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  • Goodness of Fit test for xtgee

    Hi Stata experts,

    I am using -xtgee- to run models. I have a base model that only includes control variables and the second model includes control and independent variables. Now I want to compare the goodness of fit for these two models. I tried to use AIC or BIC with the command -estat ic- but it is not supported. I know GEE is based on quasilikelihood theory rather than maximum likelihood theory.

    I read some papers that used GEE in my field. I found that they reported -2 log-likelihood value and delta (a sign of triangle) fit from the base model (χ2 ). I searched -2 log-likelihood and some people say it is deviance. My questions are:

    1. Is -2 log-likelihood value the same with deviance reported in GEE output?
    2. What command can I use to compare the goodness of fit for two gee models?

  • #2
    GEE models use quasilikelihood instead of ml, hence we can’t have AIC. But there is an user-written program named qic which provides its equivalent for GEE. That said, hopefully it applies for xtgee as well.
    Best regards,

    Marcos

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    • #3
      Originally posted by Dapeng Cheng View Post
      Hi Stata experts,

      I am using -xtgee- to run models. I have a base model that only includes control variables and the second model includes control and independent variables. Now I want to compare the goodness of fit for these two models. I tried to use AIC or BIC with the command -estat ic- but it is not supported. I know GEE is based on quasilikelihood theory rather than maximum likelihood theory.

      I read some papers that used GEE in my field. I found that they reported -2 log-likelihood value and delta (a sign of triangle) fit from the base model (χ2 ). I searched -2 log-likelihood and some people say it is deviance. My questions are:

      1. Is -2 log-likelihood value the same with deviance reported in GEE output?
      2. What command can I use to compare the goodness of fit for two gee models?
      Another way to test goodness-of-fit is to compute root mean square error and mean absolute prediction error.

      Comment

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