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:2.1e-284
The log-likelihood improved by 1.346.
Iteration 2 of at most 60:
Convergence test P-value:7.5e-196
The log-likelihood improved by 1.533.
Iteration 3 of at most 60:
Convergence test P-value:1.8e-50
The log-likelihood improved by 0.2279.
Iteration 4 of at most 60:
Convergence test P-value:1.9e-12
The log-likelihood improved by 0.05275.
Iteration 5 of at most 60:
Convergence test P-value:4.4e-04
The log-likelihood improved by 0.01804.
Iteration 6 of at most 60:
Convergence test P-value:2.5e-02
The log-likelihood improved by 0.0114.
Iteration 7 of at most 60:
Convergence test P-value:5.5e-02
The log-likelihood improved by 0.008795.
Iteration 8 of at most 60:
Convergence test P-value:5.5e-02
The log-likelihood improved by 0.008737.
Iteration 9 of at most 60:
Convergence test P-value:1.4e-01
The log-likelihood improved by 0.006722.
Iteration 10 of at most 60:
Convergence test P-value:1.6e-02
The log-likelihood improved by 0.01115.
Iteration 11 of at most 60:
Convergence test P-value:7.7e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.001988.
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 0.2171.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.1267.
Estimating equations are not within tolerance region.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.1360.
Estimating equations are not within tolerance region.
Iteration 4 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0708.
Convergence test p-value: 0.0298. 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 0.0676.
Convergence test p-value: 0.4769. 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 0.0621.
Convergence test p-value: 0.0764. 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 0.0068.
Convergence test p-value: 0.0055. 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:3.1e-101
The log-likelihood improved by 1.124.
Iteration 2 of at most 60:
Convergence test P-value:7.4e-43
The log-likelihood improved by 0.5062.
Iteration 3 of at most 60:
Convergence test P-value:5.6e-15
The log-likelihood improved by 0.1378.
Iteration 4 of at most 60:
Convergence test P-value:9.4e-13
The log-likelihood improved by 0.1135.
Iteration 5 of at most 60:
Convergence test P-value:2.1e-20
The log-likelihood improved by 0.1878.
Iteration 6 of at most 60:
Convergence test P-value:4.7e-04
The log-likelihood improved by 0.03043.
Iteration 7 of at most 60:
Convergence test P-value:2.7e-06
The log-likelihood improved by 0.04897.
Iteration 8 of at most 60:
Convergence test P-value:6e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.00198.
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 2.7647.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.6535.
Estimating equations are not within tolerance region.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0044.
Convergence test p-value: 0.1850. 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 0.0056.
Convergence test p-value: 0.0005. 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 1.247.
Iteration 2 of at most 60:
Convergence test P-value:5.9e-224
The log-likelihood improved by 1.521.
Iteration 3 of at most 60:
Convergence test P-value:4.8e-36
The log-likelihood improved by 0.1409.
Iteration 4 of at most 60:
Convergence test P-value:1.2e-08
The log-likelihood improved by 0.03234.
Iteration 5 of at most 60:
Convergence test P-value:7e-03
The log-likelihood improved by 0.01168.
Iteration 6 of at most 60:
Convergence test P-value:2.6e-02
The log-likelihood improved by 0.009603.
Iteration 7 of at most 60:
Convergence test P-value:4e-01
The log-likelihood improved by 0.004077.
Iteration 8 of at most 60:
Convergence test P-value:3.2e-01
The log-likelihood improved by 0.004598.
Iteration 9 of at most 60:
Convergence test P-value:3.3e-01
The log-likelihood improved by 0.004498.
Iteration 10 of at most 60:
Convergence test P-value:2.2e-01
The log-likelihood improved by 0.005505.
Iteration 11 of at most 60:
Convergence test P-value:2.5e-01
The log-likelihood improved by 0.005141.
Iteration 12 of at most 60:
Convergence test P-value:2.6e-01
The log-likelihood improved by 0.005109.
Iteration 13 of at most 60:
Convergence test P-value:4.3e-01
The log-likelihood improved by 0.003984.
Iteration 14 of at most 60:
Convergence test P-value:2.5e-01
The log-likelihood improved by 0.005204.
Iteration 15 of at most 60:
Convergence test P-value:5e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.003505.
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 1.2568.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.4256.
Estimating equations are not within tolerance region.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0864.
Convergence test p-value: 0.6704. 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 0.0417.
Convergence test p-value: 0.3623. 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 0.0194.
Convergence test p-value: 0.0039. 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 1.562.
Iteration 2 of at most 60:
Convergence test P-value:1.6e-188
The log-likelihood improved by 1.08.
Iteration 3 of at most 60:
Convergence test P-value:5.7e-43
The log-likelihood improved by 0.2095.
Iteration 4 of at most 60:
Convergence test P-value:2.1e-13
The log-likelihood improved by 0.05718.
Iteration 5 of at most 60:
Convergence test P-value:5.1e-02
The log-likelihood improved by 0.008783.
Iteration 6 of at most 60:
Convergence test P-value:8.2e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.001741.
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 3.0016.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.2088.
Estimating equations are not within tolerance region.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.1246.
Estimating equations are not within tolerance region.
Iteration 4 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0368.
Convergence test p-value: 0.9229. 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 0.2113.
Estimating equations are not within tolerance region.
Iteration 6 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0504.
Convergence test p-value: 0.1712. 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 0.0087.
Convergence test p-value: 0.1341. 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 0.0045.
Convergence test p-value: 0.0139. Not converged with 99% confidence; increasing sample size.
Iteration 9 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0273.
Convergence test p-value: 0.0071. 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 1.274.
Iteration 2 of at most 60:
Convergence test P-value:0e+00
The log-likelihood improved by 1.219.
Iteration 3 of at most 60:
Convergence test P-value:2.3e-187
The log-likelihood improved by 0.772.
Iteration 4 of at most 60:
Convergence test P-value:1.2e-32
The log-likelihood improved by 0.09311.
Iteration 5 of at most 60:
Convergence test P-value:3.2e-04
The log-likelihood improved by 0.0147.
Iteration 6 of at most 60:
Convergence test P-value:3.2e-03
The log-likelihood improved by 0.01137.
Iteration 7 of at most 60:
Convergence test P-value:7.8e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.00235.
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 
                1.18517293                -4.62840711 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.13111611                 0.07574328 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.20816570                -0.94372054 
  absdiff.sum.faction.id.4                  nodecovar 
               -1.08794271                 3.28604587 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                 595.93487                   25.13793 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 -46.42529                  286.86590 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 103.06897                  127.82375 
  absdiff.sum.faction.id.4                  nodecovar 
                  35.16475                  281.65913 
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 1.8431.
Estimating equations are not within tolerance region.

