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

Vivek Kulkarni viveksck at gmail.com
Tue Aug 11 12:43:27 PDT 2020


Hi,
I am trying STREGMS over 6-time points to model tie formation/persistence
in concept networks over time. Briefly, two concepts are linked if they
co-occur in a document. However, I am having trouble getting the STERGM's
to converge. I have tried various formulae including dyad specific terms,
node covariates, node factor terms but in all cases, MCMC still does not
converge. I am attaching a sample run with gwdsp where the formation
process fails to converge even after 50 iterations. Adding more covariates
like nodematch/nodemix does not seem to help or tuning MCMC parameters does
not help. I was wondering if I could get some pointers on how to interpret
the MCMC log outputs to get a sense of what I might be missing here.

I am using the following formulas:
samp.fit <- stergm(list_networks,
formation = ~edges + offset(nodefactor('born_status',
levels=c("no"))) + edgecov('seating') + absdiff('age') +
dgwdsp(decay=0.25,fixed=T),
dissolution = ~edges + offset(nodefactor('born_status',
levels=c("no"))) + absdiff('age') + edgecov('seating'),
estimate = "CMLE", times=c(1:(ix-1)),
offset.coef.form = -Inf,
offset.coef.diss = -Inf,
control=control.stergm(CMLE.MCMC.burnin=100000,
CMLE.MCMC.interval=20000, CMLE.control.form =
control.ergm(MCMLE.maxit=50),CMLE.control.diss =
control.ergm(MCMLE.maxit=50)),
verbose=TRUE
)

*Details*:
age: age of each concept
born_status: is a node factor variable that is used to handle entry and
exit of concepts gracefully. In short, I am using it to model structural
zeros so that no edges can exist between concepts that are not born in the
given time by using an offset command.

*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?
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?
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?

I would greatly appreciate any suggestions.

Best,
Vivek.
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Loading required package: statnet.common

Attaching package: ‘statnet.common’

The following object is masked from ‘package:base’:

order

Loading required package: network
network: Classes for Relational Data
Version 1.16.0 created on 2019-11-30.
copyright (c) 2005, Carter T. Butts, University of California-Irvine
Mark S. Handcock, University of California -- Los Angeles
David R. Hunter, Penn State University
Martina Morris, University of Washington
Skye Bender-deMoll, University of Washington
For citation information, type citation("network").
Type help("network-package") to get started.

sna: Tools for Social Network Analysis
Version 2.5 created on 2019-12-09.
copyright (c) 2005, Carter T. Butts, University of California-Irvine
For citation information, type citation("sna").
Type help(package="sna") to get started.

Loading required package: networkDynamic

networkDynamic: version 0.10.1, created on 2020-01-16
Copyright (c) 2020, Carter T. Butts, University of California -- Irvine
Ayn Leslie-Cook, University of Washington
Pavel N. Krivitsky, University of Wollongong
Skye Bender-deMoll, University of Washington
with contributions from
Zack Almquist, University of California -- Irvine
David R. Hunter, Penn State University
Li Wang
Kirk Li, University of Washington
Steven M. Goodreau, University of Washington
Jeffrey Horner
Martina Morris, University of Washington
Based on "statnet" project software (statnet.org).
For license and citation information see statnet.org/attribution
or type citation("networkDynamic").


Attaching package: ‘pracma’

The following object is masked from ‘package:sna’:

cutpoints

Loading required package: tergm
Loading required package: ergm

ergm: version 3.10.4, created on 2019-06-10
Copyright (c) 2019, Mark S. Handcock, University of California -- Los Angeles
David R. Hunter, Penn State University
Carter T. Butts, University of California -- Irvine
Steven M. Goodreau, University of Washington
Pavel N. Krivitsky, University of Wollongong
Martina Morris, University of Washington
with contributions from
Li Wang
Kirk Li, University of Washington
Skye Bender-deMoll, University of Washington
Chad Klumb
Based on "statnet" project software (statnet.org).
For license and citation information see statnet.org/attribution
or type citation("ergm").

