Error(s) in re-building vignettes:
  ...
--- re-building 'valued.Rmd' using rmarkdown
Loading required package: ergm
Loading required package: network

'network' 1.17.1 (2021-06-12), part of the Statnet Project
* 'news(package="network")' for changes since last version
* 'citation("network")' for citation information
* 'https://statnet.org' for help, support, and other information


'ergm' 4.1.2 (2021-07-26), part of the Statnet Project
* 'news(package="ergm")' for changes since last version
* 'citation("ergm")' for citation information
* 'https://statnet.org' for help, support, and other information

'ergm' 4 is a major update that introduces some backwards-incompatible
changes. Please type 'news(package="ergm")' for a list of major
changes.


'ergm.count' 4.0.2 (2021-06-18), part of the Statnet Project
* 'news(package="ergm.count")' for changes since last version
* 'citation("ergm.count")' for citation information
* 'https://statnet.org' for help, support, and other information

Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse'.
Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse'.
Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse'.
Best valid proposal 'Binomial' cannot take into account hint(s) 'sparse'.
Best valid proposal 'Binomial' cannot take into account hint(s) 'sparse'.
Best valid proposal 'Binomial' cannot take into account hint(s) 'sparse'.
Best valid proposal 'Geometric' cannot take into account hint(s) 'sparse'.
Best valid proposal 'Geometric' cannot take into account hint(s) 'sparse'.
Best valid proposal 'Binomial' cannot take into account hint(s) 'sparse'.
Starting contrastive divergence estimation via CD-MCMLE:
Iteration 1 of at most 60:
Convergence test P-value:5.1e-284
The log-likelihood improved by 2.001.
Iteration 2 of at most 60:
Convergence test P-value:4.3e-156
The log-likelihood improved by 1.112.
Iteration 3 of at most 60:
Convergence test P-value:3.3e-28
The log-likelihood improved by 0.123.
Iteration 4 of at most 60:
Convergence test P-value:1.1e-08
The log-likelihood improved by 0.03652.
Iteration 5 of at most 60:
Convergence test P-value:1.5e-01
The log-likelihood improved by 0.006324.
Iteration 6 of at most 60:
Convergence test P-value:4.2e-02
The log-likelihood improved by 0.009149.
Iteration 7 of at most 60:
Convergence test P-value:8.1e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.001764.
Finished CD.
Starting Monte Carlo maximum likelihood estimation (MCMLE):
Iteration 1 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.6088.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0397.
Convergence test p-value: 0.1845. Not converged with 99% confidence; increasing sample size.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0214.
Convergence test p-value: 0.0327. Not converged with 99% confidence; increasing sample size.
Iteration 4 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0043.
Convergence test p-value: 0.0098. Converged with 99% confidence.
Finished MCMLE.
Evaluating log-likelihood at the estimate. Setting up bridge sampling...
Best valid proposal 'Binomial' cannot take into account hint(s) 'sparse'.
Using 16 bridges: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 .
Note: Null model likelihood calculation is not implemented for valued
ERGMs at this time.  This means that all likelihood-based inference
(LRT, Analysis of Deviance, AIC, BIC, etc.) is only valid between
models with the same reference distribution and constraints.
This model was fit using MCMC.  To examine model diagnostics and check
for degeneracy, use the mcmc.diagnostics() function.
Starting contrastive divergence estimation via CD-MCMLE:
Iteration 1 of at most 60:
Convergence test P-value:5.7e-103
The log-likelihood improved by 1.728.
Iteration 2 of at most 60:
Convergence test P-value:1.5e-47
The log-likelihood improved by 0.5326.
Iteration 3 of at most 60:
Convergence test P-value:4.3e-22
The log-likelihood improved by 0.2203.
Iteration 4 of at most 60:
Convergence test P-value:9.3e-16
The log-likelihood improved by 0.1453.
Iteration 5 of at most 60:
Convergence test P-value:8.9e-08
The log-likelihood improved by 0.06498.
Iteration 6 of at most 60:
Convergence test P-value:2.4e-04
The log-likelihood improved by 0.03186.
Iteration 7 of at most 60:
