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I should add that what I wrote before should not explain
non-convergence per se but rather guide you towards identifying the
problem of the model specification vs data. Looking at GOF plots for
the most complex model that you fit which converged should help you
understand why it stops converging when you add GWESP.

~Michal

On Thu, Jun 4, 2020 at 9:54 PM MichaƂ Bojanowski <[log in to unmask]> wrote:
>
> Zachary,
>
> > I haven't spent much time looking at model GOF since I don't have a good comparison. The models that include nodematch terms obviously fit better than a null model that only contains the edges term, but that didn't seem particularly informative. If a model without GWESP appears to fit well, would it be acceptable to simply use it and ignore any structural effects.
>
> I guess the most important question is whether the model without GWESP
> accounts well for the ESP distribution. If it does, then you do not
> need GWESP term in the model. Folding this onto Goodreau et al
> exposition it would mean that the differential homophily you have in
> your model accounts for higher density within groups, and that already
> also accounts for the amount of transitivity in the network as whole
> (with higher density some transitivity will happen within groups "by
> accident"). Consequently, there would be not much transitivity left to
> "explain" by GWESP on top of the terms you already have in the model.
>
> Ad whether it is acceptable to go with a model without any structural
> (i.e. network endogeneous effects):
>
> This is of course a matter if it makes sense substantively. From a
> purely data-driven standpoint if a model with "demographic" effects
> only (attribute-related terms such as dyadcov, nodecov, nodefactor,
> nodematch, nodemix etc.) accounts for the network structure well in
> the sense of reproducing the important features in the data (degree
> distribution, ESP distribution and so on), then I would say yes.
>
> hth,
> Michal

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