NOTE: Versions before 3.6.1 had a bug in the implementation of the bd()
constriant which distorted the sampled distribution somewhat. In
addition, Sampson's Monks datasets had mislabeled vertices. See the
NEWS and the documentation for more details.

NOTE: Some common term arguments pertaining to vertex attribute and
level selection have changed in 3.10.0. See terms help for more
details. Use ‘options(ergm.term=list(version="3.9.4"))’ to use old
behavior.


tergm: version 3.6.1, created on 2019-06-12
Copyright (c) 2019, Pavel N. Krivitsky, University of Wollongong
Mark S. Handcock, University of California -- Los Angeles
with contributions from
David R. Hunter, Penn State University
Steven M. Goodreau, University of Washington
Martina Morris, University of Washington
Nicole Bohme Carnegie, New York University
Carter T. Butts, University of California -- Irvine
Ayn Leslie-Cook, University of Washington
Skye Bender-deMoll
Li Wang
Kirk Li, University of Washington
Based on "statnet" project software (statnet.org).
For license and citation information see statnet.org/attribution
or type citation("tergm").

Loading required package: ergm.count

ergm.count: version 3.4.0, created on 2019-05-15
Copyright (c) 2019, Pavel N. Krivitsky, University of Wollongong
with contributions from
Mark S. Handcock, University of California -- Los Angeles
David R. Hunter, Penn State University
Based on "statnet" project software (statnet.org).
For license and citation information see statnet.org/attribution
or type citation("ergm.count").

NOTE: The form of the term ‘CMP’ has been changed in version 3.2 of
‘ergm.count’. See the news or help('CMP') for more information.


statnet: version 2019.6, created on 2019-06-13
Copyright (c) 2019, Mark S. Handcock, University of California -- Los Angeles
David R. Hunter, Penn State University
Carter T. Butts, University of California -- Irvine
Steven M. Goodreau, University of Washington
Pavel N. Krivitsky, University of Wollongong
Skye Bender-deMoll
Martina Morris, University of Washington
Based on "statnet" project software (statnet.org).
For license and citation information see statnet.org/attribution
or type citation("statnet").

unable to reach CRAN
Warning message:
In dir.create(sprintf("%s/output", input_dir)) : './output' already exists
[1] "./support_vector_machines_2_node.csv-./support_vector_machines_2_edge.csv"
[1] "./support_vector_machines_3_node.csv-./support_vector_machines_3_edge.csv"
Fitting formation...
Evaluating network in model.
Initializing Metropolis-Hastings proposal(s): tergm:MH_FormationMLETNT
Initializing model.
Using initial method 'MPLE'.
Fitting initial model.
Starting maximum pseudolikelihood estimation (MPLE):
Evaluating the predictor and response matrix.
MPLE covariate matrix has 120261 rows.
Maximizing the pseudolikelihood.
Finished MPLE.
Starting Monte Carlo maximum likelihood estimation (MCMLE):
Density guard set to 105469 from an initial count of 5251 edges.

Iteration 1 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-5.34509859 -Inf
edgecov.seating absdiff.age
0.00000000 0.01219352
gwdsp.OTP.fixed.0.25
0.02029659
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-1029.384 0.000 -2479.763
gwdsp.OTP.fixed.0.25
-61888.458
Starting MCMLE Optimization...
Optimizing with step length 0.0391754871027024.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 3.546.

Iteration 2 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-5.424032e+00 -Inf
edgecov.seating absdiff.age
2.651559e-16 1.084553e-01
gwdsp.OTP.fixed.0.25
2.067790e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-1009.840 0.000 -2395.267
gwdsp.OTP.fixed.0.25
-61723.474
Starting MCMLE Optimization...
Optimizing with step length 0.0349263721170099.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.777.

Iteration 3 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-5.483841e+00 -Inf
edgecov.seating absdiff.age
7.352000e-16 1.703080e-01
gwdsp.OTP.fixed.0.25
2.183252e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-975.5039 0.0000 -2311.7852
gwdsp.OTP.fixed.0.25
-58667.5231
Starting MCMLE Optimization...
Optimizing with step length 0.0513813185963511.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 4.869.