Iteration 2 of at most 50 with parameter:
                       sum                    nonzero 
                1.43745096                -5.83668540 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.03365733                 0.05977046 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.20776615                -0.89746174 
  absdiff.sum.faction.id.4                  nodecovar 
               -1.07048758                 2.52713407 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                 573.56190                   24.81905 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 -44.46984                  282.19048 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  99.27937                  120.19683 
  absdiff.sum.faction.id.4                  nodecovar 
                  30.37778                  264.42474 
Starting MCMLE Optimization...
Optimizing with step length 0.0779.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.8059.
Distance from origin on tolerance region scale: 9227.99389791508 (previously 10210.4401378589).
Estimating equations are not within tolerance region.

Iteration 3 of at most 50 with parameter:
                       sum                    nonzero 
               1.505564778               -6.390154395 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               0.146239659               -0.003285799 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
              -0.230231652               -0.873746195 
  absdiff.sum.faction.id.4                  nodecovar 
              -0.996014380                2.245404409 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                379.611111                  -1.023810 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -41.448413                 248.392857 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 78.829365                 101.642857 
  absdiff.sum.faction.id.4                  nodecovar 
                 -9.349206                 200.964826 
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 1.7387.
Distance from origin on tolerance region scale: 6755.74320031663 (previously 14758.0155907514).
Estimating equations are not within tolerance region.

Iteration 4 of at most 50 with parameter:
                       sum                    nonzero 
                1.56701278                -6.78843914 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.17407637                -0.04766935 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.24577604                -0.86476706 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.20005525                 1.98482955 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 2048.
Average estimating function values:
                       sum                    nonzero 
                 651.37698                   20.75397 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 594.00794                  149.05952 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  80.71032                   73.78571 
  absdiff.sum.faction.id.4                  nodecovar 
                 210.81746                  291.82881 
Starting MCMLE Optimization...
Optimizing with step length 0.0705.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.8202.
Distance from origin on tolerance region scale: 7300.97846510375 (previously 5918.12442016378).
Estimating equations are not within tolerance region.

Iteration 5 of at most 50 with parameter:
                       sum                    nonzero 
                1.66278028                -7.25080656 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.10561842                -0.09769956 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.25367169                -0.78896749 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.23207626                 1.79799359 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                457.386473                  -3.342995 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                574.603865                  31.400966 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 49.285024                  46.671498 
  absdiff.sum.faction.id.4                  nodecovar 
                204.357488                 236.575684 
Starting MCMLE Optimization...
Optimizing with step length 0.0703.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.7262.
Distance from origin on tolerance region scale: 6957.65884803457 (previously 25657.8752284055).
Estimating equations are not within tolerance region.

