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Currently I'm working with ecological studies, where my response is a count variable. I need to estimate several models, each one represents a city. Afterwards I aggregate them to obtain meta-analysis and forestplots. Naturally I'm using Poisson models within GAMs but I noticed that some of the data is overdispersed. Neither I can't find satisfactory answer nor figure out if I can use Poisson for some cities and Quasi-Poisson for the others. Is it erroneous to use both type of models for one meta-analysis? Is it just the matter of the estimated error?

kjetil b halvorsen
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Tom
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    Why not fit all the models using quasi-Poisson? – mdewey Jun 04 '20 at 10:02
  • I commited an understatement. Some Quasi-Poisson models cannot be fit returning "step size truncated due to divergence" error in mgcv::gam. All Poisson models can be fit without any problem. – Tom Jun 04 '20 at 11:15
  • In that case I am at a loss I am afraid. – mdewey Jun 04 '20 at 11:59
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    If your question is if you can have groups-specific overdispersion parameters, the answer is yes. See https://stats.stackexchange.com/questions/564285/when-does-a-group-specific-dispersion-parameter-for-the-negative-binomial-distri, https://stats.stackexchange.com/questions/418777/dharma-to-detect-overdispersion-in-negative-binomial – kjetil b halvorsen Jun 16 '22 at 14:30

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