Iteration 4 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-5.575560e+00 -Inf
edgecov.seating absdiff.age
-1.826569e-15 2.772538e-01
gwdsp.OTP.fixed.0.25
2.237387e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-927.0352 0.0000 -2175.7188
gwdsp.OTP.fixed.0.25
-56304.2130
Starting MCMLE Optimization...
Optimizing with step length 0.0552334189923601.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 4.793.

Iteration 5 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-5.659094e+00 -Inf
edgecov.seating absdiff.age
-2.170612e-15 3.687555e-01
gwdsp.OTP.fixed.0.25
2.329603e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-857.9365 0.0000 -2019.2021
gwdsp.OTP.fixed.0.25
-51314.0832
Starting MCMLE Optimization...
Optimizing with step length 0.0675485556349958.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 3.

Iteration 6 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-5.699393e+00 -Inf
edgecov.seating absdiff.age
-1.661146e-15 4.273379e-01
gwdsp.OTP.fixed.0.25
2.339582e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-816.3408 0.0000 -1929.5596
gwdsp.OTP.fixed.0.25
-50192.3782
Starting MCMLE Optimization...
Optimizing with step length 0.0549825356839066.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 4.634.

Iteration 7 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-5.794999e+00 -Inf
edgecov.seating absdiff.age
3.166687e-15 5.172433e-01
gwdsp.OTP.fixed.0.25
2.485288e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-698.6514 0.0000 -1722.8164
gwdsp.OTP.fixed.0.25
-33750.8018
Starting MCMLE Optimization...
Optimizing with step length 0.0759191592516049.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 3.484.

Iteration 8 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-5.866925e+00 -Inf
edgecov.seating absdiff.age
3.594108e-15 6.019316e-01
gwdsp.OTP.fixed.0.25
2.473841e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-528.252 0.000 -1506.715
gwdsp.OTP.fixed.0.25
11199.860
Starting MCMLE Optimization...
Optimizing with step length 0.0746853878398426.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 3.313.

Iteration 9 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-5.941346e+00 -Inf
edgecov.seating absdiff.age
6.433401e-14 6.877147e-01
gwdsp.OTP.fixed.0.25
2.484799e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-384.8203 0.0000 -1364.8789
gwdsp.OTP.fixed.0.25
72415.1016
Starting MCMLE Optimization...
Optimizing with step length 0.088493501748961.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 4.049.

Iteration 10 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.013264e+00 -Inf
edgecov.seating absdiff.age
9.115637e-14 7.793799e-01
gwdsp.OTP.fixed.0.25
2.492991e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-290.625 0.000 -1228.592
gwdsp.OTP.fixed.0.25
98010.748
Starting MCMLE Optimization...
Optimizing with step length 0.0435392237077261.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.746.

Iteration 11 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.059482e+00 -Inf
edgecov.seating absdiff.age
9.804483e-14 8.263124e-01
gwdsp.OTP.fixed.0.25
2.456302e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-273.166 0.000 -1195.520
gwdsp.OTP.fixed.0.25
106284.387
Starting MCMLE Optimization...
Optimizing with step length 0.0197607456482947.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.676.

Iteration 12 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.088076e+00 -Inf
edgecov.seating absdiff.age
8.646372e-14 8.516438e-01
gwdsp.OTP.fixed.0.25
2.317860e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-270.3311 0.0000 -1168.2803
gwdsp.OTP.fixed.0.25
107915.9000
Starting MCMLE Optimization...
Optimizing with step length 0.0108228364619351.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.941.

Iteration 13 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.099646e+00 -Inf
edgecov.seating absdiff.age
8.526187e-14 8.700375e-01
gwdsp.OTP.fixed.0.25
1.831344e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-324.0156 0.0000 -1164.4121
gwdsp.OTP.fixed.0.25
79429.7768
Starting MCMLE Optimization...
Optimizing with step length 0.105721228486285.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 3.254.

Iteration 14 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.181580e+00 -Inf
edgecov.seating absdiff.age
1.086916e-13 9.523844e-01
gwdsp.OTP.fixed.0.25
1.841147e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-334.4717 0.0000 -1041.4219
gwdsp.OTP.fixed.0.25
45219.1568
Starting MCMLE Optimization...
Optimizing with step length 0.126459012558736.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 3.

