[email protected]
> many thanks for answering. I am not sure I understood what
> you said. I tell you
> what I have done:
> 1. I have run a poisson regression model where I estimated
> a count data
> dependent variable as a function of 13 variables calculated
> across 90
> observations + a costant, using the "poisson" command.
> 2. by using the "poisgof" command I then evaluated the
> goodness of fit, which
> gives me a chi2 prob = 0.9.
> 3. I have also run a negative binomial regression model on
> the same data, by
> using the command "nbreg" as well as the command "nbreg
> [var], dispersion
> (constant)". In the first case, the alfa was not
> significant (chi2=0.5). In the
> second case, the alfa was also not significant (although
> the chi2 prob=0.25).
> 4.I have then use the command "summarize [var], detail" in
> order to have the
> mean and variance of the dependent variable. The result is
> that the difference
> between the two measures is about 0.40.
> 5. Following a procedure suggested in the FAQs STATA web
> page, I have then used
> the "nbvarg" command in order to estimate the theoretical
> probability for the
> poisson and negative binomial distributions as well as for
> the observed. But,
> if I have understood you correctly, the two theoretical
> distributions cannot be
> compared.
> QUESTIONS
> 1. Do you think I can adopt the poisson regression model
> given these results?
Sorry, I don't think one should presume to give research
advice like this. I'm not an expert on these models
and even I were I don't have a sight of your data
or a feeling for whatever science is involved.
> 2. What do you mean by estimating the nbreg with or without
> covariates?
Covariates here = explanatory, predictor, "independent"
variables. If I go
. nbreg varname
I am fitting a model without covariates, i.e. a
unconditional negative binomial distribuion. It might
not be parameterised as you would wish, but that
is a different matter. -nbvargr- (and also -nbfit-
on SSC) are different ways of approaching it.
> 3. What do you mean by fitting the poisson with one
> parameter and fitting the
> negative binomial with two?
In the absence of covariates, the Poisson
"model" has one parameter -- the mean response, say
-- and the negative binomial has two.
See any book on probability distributions for more
detail.
Nick
[email protected]
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