[Rsiena-help] What does this error mean?
Victor, Jennifer Nicoll
jnvictor at pitt.edu
Wed Mar 21 16:38:09 CET 2012
Thank you to Ruth Ripley and Nate Doogan for providing help with this modeling problem. Based on their suggestions I have simplified the model and the effects; however, I still am unable to estimate the model. I'm seeking some general advice about fitting my data. Ruth wondered if Siena was the appropriate model for these data and I'm attempting to investigate this question and get more feedback. I appreciate further feedback from users of this list. I've also posted to the general Statnet list and have estimated ERGMs with these data.
I have encountered numerous problems relating to degeneracy and model specification. The data are from three waves (congresses) of US members of Congress and their co-membership in legislative organizations. I have a number of dyadic and nodal attribute variables as well about the actors. The hypothesis I seek to test is whether there are bridging-ties among the legislators through their memberships in legislative organizations. I suspect that the high degree of density in the data may be part of the problem.
One approach I've tried is to simply calculate betweenness (in the SNA package) for each network of legislators in each time period, add this as a vertex attribute, and estimate an ERGM, in which betweenness is a nodal-covariate; however these models have difficulty with convergence and fit. I am unable to obtain MCMC standard errors, and even after numerous attempts at respecification the diagnostics suggest that fit may be problematic.
Another approach is to use Siena. Siena should allow me to take advantage of the longitudinal nature of the data. But the output that I've been able to obtain suggests that the model is problematic, as Ruth and Nate have pointed out. See output below.
So my various attempts at testing this hypothesis using these advanced methods have left me frustrated. While we can (and have) tested the idea using a variety of less sophisticated methods, it seems to me that the advanced statistical methods, such as those available through ERGM and Siena, are designed for exactly this type of modeling; however, I have not been able to achieve satisfactory findings that are reportable. I can continue to try to reparameterize the models to search for fit and to avoid degeneracy, but I would like to hear thoughts of readers of this list regarding:
- the appropriate method for testing the hypothesis
- best practices for iterative modeling and specification for these types of models (given that each iteration is so computationally expensive)
- which architecture (ERGM, Siena, something else?) seems most appropriate for the data?
Siena verbose output:
> myeffus
effectName include fix test initialValue parm
1 constant caucuses rate (period 1) TRUE FALSE FALSE 100.00000 0
2 constant caucuses rate (period 2) TRUE FALSE FALSE 70.41182 0
3 degree (density) TRUE FALSE FALSE 0.03424 0
4 betweenness TRUE FALSE FALSE 0.00000 0
5 same usparty TRUE FALSE FALSE 0.00000 0
> myusmodel<-sienaModelCreate(useStdInits=FALSE, projname='siena_us_02')
> usans02<-siena07(myusmodel, data=myusdata,effects=myeffus, batch=TRUE, verbose=TRUE)
Stochastic approximation algorithm.
Initial value for gain parameter = 0.2.
Start of the algorithm.
Observed function values are
1. 123349.0000 2. 5156392.0000 3. 123580.0000
Start phase 0
theta: 0.0342 0.0000 0.0000
Current parameter values:
0.03424475 0.00000000 0.00000000
Start phase 1
Phase 1 Iteration 1 Progress: 0%
Phase 1 Iteration 2 Progress: 0%
Phase 1 Iteration 3 Progress: 0%
Phase 1 Iteration 4 Progress: 0%
Phase 1 Iteration 5 Progress: 0%
Phase 1 Iteration 10 Progress: 0%
Phase 1 Iteration 15 Progress: 1%
Time per iteration in phase 1 = 50.7582
Average deviations NR generated statistics and targets
after phase 1:
-39637.750000
6787139.500000
-39249.250000
Diagonal values of derivative matrix :
32772.3901 524963179.7438 38364.4002
dfra :
32772.39 3087677.74 17924.11
5095442.50 524963179.74 3357687.99
39393.49 3556965.56 38364.40
inverse of dfra :
0.00029660366044 -0.00000197940840 0.00003466438151
-0.00000228738847 0.00000001994553 -0.00000067696765
-0.00009248392723 0.00000018324891 0.00005323684804
Full Quasi-Newton-Raphson step after phase 1:
1. 26.551774
2. -0.252611
3. -2.820084
This step is multiplied by the factor 0.10000.