Iteration 15 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.238616e+00 -Inf
edgecov.seating absdiff.age
1.049682e-13 1.020952e+00
gwdsp.OTP.fixed.0.25
1.851956e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-392.1367 0.0000 -925.6777
gwdsp.OTP.fixed.0.25
-4911.3765
Starting MCMLE Optimization...
Optimizing with step length 0.16112208194536.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 4.363.

Iteration 16 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.319864e+00 -Inf
edgecov.seating absdiff.age
1.001675e-13 1.113517e+00
gwdsp.OTP.fixed.0.25
1.858667e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-488.1709 0.0000 -932.4111
gwdsp.OTP.fixed.0.25
-53430.5580
Starting MCMLE Optimization...
Optimizing with step length 0.11495508178526.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 5.06.

Iteration 17 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.347603e+00 -Inf
edgecov.seating absdiff.age
9.961550e-14 1.119803e+00
gwdsp.OTP.fixed.0.25
2.037819e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-448.9707 0.0000 -869.5312
gwdsp.OTP.fixed.0.25
-50128.5068
Starting MCMLE Optimization...
Optimizing with step length 0.0720423055856808.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 3.098.

Iteration 18 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.396126e+00 -Inf
edgecov.seating absdiff.age
1.011411e-13 1.146636e+00
gwdsp.OTP.fixed.0.25
2.206302e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-412.6455 0.0000 -787.8086
gwdsp.OTP.fixed.0.25
-46762.9599
Starting MCMLE Optimization...
Optimizing with step length 0.0722131403466451.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.455.

Iteration 19 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.409798e+00 -Inf
edgecov.seating absdiff.age
1.006329e-13 1.140578e+00
gwdsp.OTP.fixed.0.25
2.314747e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-397.9766 0.0000 -763.7900
gwdsp.OTP.fixed.0.25
-39408.9921
Starting MCMLE Optimization...
Optimizing with step length 0.250639527316562.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 6.72.

Iteration 20 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.528366e+00 -Inf
edgecov.seating absdiff.age
1.141117e-13 1.243752e+00
gwdsp.OTP.fixed.0.25
2.370594e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-243.4189 0.0000 -459.2012
gwdsp.OTP.fixed.0.25
-23909.5505
Starting MCMLE Optimization...
Optimizing with step length 0.281735470364271.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 3.229.

Iteration 21 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.575595e+00 -Inf
edgecov.seating absdiff.age
9.752839e-14 1.295292e+00
gwdsp.OTP.fixed.0.25
2.415563e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-94.28613 0.00000 -277.21582
gwdsp.OTP.fixed.0.25
17902.78313
Starting MCMLE Optimization...
Optimizing with step length 0.510998259570153.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.279.

Iteration 22 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.628002e+00 -Inf
edgecov.seating absdiff.age
1.052544e-13 1.348442e+00
gwdsp.OTP.fixed.0.25
2.420450e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
18.28906 0.00000 -179.25879
gwdsp.OTP.fixed.0.25
68908.83687
Starting MCMLE Optimization...
Optimizing with step length 0.281096522781185.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.837.

Iteration 23 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.672465e+00 -Inf
edgecov.seating absdiff.age
1.051411e-13 1.384297e+00
gwdsp.OTP.fixed.0.25
2.411986e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
54.36133 0.00000 -171.79102
gwdsp.OTP.fixed.0.25
95830.11830
Starting MCMLE Optimization...
Optimizing with step length 0.0682734552854799.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.925.

Iteration 24 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.686535e+00 -Inf
edgecov.seating absdiff.age
1.014979e-13 1.403924e+00
gwdsp.OTP.fixed.0.25
2.357444e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
69.99902 0.00000 -163.36914
gwdsp.OTP.fixed.0.25
105416.66953
Starting MCMLE Optimization...
Optimizing with step length 0.0516215277565689.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.235.