Convergence test P-value:9.7e-03
The log-likelihood improved by 0.01804.
Iteration 8 of at most 60:
Convergence test P-value:1.6e-03
The log-likelihood improved by 0.02509.
Iteration 9 of at most 60:
Convergence test P-value:1.7e-02
The log-likelihood improved by 0.01585.
Iteration 10 of at most 60:
Convergence test P-value:4.1e-01
The log-likelihood improved by 0.00345.
Iteration 11 of at most 60:
Convergence test P-value:2.6e-02
The log-likelihood improved by 0.01443.
Iteration 12 of at most 60:
Convergence test P-value:8.2e-03
The log-likelihood improved by 0.01849.
Iteration 13 of at most 60:
Convergence test P-value:7.8e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.001006.
Finished CD.
Starting Monte Carlo maximum likelihood estimation (MCMLE):
Iteration 1 of at most 60:
Optimizing with step length 0.5407.
The log-likelihood improved by 3.2326.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 2.3468.
Estimating equations are not within tolerance region.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0038.
Convergence test p-value: 0.0001. Converged with 99% confidence.
Finished MCMLE.
Evaluating log-likelihood at the estimate. Setting up bridge sampling...
Using 16 bridges: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 .
Note: Null model likelihood calculation is not implemented for valued
ERGMs at this time.  This means that all likelihood-based inference
(LRT, Analysis of Deviance, AIC, BIC, etc.) is only valid between
models with the same reference distribution and constraints.
This model was fit using MCMC.  To examine model diagnostics and check
for degeneracy, use the mcmc.diagnostics() function.
Best valid proposal 'Binomial' cannot take into account hint(s) 'sparse'.
Starting contrastive divergence estimation via CD-MCMLE:
Iteration 1 of at most 60:
Convergence test P-value:0e+00
The log-likelihood improved by 1.454.
Iteration 2 of at most 60:
Convergence test P-value:2e-185
The log-likelihood improved by 1.153.
Iteration 3 of at most 60:
Convergence test P-value:1.2e-22
The log-likelihood improved by 0.08417.
Iteration 4 of at most 60:
Convergence test P-value:2.4e-03
The log-likelihood improved by 0.0135.
Iteration 5 of at most 60:
Convergence test P-value:5.7e-04
The log-likelihood improved by 0.01593.
Iteration 6 of at most 60:
Convergence test P-value:8.7e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.00163.
Finished CD.
Starting Monte Carlo maximum likelihood estimation (MCMLE):
Iteration 1 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 1.6295.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.2131.
Estimating equations are not within tolerance region.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.2099.
Estimating equations are not within tolerance region.
Iteration 4 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.1440.
Estimating equations are not within tolerance region.
Iteration 5 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0646.
Convergence test p-value: 0.7557. Not converged with 99% confidence; increasing sample size.
Iteration 6 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.1043.
Estimating equations are not within tolerance region.
Estimating equations did not move closer to tolerance region more than 1 time(s) in 4 steps; increasing sample size.
Iteration 7 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0309.
Convergence test p-value: 0.0149. Not converged with 99% confidence; increasing sample size.
Iteration 8 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0095.
Convergence test p-value: 0.0021. Converged with 99% confidence.
Finished MCMLE.
Evaluating log-likelihood at the estimate. Setting up bridge sampling...
Best valid proposal 'Binomial' cannot take into account hint(s) 'sparse'.
Using 16 bridges: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 .
Note: Null model likelihood calculation is not implemented for valued
ERGMs at this time.  This means that all likelihood-based inference
(LRT, Analysis of Deviance, AIC, BIC, etc.) is only valid between
models with the same reference distribution and constraints.
This model was fit using MCMC.  To examine model diagnostics and check
for degeneracy, use the mcmc.diagnostics() function.
Best valid proposal 'Binomial' cannot take into account hint(s) 'sparse'.