Iteration 6 of at most 50 with parameter:
                       sum                    nonzero 
                1.75501078                -7.55421274 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.03730018                -0.08531393 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.27906030                -0.74771889 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.26957554                 1.78274974 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                425.854806                  -1.496933 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                525.842536                  33.525562 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 43.940695                  44.860941 
  absdiff.sum.faction.id.4                  nodecovar 
                185.476483                 215.569656 
Starting MCMLE Optimization...
Optimizing with step length 0.0981.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.2436.
Distance from origin on tolerance region scale: 4655.57819149097 (previously 5566.58787395608).
Estimating equations are not within tolerance region.

Iteration 7 of at most 50 with parameter:
                       sum                    nonzero 
                1.87320534                -7.95248407 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.32837508                -0.07635337 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.26037911                -0.75786762 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.28779504                 2.05686103 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 2048.
Average estimating function values:
                       sum                    nonzero 
                449.063492                   5.186508 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                457.992063                  36.559524 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 46.424603                  43.476190 
  absdiff.sum.faction.id.4                  nodecovar 
                186.242063                 211.624357 
Starting MCMLE Optimization...
Optimizing with step length 0.0902.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.5616.
Distance from origin on tolerance region scale: 3825.71357933017 (previously 4670.57756242207).
Estimating equations are not within tolerance region.

Iteration 8 of at most 50 with parameter:
                       sum                    nonzero 
                1.87980025                -8.07136933 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.39174972                -0.05947165 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.29100178                -0.73188171 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.32364078                 2.04927771 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                465.875519                  -2.273859 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                177.622407                  45.390041 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 46.336100                  50.257261 
  absdiff.sum.faction.id.4                  nodecovar 
                183.887967                 227.460526 
Starting MCMLE Optimization...
Optimizing with step length 0.0884.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.9534.
Distance from origin on tolerance region scale: 4978.39767814479 (previously 6668.24514651136).
Estimating equations are not within tolerance region.

Iteration 9 of at most 50 with parameter:
                       sum                    nonzero 
                1.85093088                -8.05616200 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.33941774                -0.04849571 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.31093547                -0.66443113 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.34503321                 1.87766457 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 2048.
Average estimating function values:
                       sum                    nonzero 
                510.334091                  -4.056818 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -54.725000                  41.929545 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 51.461364                  67.359091 
  absdiff.sum.faction.id.4                  nodecovar 
                184.752273                 256.510482 
Starting MCMLE Optimization...
Optimizing with step length 0.0880.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 3.1546.
Distance from origin on tolerance region scale: 8126.63461873045 (previously 15550.4168153152).
Estimating equations are not within tolerance region.

Iteration 10 of at most 50 with parameter:
                       sum                    nonzero 
                1.92330855                -8.77495146 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.07392322                -0.04470298 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.29435055                -0.60050332 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.35727846                 1.48756026 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                 482.62802                   -2.73430 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 -47.81643                   44.84541 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  47.71498                   61.29952 
  absdiff.sum.faction.id.4                  nodecovar 
                 169.44444                  234.32361 
Starting MCMLE Optimization...
Optimizing with step length 0.0735.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.7989.
Distance from origin on tolerance region scale: 6629.57309816911 (previously 8021.95247634347).
Estimating equations are not within tolerance region.

Iteration 11 of at most 50 with parameter:
                       sum                    nonzero 
               1.906724988               -8.828710082 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               0.073276078               -0.006508107 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
              -0.295044273               -0.530314271 
  absdiff.sum.faction.id.4                  nodecovar 
              -0.385695574                1.337220377 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 4096.
Average estimating function values:
                       sum                    nonzero 
                425.488263                  -2.906103 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -34.507042                  45.509390 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 41.530516                  53.107981 
  absdiff.sum.faction.id.4                  nodecovar 
                143.852113                 199.819125 
Starting MCMLE Optimization...
Optimizing with step length 0.1271.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.3833.
Distance from origin on tolerance region scale: 2944.5604072074 (previously 3766.94402144642).
Estimating equations are not within tolerance region.

Iteration 12 of at most 50 with parameter:
                       sum                    nonzero 
                1.80580092                -8.28647531 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.09742585                 0.01820818 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.30253466                -0.53457002 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.38697947                 1.37445520 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 2048.
Average estimating function values:
                       sum                    nonzero 
                 361.25926                   -3.75000 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 -35.69907                   41.84722 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  36.09722                   40.65278 
  absdiff.sum.faction.id.4                  nodecovar 
                 124.27315                  169.51575 
Starting MCMLE Optimization...
Optimizing with step length 0.1127.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.6001.
Distance from origin on tolerance region scale: 2507.5420456207 (previously 3261.34223524404).
Estimating equations are not within tolerance region.