Iteration 25 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.679494e+00 -Inf
edgecov.seating absdiff.age
9.415422e-14 1.400043e+00
gwdsp.OTP.fixed.0.25
2.274233e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
75.38867 0.00000 -142.03809
gwdsp.OTP.fixed.0.25
102786.46236
Starting MCMLE Optimization...
Optimizing with step length 0.0344192542249111.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 0.9812.

Iteration 26 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.645709e+00 -Inf
edgecov.seating absdiff.age
1.237301e-13 1.378638e+00
gwdsp.OTP.fixed.0.25
2.213327e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
49.09961 0.00000 -173.55371
gwdsp.OTP.fixed.0.25
95060.41788
Starting MCMLE Optimization...
Optimizing with step length 0.0288928942065054.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.443.

Iteration 27 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.618333e+00 -Inf
edgecov.seating absdiff.age
1.059955e-13 1.362000e+00
gwdsp.OTP.fixed.0.25
2.103780e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
52.44824 0.00000 -168.68555
gwdsp.OTP.fixed.0.25
93296.86038
Starting MCMLE Optimization...
Optimizing with step length 0.0403282478132077.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.285.

Iteration 28 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.601831e+00 -Inf
edgecov.seating absdiff.age
1.105742e-13 1.361084e+00
gwdsp.OTP.fixed.0.25
1.981205e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
22.6582 0.0000 -163.9912
gwdsp.OTP.fixed.0.25
76703.0486
Starting MCMLE Optimization...
Optimizing with step length 0.112861108931724.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.343.

Iteration 29 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.596513e+00 -Inf
edgecov.seating absdiff.age
1.108575e-13 1.363454e+00
gwdsp.OTP.fixed.0.25
1.950518e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
40.39844 0.00000 -94.97559
gwdsp.OTP.fixed.0.25
63172.68485
Starting MCMLE Optimization...
Optimizing with step length 0.15911420430671.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.443.

Iteration 30 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.584664e+00 -Inf
edgecov.seating absdiff.age
1.114957e-13 1.357695e+00
gwdsp.OTP.fixed.0.25
1.920181e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
9.194336 0.000000 -105.090820
gwdsp.OTP.fixed.0.25
46079.924050
Starting MCMLE Optimization...
Optimizing with step length 0.233431568587356.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.196.

Iteration 31 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.572206e+00 -Inf
edgecov.seating absdiff.age
1.221430e-13 1.355592e+00
gwdsp.OTP.fixed.0.25
1.878615e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
9.939453 0.000000 -46.510742
gwdsp.OTP.fixed.0.25
29110.615676
Starting MCMLE Optimization...
Optimizing with step length 0.376310317073285.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.141.

Iteration 32 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.572968e+00 -Inf
edgecov.seating absdiff.age
1.193209e-13 1.368202e+00
gwdsp.OTP.fixed.0.25
1.841572e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-44.59668 0.00000 -56.38770
gwdsp.OTP.fixed.0.25
-6273.94131
Starting MCMLE Optimization...
Optimizing with step length 1.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 0.3441.
Step length converged once. Increasing MCMC sample size.

Iteration 33 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.532773e+00 -Inf
edgecov.seating absdiff.age
1.290261e-13 1.353439e+00
gwdsp.OTP.fixed.0.25
1.837239e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-158.9375 0.0000 -234.2495
gwdsp.OTP.fixed.0.25
-47471.9689
Starting MCMLE Optimization...
Optimizing with step length 0.227308641587688.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
Starting MCMC s.e. computation.
The log-likelihood improved by 9.588.

Iteration 34 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.540788e+00 -Inf
edgecov.seating absdiff.age
1.290141e-13 1.337471e+00
gwdsp.OTP.fixed.0.25
2.025515e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-163.0371 0.0000 -251.0596
gwdsp.OTP.fixed.0.25
-44617.6815
Starting MCMLE Optimization...
Optimizing with step length 0.0856502002458405.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 3.744.

Iteration 35 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.556688e+00 -Inf
edgecov.seating absdiff.age
1.291381e-13 1.320080e+00
gwdsp.OTP.fixed.0.25
2.237047e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-172.9678 0.0000 -285.9844
gwdsp.OTP.fixed.0.25
-37888.9413
Starting MCMLE Optimization...
Optimizing with step length 0.124242131308664.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.647.

