximum likelihood models. No it does not -- only for those models that are members of the exponential family of distributions having a log link. For example, you would exponentiate the linear predictor to obtain the fitted values, mu, for the log-normal, canonical Poisson, and log-negative binomial (the log link is not canonical for the NB distribution). Read the reference manual for GLM to get more information. Joe Hilbe -------------------- 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. Scott > > 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 * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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