Starting contrastive divergence estimation via CD-MCMLE:
Iteration 1 of at most 60:
Convergence test P-value:0e+00
The log-likelihood improved by 1.126.
Iteration 2 of at most 60:
Convergence test P-value:9e-221
The log-likelihood improved by 1.531.
Iteration 3 of at most 60:
Convergence test P-value:4e-49
The log-likelihood improved by 0.2457.
Iteration 4 of at most 60:
Convergence test P-value:1.4e-04
The log-likelihood improved by 0.02024.
Iteration 5 of at most 60:
Convergence test P-value:4.9e-02
The log-likelihood improved by 0.008923.
Iteration 6 of at most 60:
Convergence test P-value:2.8e-01
The log-likelihood improved by 0.005004.
Iteration 7 of at most 60:
Convergence test P-value:7.3e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.002185.
Finished CD.
Starting Monte Carlo maximum likelihood estimation (MCMLE):
Iteration 1 of at most 60:
Optimizing with step length 0.7979.
The log-likelihood improved by 2.1714.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.1799.
Estimating equations are not within tolerance region.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0094.
Convergence test p-value: 0.7515. Not converged with 99% confidence; increasing sample size.
Iteration 4 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0565.
Convergence test p-value: 0.7302. Not converged with 99% confidence; increasing sample size.
Iteration 5 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0473.
Convergence test p-value: 0.1250. Not converged with 99% confidence; increasing sample size.
Iteration 6 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0480.
Convergence test p-value: 0.0307. Not converged with 99% confidence; increasing sample size.
Iteration 7 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0180.
Convergence test p-value: 0.0040. Converged with 99% confidence.
Finished MCMLE.
Evaluating log-likelihood at the estimate. Setting up bridge sampling...
Best valid proposal 'Binomial' cannot take into account hint(s) 'sparse'.
Using 16 bridges: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 .
Note: Null model likelihood calculation is not implemented for valued
ERGMs at this time.  This means that all likelihood-based inference
(LRT, Analysis of Deviance, AIC, BIC, etc.) is only valid between
models with the same reference distribution and constraints.
This model was fit using MCMC.  To examine model diagnostics and check
for degeneracy, use the mcmc.diagnostics() function.
Evaluating network in model.
Initializing unconstrained Metropolis-Hastings proposal: 'ergm.count:MH_PoissonTNT'.
Initializing model...
Model initialized.
Using initial method 'CD'.
Fitting initial model.
Starting contrastive divergence estimation via CD-MCMLE:
Iteration 1 of at most 60:
Convergence test P-value:0e+00
The log-likelihood improved by 1.383.
Iteration 2 of at most 60:
Convergence test P-value:0e+00
The log-likelihood improved by 1.431.
Iteration 3 of at most 60:
Convergence test P-value:2.8e-180
The log-likelihood improved by 0.7104.
Iteration 4 of at most 60:
Convergence test P-value:8.5e-26
The log-likelihood improved by 0.07414.
Iteration 5 of at most 60:
Convergence test P-value:7.4e-05
The log-likelihood improved by 0.01627.
Iteration 6 of at most 60:
Convergence test P-value:1.1e-01
The log-likelihood improved by 0.006525.
Iteration 7 of at most 60:
Convergence test P-value:2.3e-01
The log-likelihood improved by 0.005324.
Iteration 8 of at most 60:
Convergence test P-value:8.7e-03
The log-likelihood improved by 0.01017.
Iteration 9 of at most 60:
Convergence test P-value:5.7e-02
The log-likelihood improved by 0.007716.
Iteration 10 of at most 60:
Convergence test P-value:2.4e-01
The log-likelihood improved by 0.005166.
Iteration 11 of at most 60:
Convergence test P-value:2.1e-01
The log-likelihood improved by 0.005395.
Iteration 12 of at most 60:
Convergence test P-value:1.5e-01
The log-likelihood improved by 0.005824.
Iteration 13 of at most 60:
Convergence test P-value:1.2e-01
The log-likelihood improved by 0.006431.
Iteration 14 of at most 60:
Convergence test P-value:6.4e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.003008.
Finished CD.
Starting Monte Carlo maximum likelihood estimation (MCMLE):
Density guard set to 10000 from an initial count of 78 edges.