Iteration 36 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.591940e+00 -Inf
edgecov.seating absdiff.age
1.317820e-13 1.331963e+00
gwdsp.OTP.fixed.0.25
2.314164e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-136.1611 0.0000 -227.0664
gwdsp.OTP.fixed.0.25
-27539.8500
Starting MCMLE Optimization...
Optimizing with step length 0.341935383002005.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.043.

Iteration 37 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.612578e+00 -Inf
edgecov.seating absdiff.age
1.332754e-13 1.340451e+00
gwdsp.OTP.fixed.0.25
2.360764e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-61.44922 0.00000 -134.08887
gwdsp.OTP.fixed.0.25
820.43677
Starting MCMLE Optimization...
Optimizing with step length 1.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.772.
Step length converged once. Increasing MCMC sample size.

Iteration 38 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.653444e+00 -Inf
edgecov.seating absdiff.age
1.395463e-14 1.385930e+00
gwdsp.OTP.fixed.0.25
2.366107e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
66.82764 0.00000 -120.65088
gwdsp.OTP.fixed.0.25
82380.41376
Starting MCMLE Optimization...
Optimizing with step length 0.658888465443814.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
Starting MCMC s.e. computation.
The log-likelihood improved by 2.879.

Iteration 39 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.681600e+00 -Inf
edgecov.seating absdiff.age
-5.082007e-15 1.407378e+00
gwdsp.OTP.fixed.0.25
2.360922e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
89.02148 0.00000 -129.42188
gwdsp.OTP.fixed.0.25
104325.82594
Starting MCMLE Optimization...
Optimizing with step length 0.0299209696583385.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 3.257.

Iteration 40 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.673257e+00 -Inf
edgecov.seating absdiff.age
-4.861174e-15 1.413001e+00
gwdsp.OTP.fixed.0.25
2.152208e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
98.04492 0.00000 -70.35742
gwdsp.OTP.fixed.0.25
96242.43909
Starting MCMLE Optimization...
Optimizing with step length 0.0301382627209378.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.281.

Iteration 41 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.655642e+00 -Inf
edgecov.seating absdiff.age
8.086825e-15 1.399522e+00
gwdsp.OTP.fixed.0.25
2.061132e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
74.20215 0.00000 -115.19141
gwdsp.OTP.fixed.0.25
94408.33925
Starting MCMLE Optimization...
Optimizing with step length 0.0468006742675201.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.854.

Iteration 42 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.620624e+00 -Inf
edgecov.seating absdiff.age
2.254381e-15 1.383828e+00
gwdsp.OTP.fixed.0.25
1.927265e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
70.01953 0.00000 -60.49609
gwdsp.OTP.fixed.0.25
72197.60058
Starting MCMLE Optimization...
Optimizing with step length 0.141024045206646.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 0.8463.

Iteration 43 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.618738e+00 -Inf
edgecov.seating absdiff.age
8.342837e-16 1.384179e+00
gwdsp.OTP.fixed.0.25
1.910487e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
57.57324 0.00000 -21.50879
gwdsp.OTP.fixed.0.25
52964.10389
Starting MCMLE Optimization...
Optimizing with step length 0.114151339352238.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.419.

Iteration 44 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.603505e+00 -Inf
edgecov.seating absdiff.age
3.668797e-15 1.379979e+00
gwdsp.OTP.fixed.0.25
1.861717e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
27.3418 0.0000 -24.4541
gwdsp.OTP.fixed.0.25
35327.4888
Starting MCMLE Optimization...
Optimizing with step length 0.505589868005123.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.994.

Iteration 45 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.574318e+00 -Inf
edgecov.seating absdiff.age
-6.586801e-15 1.368651e+00
gwdsp.OTP.fixed.0.25
1.836350e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-64.77148 0.00000 -86.86035
gwdsp.OTP.fixed.0.25
-13918.24281
Starting MCMLE Optimization...
Optimizing with step length 1.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 0.5017.
Step length converged once. Increasing MCMC sample size.

