[statnet_help] Upgrading from ERGM v3.10 to v4.6

[Carter T. Butts via statnet_help]

Hi, Aditya -

I will defer to the mighty Pavel for the exact best formula to reproduce 
3.x fits with the latest codebase.  (You need to switch convergence 
detection to "Hotelling," and there are some other things that must be 
modified.)  However, as a general matter, for challenging models where 
Geyer-Thompson-Hummel has a hard time converging (particularly on a 
large node set), you may find it useful to try the stochastic 
approximation method (main="Stochastic" in your control argument will 
activate it). G-T-H can (in principle) have sharper convergence when 
near the solution, but in practice SA fails more gracefully.   I would 
suggest increasing your default MCMC thinning interval (MCMC.interval), 
given your network size; depending on density, extent of dependence, and 
other factors, you may need O(N^2) toggles per step.  It is sometimes 
possible to get away with as few as k*N (for some k in, say, the 5-100 
range), but if your model has substantial dependence and is not 
exceptionally sparse then you will probably need to be in the quadratic 
regime.  One notes that it can sometimes be helpful when getting things 
set up to run "pilot" fits with the default or otherwise smaller 
thinning intervals, so that you can discover if e.g. you have a data 
issue or other problem before you spend the waiting time on a 
high-quality model fit.

To put in the obligatory PSA, both G-T-H and SA are simply different 
strategies for computing the same thing (the MLE, in this case), so both 
are fine - they just have different engineering tradeoffs.  So use 
whichever proves more effective for your model and data set.

Hope that helps,

-Carter


On 5/16/24 7:52 AM, Khanna, Aditya via statnet_help wrote:

> Dear Statnet Dev and User Community:

>

> I have an ERGM that I fit previously with ERGM v3.10.4 on a directed 

> network with 32,000 nodes. The model included in- and out-degrees, in 

> addition to other terms. The complete Rout from this fit can be seen 

> here 

> <https://urldefense.com/v3/__https://gist.github.com/khanna7/aefd836baf47463051439c9e72764388__;!!CzAuKJ42GuquVTTmVmPViYEvSg!KsbhvmLlx8TkLK7y2NKz59hK4-4H7KXVV7dEyUG4vcQzi4Mh7nO-9HupA7_ep2V2p9KkD_i00tcg6nDqczDwORmxHSho$>. 

> I am now trying to reproduce this fit with ergm v4.6, but the model 

> does not converge. (See here 

> <https://urldefense.com/v3/__https://gist.github.com/khanna7/fbabdde53c79504dfeaebd215bb5ee20__;!!CzAuKJ42GuquVTTmVmPViYEvSg!KsbhvmLlx8TkLK7y2NKz59hK4-4H7KXVV7dEyUG4vcQzi4Mh7nO-9HupA7_ep2V2p9KkD_i00tcg6nDqczDwOW7y31IM$>.)

>

> I am looking for ideas on how to trouble shoot this. One suggestion I 

> got was to set values for the "tuning parameters" in the v4.6 to their 

> defaults from v3.11.4. But ERGM v4.6 has a lot more  parameters that 

> can be specified, and I am not sure which ones make most sense to 

> consider.

>

> I would be grateful for any suggestions on this or alternate ideas to try.

>

> Many thanks,

> Aditya

>

>

>

>

> -- 

>

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> 	

>

> Aditya S. Khanna, Ph.D.

>

> Assistant Professor

>

> Department of Behavioral and Social Sciences

>

> Center for Alcohol and Addiction Studies

>

> Brown University School of Public Health

>

> Pronouns: he/him/his

>

>

> 401-863-6616

>

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