Iteration 1 of at most 50 with parameter:
                       sum                    nonzero 
                1.14757556                -4.54002413 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.02256609                 0.06934813 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.31689416                -1.06701157 
  absdiff.sum.faction.id.4                  nodecovar 
               -1.21963250                 3.16504720 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                521.159420                  26.004831 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -46.845411                 317.628019 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 86.574879                 146.033816 
  absdiff.sum.faction.id.4                  nodecovar 
                 -9.507246                 248.987576 
Starting MCMLE Optimization...
Optimizing with step length 0.0383.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.7624.
Estimating equations are not within tolerance region.

Iteration 2 of at most 50 with parameter:
                       sum                    nonzero 
                1.34681434                -5.48331623 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.09059381                 0.02167195 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.32433100                -1.07020557 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.56070256                 2.58833824 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 256.
Average estimating function values:
                       sum                    nonzero 
                489.315068                  25.558904 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -44.487671                 301.312329 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 82.347945                 133.920548 
  absdiff.sum.faction.id.4                  nodecovar 
                 -8.284932                 229.931123 
Starting MCMLE Optimization...
Optimizing with step length 0.0649.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.9112.
Distance from origin on tolerance region scale: 9062.8346363364 (previously 10333.3386740638).
Estimating equations are not within tolerance region.

Iteration 3 of at most 50 with parameter:
                       sum                    nonzero 
                1.43771346                -5.82475754 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.24262250                -0.06581077 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.41729149                -1.09081280 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.46961916                 2.36915223 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                 633.22727                   23.47727 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 573.08182                  153.16136 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  73.91591                   75.56591 
  absdiff.sum.faction.id.4                  nodecovar 
                 182.14773                  288.08916 
Starting MCMLE Optimization...
Optimizing with step length 0.0693.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.8659.
Distance from origin on tolerance region scale: 7757.21280174457 (previously 7063.19528803493).
Estimating equations are not within tolerance region.

Iteration 4 of at most 50 with parameter:
                       sum                    nonzero 
                 1.5343320                 -6.4043232 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 0.1745123                 -0.0870523 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                -0.4300502                 -0.9986735 
  absdiff.sum.faction.id.4                  nodecovar 
                -0.4675286                  2.1416028 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                 581.84259                   21.23765 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 524.96914                  143.89815 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  67.48148                   70.41975 
  absdiff.sum.faction.id.4                  nodecovar 
                 168.37346                  266.28517 
Starting MCMLE Optimization...
Optimizing with step length 0.0562.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.2744.
Distance from origin on tolerance region scale: 8049.24321748246 (previously 9503.37183970382).
Estimating equations are not within tolerance region.

Iteration 5 of at most 50 with parameter:
                       sum                    nonzero 
                 1.6085812                 -6.8444890 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 0.1140250                 -0.1358107 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                -0.4472581                 -0.9009664 
  absdiff.sum.faction.id.4                  nodecovar 
                -0.4916166                  1.9834600 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                392.944444                  -4.452381 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                516.166667                  27.011905 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 38.317460                  43.384921 
  absdiff.sum.faction.id.4                  nodecovar 
                164.392857                 213.799477 
Starting MCMLE Optimization...
Optimizing with step length 0.0813.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.9103.
Distance from origin on tolerance region scale: 5758.42361323797 (previously 23012.4750823145).
Estimating equations are not within tolerance region.

Iteration 6 of at most 50 with parameter:
                       sum                    nonzero 
                1.76127416                -7.50168807 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.06084443                -0.11936371 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.46319424                -0.90553456 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.49770339                 1.89788183 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                364.816393                  -3.668852 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                469.209836                  28.078689 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 34.055738                  36.288525 
  absdiff.sum.faction.id.4                  nodecovar 
                151.321311                 192.085789 
Starting MCMLE Optimization...
Optimizing with step length 0.1007.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.0120.
Distance from origin on tolerance region scale: 3954.60704358574 (previously 4760.06349209778).
Estimating equations are not within tolerance region.

