[statnet_help] MCMC does not converge even after 50 iterations for STERGM

Pavel N. Krivitsky p.krivitsky at unsw.edu.au
Tue Aug 11 19:40:40 PDT 2020

Hi, Vivek,
The first thing to try might be to reduce the dgwdsp decay to something
less than .25, since you might be dealing with a phase transition (a
model such that a small change in parameter value causeing a huge
change in network structure).

> Questions:

> 1. In particular: how should I interpret the log output that output

> Estimating Function Values. For a model that is going to converge do

> these values get close to 0?

Yes. At the MLE the expected network statistics equal to the observed,
and so their difference as a function of the parameters is called an
estimating function. Of course, when we use MCMC, we have to
approximate this by taking the sample means of the simulated statistics
and subtracting off the observed.

> 2. What are the scaling limits of STERGMS? How many nodes/edges per

> time point can they handle gracefully? Also is there a scalability

> limit on number of timepoints?

Underneath it all, STERGM CMLE just calls ergm() with a specially
formulated specification. So, STERGM CMLE will scale about as well as
ergm itself.

> 3. In general, what diagnostics in the output log attached can I look

> at to get a sense of why my model is not converging?

That the estimating function values are fluctuating like this suggests
that you might have a phase transition, hence my advice above.
If some of these statistics were going off to infinity, you would have
a diagnostic of multicollinearity or related problems. (The current
master branch on GitHub, if you feel up to installing it, tries to
diagnose those problems before the estimation.)
You can use the MCMC.runtime.traceplot= control parameter to have it
make traceplots of MCMC runs to check for insufficient burn-in or a
phase transition.
I hope this helps,

> I would greatly appreciate any suggestions.


> Best,

> Vivek.



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