Iteration 46 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.566735e+00 -Inf
edgecov.seating absdiff.age
-8.273402e-15 1.371009e+00
gwdsp.OTP.fixed.0.25
1.838558e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-185.4832 0.0000 -265.6824
gwdsp.OTP.fixed.0.25
-48880.0987
Starting MCMLE Optimization...
Optimizing with step length 0.149405880034721.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
Starting MCMC s.e. computation.
The log-likelihood improved by 8.731.

Iteration 47 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.592610e+00 -Inf
edgecov.seating absdiff.age
-7.438008e-15 1.358771e+00
gwdsp.OTP.fixed.0.25
2.094126e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-184.1709 0.0000 -271.2764
gwdsp.OTP.fixed.0.25
-43006.0801
Starting MCMLE Optimization...
Optimizing with step length 0.121694024861973.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.616.

Iteration 48 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.599001e+00 -Inf
edgecov.seating absdiff.age
-7.156425e-15 1.354255e+00
gwdsp.OTP.fixed.0.25
2.161462e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-182.2764 0.0000 -280.4229
gwdsp.OTP.fixed.0.25
-39893.6136
Starting MCMLE Optimization...
Optimizing with step length 0.137145355976951.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 3.26.

Iteration 49 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.612470e+00 -Inf
edgecov.seating absdiff.age
-7.294589e-15 1.352702e+00
gwdsp.OTP.fixed.0.25
2.287888e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-149.6768 0.0000 -217.4082
gwdsp.OTP.fixed.0.25
-33310.8088
Starting MCMLE Optimization...
Optimizing with step length 0.12741706384111.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.062.

Iteration 50 of at most 50 with parameter:
edges offset(nodefactor.born_status.no)
-6.626453e+00 -Inf
edgecov.seating absdiff.age
-6.488506e-15 1.354508e+00
gwdsp.OTP.fixed.0.25
2.390133e-02
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Average estimating function values:
edges edgecov.seating absdiff.age
-94.79492 0.00000 -145.49219
gwdsp.OTP.fixed.0.25
-22590.36827
Starting MCMLE Optimization...
Optimizing with step length 0.303928394194308.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
Starting MCMC s.e. computation.
The log-likelihood improved by 2.438.
MCMLE estimation did not converge after 50 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; se
e 'init' under ?control.ergm for details.
Finished MCMLE.
Evaluating log-likelihood at the estimate. Using 20 bridges: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 .
This model was fit using MCMC. To examine model diagnostics and check
for degeneracy, use the mcmc.diagnostics() function.
Fitting dissolution...
Evaluating network in model.
Initializing Metropolis-Hastings proposal(s): tergm:MH_DissolutionMLETNT
Initializing model.
Using initial method 'MPLE'.
Fitting initial model.
Starting maximum pseudolikelihood estimation (MPLE):
Evaluating the predictor and response matrix.
MPLE covariate matrix has 1802 rows.
Maximizing the pseudolikelihood.
Finished MPLE.
Stopping at the initial estimate.
Evaluating log-likelihood at the estimate.

==========================
Formation fit diagnostics
==========================

Sample statistics summary:

Iterations = 16384:1063936
Thinning interval = 1024
Number of chains = 1
Sample size per chain = 1024

1. Empirical mean and standard deviation for each variable,
plus standard error of the mean:

Mean SD Naive SE Time-series SE
edges 94.79 48.04 1.501 7.656
edgecov.seating 0.00 0.00 0.000 0.000
absdiff.age 145.49 85.00 2.656 17.678
gwdsp.OTP.fixed.0.25 22590.37 3130.52 97.829 1199.954

2. Quantiles for each variable:

2.5% 25% 50% 75% 97.5%
edges 2.00 65 96 124 187.4
edgecov.seating 0.00 0 0 0 0.0
absdiff.age -12.85 89 152 204 296.0
gwdsp.OTP.fixed.0.25 16199.49 20743 22374 24370 28893.2


Sample statistics cross-correlations:
edges edgecov.seating absdiff.age gwdsp.OTP.fixed.0.25
edges 1.0000000 NA 0.9460685 0.3337597
edgecov.seating NA 1 NA NA
absdiff.age 0.9460685 NA 1.0000000 0.3257464
gwdsp.OTP.fixed.0.25 0.3337597 NA 0.3257464 1.0000000