Iteration 7 of at most 50 with parameter:
                       sum                    nonzero 
                 1.8653686                 -7.7669190 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -0.3388983                 -0.1459032 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                -0.5051400                 -0.8829183 
  absdiff.sum.faction.id.4                  nodecovar 
                -0.5188820                  2.1900313 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                336.466049                  -2.274691 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                426.956790                  17.833333 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 29.753086                  35.188272 
  absdiff.sum.faction.id.4                  nodecovar 
                140.466049                 175.760734 
Starting MCMLE Optimization...
Optimizing with step length 0.1056.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.1527.
Distance from origin on tolerance region scale: 3850.29914671729 (previously 4623.56751579031).
Estimating equations are not within tolerance region.

Iteration 8 of at most 50 with parameter:
                       sum                    nonzero 
                1.92683426                -7.92485322 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.59255363                -0.08492026 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.46717107                -0.85929250 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.54576801                 2.45863178 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                 962.34773                   20.72955 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 -38.98636                  104.97955 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  87.55455                  116.00227 
  absdiff.sum.faction.id.4                  nodecovar 
                 291.38636                  425.06811 
Starting MCMLE Optimization...
Optimizing with step length 0.0391.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.5770.
Distance from origin on tolerance region scale: 33710.4490246565 (previously 52900.2504868789).
Estimating equations are not within tolerance region.

Iteration 9 of at most 50 with parameter:
                       sum                    nonzero 
                2.17421495               -10.17109988 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.40887326                -0.08741896 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.43771947                -0.77404037 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.51035544                 1.66151655 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                 933.49885                   20.81755 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 -36.00000                  102.86605 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  85.03695                  110.54503 
  absdiff.sum.faction.id.4                  nodecovar 
                 281.80370                  403.65381 
Starting MCMLE Optimization...
Optimizing with step length 0.0780.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 3.1036.
Distance from origin on tolerance region scale: 10177.410440676 (previously 11381.2102337954).
Estimating equations are not within tolerance region.

Iteration 10 of at most 50 with parameter:
                       sum                    nonzero 
                 2.1862675                -10.4576029 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -0.2366519                 -0.0667600 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                -0.3737137                 -0.6826825 
  absdiff.sum.faction.id.4                  nodecovar 
                -0.5148818                  1.4260390 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                 852.00549                   19.59615 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 -37.73077                  101.48077 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  81.28846                   92.17033 
  absdiff.sum.faction.id.4                  nodecovar 
                 246.52473                  350.37316 
Starting MCMLE Optimization...
Optimizing with step length 0.0716.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.6966.
Distance from origin on tolerance region scale: 6595.59423028242 (previously 8021.08916646425).
Estimating equations are not within tolerance region.

Iteration 11 of at most 50 with parameter:
                       sum                    nonzero 
                2.15742949               -10.48290163 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.12967638                -0.06646713 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.36010755                -0.61477014 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.51442340                 1.35246212 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 4096.
Average estimating function values:
                       sum                    nonzero 
                549.742604                  -6.470414 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -46.636095                  51.100592 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 52.479290                  71.408284 
  absdiff.sum.faction.id.4                  nodecovar 
                170.840237                 263.974424 
Starting MCMLE Optimization...
Optimizing with step length 0.0844.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.9243.
Distance from origin on tolerance region scale: 5384.13238783937 (previously 7939.76908255329).
Estimating equations are not within tolerance region.

Iteration 12 of at most 50 with parameter:
                       sum                    nonzero 
                2.09145129                -9.93039321 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.03142636                -0.06650590 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.36626784                -0.61689381 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.53257023                 1.36161694 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 2048.
Average estimating function values:
                       sum                    nonzero 
                494.338164                  -6.570048 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -45.309179                  44.980676 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 47.198068                  61.545894 
  absdiff.sum.faction.id.4                  nodecovar 
                152.642512                 235.594748 
Starting MCMLE Optimization...
Optimizing with step length 0.0728.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.1836.
Distance from origin on tolerance region scale: 4443.33772035737 (previously 5272.72374701386).
Estimating equations are not within tolerance region.