Intervention 1.4.2: jump after phase 1 decreased by factor 26.5517736817607 .
Phase 1 achieved after 16 iterations.
theta: 1.03424 -0.00951 -0.10621
Current parameter values:
1.034244749 -0.009513884 -0.106210769
Phase 2 has 4 subphases.
Each subphase can be repeated up to 4 times
Start phase 2.1
Phase 2 Subphase 1 Iteration 1 Progress: 3%
Phase 2 Subphase 1 Iteration 2 Progress: 3%
theta 1.0433 -0.0096 -0.0982
ac 1.009 0.985 1.014
Phase 2 Subphase 1 Iteration 3 Progress: 3%
Phase 2 Subphase 1 Iteration 4 Progress: 3%
theta 1.06134 -0.00977 -0.08233
ac 1.006 0.992 1.008
Phase 2 Subphase 1 Iteration 5 Progress: 3%
Phase 2 Subphase 1 Iteration 6 Progress: 3%
theta 1.07940 -0.00994 -0.06651
ac 1.01 0.99 1.01
Phase 2 Subphase 1 Iteration 7 Progress: 3%
Phase 2 Subphase 1 Iteration 8 Progress: 3%
theta 1.0975 -0.0101 -0.0508
ac 1.004 0.995 1.007
Phase 2 Subphase 1 Iteration 9 Progress: 3%
Phase 2 Subphase 1 Iteration 10 Progress: 3%
theta 1.1155 -0.0103 -0.0353
ac 1.004 0.996 1.006
Phase 2 Subphase 1 Iteration 20 Progress: 3%
theta 1.2058 -0.0112 0.0411
ac 1.002 0.999 1.006
Phase 2 Subphase 1 Iteration 30 Progress: 4%
theta 1.2961 -0.0121 0.1146
ac 1.00 1.00 1.01
Phase 2 Subphase 1 Iteration 40 Progress: 4%
theta 1.386 -0.013 0.185
ac 1.00 1.00 1.01
Phase 2 Subphase 1 Iteration 50 Progress: 5%
theta 1.4767 -0.0139 0.2526
ac 1.00 1.00 1.01
Phase 2 Subphase 1 Iteration 60 Progress: 5%
theta 1.5670 -0.0148 0.3167
ac 1.00 1.00 1.01
Phase 2 Subphase 1 Iteration 70 Progress: 5%
theta 1.6574 -0.0157 0.3777
ac 1.00 1.00 1.01
Phase 2 Subphase 1 Iteration 80 Progress: 6%
theta 1.7477 -0.0166 0.4350
ac 1.00 1.00 1.01
Phase 2 Subphase 1 Iteration 90 Progress: 6%
theta 1.8380 -0.0174 0.4888
ac 1.00 1.00 1.01
Phase 2 Subphase 1 Iteration 100 Progress: 7%
theta 1.9283 -0.0182 0.5386
ac 1.00 1.00 1.01
Phase 2 Subphase 1 Iteration 110 Progress: 7%
theta 2.019 -0.019 0.585
ac 1.00 1.00 1.01
Error in x$FRAN(zsmall, xsmall) :
Unlikely to terminate this epoch: more than 1000000 steps
Calls: siena07 ... proc2subphase -> doIterations -> <Anonymous> -> .Call
Execution halted
If you got to the end of this post, I appreciate your attention and any advice you might have!
