Date: Thu, 2 Mar 2006 22:14:07 -0500 Tony Yang "SAS(r) Discussion" Tony Yang Re: proc genmod-criteria for overdispersion To: nevin.krishna@gmail.com text/plain; charset=ISO-8859-1

> 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

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