Iteration 13 of at most 50 with parameter:
                       sum                    nonzero 
                2.02571025                -9.45016396 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.03271826                -0.05932386 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.38193436                -0.60197374 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.56170635                 1.40404017 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                456.610315                  -5.621777 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -35.088825                  43.249284 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 42.939828                  57.805158 
  absdiff.sum.faction.id.4                  nodecovar 
                131.905444                 212.702325 
Starting MCMLE Optimization...
Optimizing with step length 0.0987.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.9845.
Distance from origin on tolerance region scale: 4064.79388309244 (previously 4952.58613488379).
Estimating equations are not within tolerance region.

Iteration 14 of at most 50 with parameter:
                       sum                    nonzero 
                1.93464053                -8.74069515 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.10537371                -0.07003491 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.39003199                -0.57699127 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.57883999                 1.44881609 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 2048.
Average estimating function values:
                       sum                    nonzero 
                464.456818                  -3.056818 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                283.822727                  41.725000 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 43.475000                  63.822727 
  absdiff.sum.faction.id.4                  nodecovar 
                138.825000                 220.361898 
Starting MCMLE Optimization...
Optimizing with step length 0.1198.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.0748.
Distance from origin on tolerance region scale: 2883.82880307752 (previously 5696.563373274).
Estimating equations are not within tolerance region.

Iteration 15 of at most 50 with parameter:
                       sum                    nonzero 
                1.87518335                -8.32375781 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.09498195                -0.07615420 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.39582574                -0.56654231 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.58367287                 1.43846114 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                414.672165                  -2.847423 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                249.573196                  32.964948 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 38.764948                  59.868041 
  absdiff.sum.faction.id.4                  nodecovar 
                126.348454                 200.655604 
Starting MCMLE Optimization...
Optimizing with step length 0.1568.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 3.2153.
Distance from origin on tolerance region scale: 2608.68735455347 (previously 3258.92852265549).
Estimating equations are not within tolerance region.

Iteration 16 of at most 50 with parameter:
                       sum                    nonzero 
                1.77488196                -7.78762536 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.08710843                -0.04411953 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.39648873                -0.56767866 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.59332614                 1.46009494 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                 181.25926                  -23.81481 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 225.78788                   14.48485 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  17.45791                   27.03367 
  absdiff.sum.faction.id.4                  nodecovar 
                  63.53535                  103.49873 
Starting MCMLE Optimization...
Optimizing with step length 0.1049.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.7832.
Distance from origin on tolerance region scale: 3227.26906855036 (previously 9579.99274657682).
Estimating equations are not within tolerance region.

Iteration 17 of at most 50 with parameter:
                       sum                    nonzero 
                1.66249017                -7.04439811 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.10217278                -0.06877268 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.41513175                -0.54217634 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.61958629                 1.48278696 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 262144.
Average estimating function values:
                       sum                    nonzero 
                291.926641                  -4.104247 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                366.664093                  27.938224 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 28.181467                  41.324324 
  absdiff.sum.faction.id.4                  nodecovar 
                101.413127                 146.530061 
Starting MCMLE Optimization...
Optimizing with step length 0.1822.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.1452.
Distance from origin on tolerance region scale: 1287.88504271744 (previously 1758.86296646802).
Estimating equations are not within tolerance region.

Iteration 18 of at most 50 with parameter:
                       sum                    nonzero 
                1.56381331                -6.42136263 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.10341604                -0.07258483 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.43872666                -0.55666662 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.63375292                 1.55214143 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 262144.
Average estimating function values:
                       sum                    nonzero 
                 285.50133                    2.35200 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 355.91467                   28.37333 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  27.36267                   40.57333 
  absdiff.sum.faction.id.4                  nodecovar 
                 100.07200                  140.74773 
Starting MCMLE Optimization...
Optimizing with step length 0.2152.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.4121.
Distance from origin on tolerance region scale: 1039.38715545015 (previously 1403.00000659055).
Estimating equations are not within tolerance region.

Iteration 19 of at most 50 with parameter:
                       sum                    nonzero 
                1.49135894                -5.91164668 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.04436801                -0.07805467 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.44924758                -0.60235655 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.66879138                 1.72183431 
Starting unconstrained MCMC...
Killed