Best,
Jennifer Victor
___________________________________________
Jennifer Nicoll Victor
Assistant Professor
Department of Political Science
University of Pittsburgh
4600 Wesley W. Posvar Hall
(412) 624-7204
E-mail: jnvictor at pitt.edu
Homepage: http://www.polisci.pitt.edu/person/jennifer-nicoll-victor
-----Original Message-----
From: rsiena-help-bounces at r-forge.wu-wien.ac.at [mailto:rsiena-help-bounces at r-forge.wu-wien.ac.at] On Behalf Of Ruth Ripley
Sent: Friday, March 09, 2012 8:39 AM
To: rsiena-help at r-forge.wu-wien.ac.at
Subject: Re: [Rsiena-help] What does this error mean?
Dear Jennifer,
The errors both indicate that you are fitting a model which is too
complicated for your data. I suggest you start with just the default
effects and add others one by one to see which one is giving the problem.
Regards,
Ruth
On 09/03/2012 02:45, Victor, Jennifer Nicoll wrote:
> After letting my RSiena model for 12 hours on a high memory machine I received the following output. What advice can you provide to help me overcome this error?
>
>> myusmodel<-sienaModelCreate(useStdInits=FALSE, projname='siena_us_01')
>> usans01<-siena07(myusmodel, data=myusdata,effects=myeffus)
> No X11 device available, forcing use of batch mode
> Start phase 0
> theta: 0.0342 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
>
> Start phase 1
> Phase 1 Iteration 1 Progress: 0%
> Phase 1 Iteration 2 Progress: 0%
> Phase 1 Iteration 3 Progress: 0%
> Phase 1 Iteration 4 Progress: 0%
> Phase 1 Iteration 5 Progress: 0%
> Phase 1 Iteration 10 Progress: 0%
> Phase 1 Iteration 15 Progress: 0%
> Phase 1 Iteration 20 Progress: 1%
> Phase 1 Iteration 25 Progress: 1%
> Phase 1 Iteration 30 Progress: 1%
> Phase 1 Iteration 35 Progress: 1%
> Phase 1 Iteration 40 Progress: 1%
> Error in solve.default(z$dfra) :
> system is computationally singular: reciprocal condition number = 4.58224e-20
> theta: 0.03416 0.01123 -0.01203 -0.00364 -0.17691 -0.40977 0.23908 -0.14365 0.25761 1.00000 0.21070
>
> Start phase 2.1
> Phase 2 Subphase 1 Iteration 1 Progress: 14%
> Phase 2 Subphase 1 Iteration 2 Progress: 14%
> theta 0.04034 0.01130 -0.01206 -0.00342 -0.17012 -0.40569 0.24425 -0.13712 0.25683 0.99919 0.20914
> ac 1.063 1.049 1.006 1.044 1.025 1.063 1.067 1.065 0.651 0.954 0.866
> Phase 2 Subphase 1 Iteration 3 Progress: 14%
> Phase 2 Subphase 1 Iteration 4 Progress: 14%
> theta 0.05223 0.01144 -0.01213 -0.00299 -0.15670 -0.39786 0.25405 -0.12452 0.25377 0.99722 0.20489
> ac 1.136 1.112 1.013 1.096 1.105 1.134 1.161 1.137 0.690 0.984 0.881
> Phase 2 Subphase 1 Iteration 5 Progress: 14%
> Phase 2 Subphase 1 Iteration 6 Progress: 14%
> theta 0.06332 0.01158 -0.01221 -0.00256 -0.14449 -0.39056 0.26230 -0.11261 0.24601 0.99419 0.19779
> ac 1.137 1.140 0.936 1.145 1.104 1.136 1.164 1.140 0.863 0.962 0.967
> Phase 2 Subphase 1 Iteration 7 Progress: 14%
> Error in x$FRAN(zsmall, xsmall) :
> Unlikely to terminate this epoch: more than 1000000 steps
> Calls: siena07 ... proc2subphase -> doIterations -> <Anonymous> -> .Call
> Execution halted
>
>
>
> Jennifer N. Victor
> Assistant Professor of Political Science
> University of Pittsburgh
> 4600 Posvar Hall
> jnvictor at pitt.edu
> (412) 624-7204
> _______________________________________________
> Rsiena-help mailing list
> Rsiena-help at lists.r-forge.r-project.org
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