|
> Hi, Nevin;
>
> The value 1.3935 is hard to say if the Poisson model has overdispersion,
> while you can try an alternative model such as negative binomial or
> generalized Poisson model to fit the data, and you can try to use the score
> test if the NB or GP model is better than the Poisson model.
>
> Meanwhile, there is a dispersion index, defined as d=variance/mean,
> if d>1, we can consider there is overdispersion in your data, this time you
> can try NB or GP model, since 1.3935 is not big enough to consider other
> models.
>
> Also, I suggest you make a plot for your response count variable, the
> x-axis is the count, and the y-axis is the observed percent, to see if there
> is some characteristic. Generally speaking, Poisson model, NB and GP model
> will fit a unimodal data, if your data do not have this characteristic, you
> may consider other alternatives.
>
> I am attaching my submitted paper for your reference.
>
>
> On 3/2/06, nevin.krishna@gmail.com <nevin.krishna@gmail.com> wrote:
> >
> > Hello all,
> >
> > i am trying to do a poisson regression with an outcome being rate of
> > disease, and independent variables being agecode (categories of age),
> > region, and sex.
> > I am trying to figure out how to interpret whether there is evidence of
> > overdispersion: i was taught that this can be accomplished by
> > looking at the value/df from the criteria for assessing goodness of fit
> > output...the closer the value is to 1 the less the influence of
> > overdispersion.
> >
> > when i run the following code, i get the following output..
> >
> >
> > proc genmod data=mening_poisson;
> > class agecode sex region;
> > model count=agecode sex region / offset=l_pop dist=poi link=log
> > type3;
> > run;
> >
> > Criteria For Assessing Goodness Of Fit
> >
> > Criterion DF Value
> > Value/DF
> >
> > Deviance 161 224.3462
> > 1.3935
> > Scaled Deviance 161 224.3462
> > 1.3935
> > Pearson Chi-Square 161 204.5569
> > 1.2705
> > Scaled Pearson X2 161 204.5569
> > 1.2705
> > Log Likelihood 843.6215
> >
> >
> > Is 1.39 considered close to 1 ? what is the cutpoint for what
> > determines overdispersion based on the value/df stat?
> >
> > Thanks, nevin
> >
>
>
>
> --
> Best regards,
>
> Tony
>
>
--
Best regards,
Tony
|