Sample statistics auto-correlation:
Chain 1
edges edgecov.seating absdiff.age gwdsp.OTP.fixed.0.25
Lag 0 1.0000000 NaN 1.0000000 1.0000000
Lag 1024 0.9187853 NaN 0.9513040 0.9867816
Lag 2048 0.8514702 NaN 0.9096415 0.9738605
Lag 3072 0.7920321 NaN 0.8707786 0.9606119
Lag 4096 0.7359955 NaN 0.8331404 0.9490778
Lag 5120 0.6835085 NaN 0.7943915 0.9378093

Sample statistics burn-in diagnostic (Geweke):
Chain 1

Fraction in 1st window = 0.1
Fraction in 2nd window = 0.5

edges edgecov.seating absdiff.age
0.9001 NaN 0.6687
gwdsp.OTP.fixed.0.25
8.1579

Individual P-values (lower = worse):
edges edgecov.seating absdiff.age
3.680416e-01 NaN 5.036558e-01
gwdsp.OTP.fixed.0.25
3.409359e-16
Joint P-value (lower = worse): 0.05218092 .

MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used fo
r this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF comman
d: gof(ergmFitObject, GOF=~model).
Warning messages:
1: In cor(as.matrix(x)) : the standard deviation is zero
2: In approx.hotelling.diff.test(x1, x2, var.equal = TRUE) :
Vector(s) do not vary but equal mu0; they have been ignored for the purposes of testing.
Starting GOF for the given ERGM formula.
Calculating observed network statistics.
Starting simulations.
Sim 1 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 2 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 3 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 4 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 5 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 6 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 7 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 8 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 9 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 10 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 11 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 12 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 13 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 14 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 15 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 16 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 17 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 18 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 19 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 20 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 21 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 22 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 23 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 24 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 25 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 26 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 27 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 28 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 29 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 30 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 31 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 32 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 33 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 34 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 35 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 36 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 37 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 38 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 39 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 40 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 41 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 42 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 43 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 44 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 45 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 46 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 47 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 48 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 49 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 50 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 51 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 52 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 53 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 54 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 55 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 56 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 57 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 58 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 59 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 60 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 61 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 62 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 63 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 64 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 65 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 66 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 67 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 68 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 69 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 70 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 71 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 72 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 73 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 74 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 75 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 76 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 77 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 78 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 79 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 80 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 81 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 82 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 83 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 84 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 85 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 86 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 87 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 88 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 89 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 90 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 91 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 92 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 93 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 94 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 95 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 96 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 97 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 98 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 99 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 100 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.

Starting GOF for the given ERGM formula.
Calculating observed network statistics.
Starting simulations.
Sim 1 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 2 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 3 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 4 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 5 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 6 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 7 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 8 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 9 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 10 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 11 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 12 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 13 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 14 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 15 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 16 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 17 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 18 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 19 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 20 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 21 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 22 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 23 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 24 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 25 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 26 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 27 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 28 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 29 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 30 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 31 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 32 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 33 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 34 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 35 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 36 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 37 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 38 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 39 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 40 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 41 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 42 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 43 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 44 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 45 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 46 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 47 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 48 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 49 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 50 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 51 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 52 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 53 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 54 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 55 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 56 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 57 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 58 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 59 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 60 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 61 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 62 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 63 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 64 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 65 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 66 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 67 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 68 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 69 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 70 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 71 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 72 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 73 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 74 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 75 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 76 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 77 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 78 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 79 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 80 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 81 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 82 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 83 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 84 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 85 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 86 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 87 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 88 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 89 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 90 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 91 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 92 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 93 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 94 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 95 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 96 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 97 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 98 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 99 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.
Sim 100 of 100: Starting MCMC iterations to generate 1 network
Finished simulation 1 of 1.

null device
1
(viveksckR) viveksck at john6:/u/scr/viveksck/structural_revolutions/acl_emnlp_naacl/working/scratch/stergm_graphs_Aug1_directed$ q


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