[Rsiena-help] Error in x$FRAN(zsmall, xsmall) : Unlikely to terminate this epoch: more than 1000000 steps

[Philip Leifeld]

Hi,

I am relatively new to RSiena, so hello to everybody on this list. I am 
currently replicating a two-mode RSiena analysis that was published a 
while ago by somebody else. I have the data and I have the code and I 
can reasonably well replicate two of the three models reported, but I 
keep getting an error message during the estimation of the third model. 
The probability that this error message shows up seems to vary across 
RSiena versions. I thought maybe you could let me know your thoughts on 
what may be causing the problem or how to avoid it. Here is what I get 
after about five minutes:

Phase 2 Subphase 1 Iteration 3 Progress: 14%
Error in x$FRAN(zsmall, xsmall) :
   Unlikely to terminate this epoch:  more than 1000000 steps
Calls: siena07 ... proc2subphase -> doIterations -> <Anonymous> -> .Call

I found an old thread in the archive of this mailing list where somebody 
had the same problem. In that case, the advice was to try unconditional 
estimation. Here is the message:

http://lists.r-forge.r-project.org/pipermail/rsiena-help/2012-March/000237.html

In my case, there is also only one dependent network, so I tried 
sienaModelCreate(cond = FALSE), but it did not change anything. The 
other potential reason reported in the thread was that the composition 
change was possibly too substantial. However, in the data I am dealing 
with, the vast majority of both node types persists between consecutive 
time steps, and apparently the estimation worked in the original 
analysis, otherwise the authors couldn't have reported their results.

Interestingly, the problem varies across RSiena versions. First, I was 
using r267 (the most recent build on R-Forge) and r232 (the current 
stable release on CRAN) on my desktop computer, and the problem showed 
up every single time and usually around the tenth iteration. Then I 
started to use r169, which is the version the original authors were 
using in their original analysis (as reported in the paper), and the 
problem disappeared, except for every 10th estimation or so on average 
(still during the tenth iteration). Then I thought, OK, fine, this is 
not optimal, but I can live with the problem as long as it shows up only 
every 10th run. So I installed r169 also on the HPC cluster I have 
access to, and there it seems to produce the error message every single 
time during the third iteration (as in the example message printed above).

I really have no clue (a) what is causing the problem and (b) why its 
probability of occurrence seems to vary across versions and computers.

In one of the earlier e-mails cited above, Tom Snijders stated that the 
problem is most likely due to "fitting a model which is too complicated 
for your data" and that one should go one step back and build a simpler 
model, and then add other model terms step by step. While I agree that 
this is a good strategy for model-building, this is not really 
satisfactory for a replication because the very goal is to test whether 
the same model with the same data leads approximately to the same 
coefficients and standard errors. I was also wondering whether this 
error message is basically a sign of degeneracy, similar to estimating a 
degenerate model in statnet, but with an error message rather than 
hard-to-detect convergence issues.

I would be happy to receive feedback about what exactly is going on or 
how to avoid the problem. Thanks very much in advance!

Philip

--
Postdoctoral Fellow
University of Konstanz, Germany, and
Eawag, ETH domain, Switzerland