Iteration 13 of at most 50 with parameter:
                       sum                    nonzero 
                1.71311196                -7.74877528 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.13755325                 0.04471018 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.31222328                -0.53472268 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.40841631                 1.43610922 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                388.911616                  -1.762626 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                243.883838                 138.348485 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 51.527778                  76.015152 
  absdiff.sum.faction.id.4                  nodecovar 
                 72.853535                 189.396164 
Starting MCMLE Optimization...
Optimizing with step length 0.1239.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.9668.
Distance from origin on tolerance region scale: 2555.82872043944 (previously 6567.57210933704).
Estimating equations are not within tolerance region.

Iteration 14 of at most 50 with parameter:
                       sum                    nonzero 
                1.68579667                -7.35727943 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.11527457                 0.01066355 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.35008399                -0.53553106 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.45244379                 1.40933519 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
               344.5775281                  0.6404494 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               211.6674157                130.0988764 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                43.1865169                 59.3887640 
  absdiff.sum.faction.id.4                  nodecovar 
                61.5348315                156.0961979 
Starting MCMLE Optimization...
Optimizing with step length 0.1583.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.3738.
Distance from origin on tolerance region scale: 1890.15684361375 (previously 2591.01516560244).
Estimating equations are not within tolerance region.

Iteration 15 of at most 50 with parameter:
                       sum                    nonzero 
                1.63189358                -6.93695495 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.09885167                -0.02490172 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.38908588                -0.57160401 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.47856223                 1.42119867 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 4096.
Average estimating function values:
                       sum                    nonzero 
                332.903846                   2.538462 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                405.819231                  38.303846 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 31.376923                  40.234615 
  absdiff.sum.faction.id.4                  nodecovar 
                125.284615                 160.532089 
Starting MCMLE Optimization...
Optimizing with step length 0.0886.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.3548.
Distance from origin on tolerance region scale: 3439.2472480441 (previously 10107.5272988973).
Estimating equations are not within tolerance region.

Iteration 16 of at most 50 with parameter:
                       sum                    nonzero 
                1.71238623                -7.13352224 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.16479405                -0.04623711 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.40291702                -0.59619615 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.50927017                 1.77126918 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 8192.
Average estimating function values:
                       sum                    nonzero 
               388.5912698                  0.6587302 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -57.3690476                135.5396825 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                53.4444444                 88.2460317 
  absdiff.sum.faction.id.4                  nodecovar 
                61.6507937                196.9782710 
Starting MCMLE Optimization...
Optimizing with step length 0.0736.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.5556.
Distance from origin on tolerance region scale: 5720.89146246263 (previously 40114.6302733975).
Estimating equations are not within tolerance region.

Iteration 17 of at most 50 with parameter:
                       sum                    nonzero 
               1.721841039               -7.322984282 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
              -0.005122897               -0.054391691 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
              -0.423831779               -0.579906371 
  absdiff.sum.faction.id.4                  nodecovar 
              -0.462284560                1.574432257 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 4096.
Average estimating function values:
                       sum                    nonzero 
               359.5518672                  0.8838174 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -49.1991701                125.3319502 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                46.4315353                 79.2780083 
  absdiff.sum.faction.id.4                  nodecovar 
                59.1410788                176.7716043 
Starting MCMLE Optimization...
Optimizing with step length 0.1271.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.0773.
Distance from origin on tolerance region scale: 2560.56724349429 (previously 3138.39110554276).
Estimating equations are not within tolerance region.

Iteration 18 of at most 50 with parameter:
                       sum                    nonzero 
                1.67731800                -7.14608697 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.07151099                -0.06108692 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.41983758                -0.55513598 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.45414951                 1.49041097 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 16384.
Average estimating function values:
                       sum                    nonzero 
                 211.82625                  -14.89961 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 257.37066                   17.23552 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  19.03089                   30.32819 
  absdiff.sum.faction.id.4                  nodecovar 
                  83.11583                  113.86331 
Starting MCMLE Optimization...
Optimizing with step length 0.1283.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.4364.
Distance from origin on tolerance region scale: 1738.9235260323 (previously 5294.84156809408).
Estimating equations are not within tolerance region.

Iteration 19 of at most 50 with parameter:
                       sum                    nonzero 
                1.58882402                -6.56511854 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.07880627                -0.06339653 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.43894001                -0.61123946 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.48906875                 1.56187689 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 8192.
Average estimating function values:
                       sum                    nonzero 
                323.342995                   4.154589 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                398.062802                  33.159420 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 29.207729                  39.811594 
  absdiff.sum.faction.id.4                  nodecovar 
                126.768116                 159.491603 
Starting MCMLE Optimization...
Optimizing with step length 0.1130.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.9413.
Distance from origin on tolerance region scale: 3023.52104344259 (previously 1674.37957592683).
Estimating equations are not within tolerance region.

Iteration 20 of at most 50 with parameter:
                       sum                    nonzero 
                1.66037971                -6.67276776 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.16772336                -0.08574011 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.47483370                -0.67064804 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.52279423                 1.87241710 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 16384.
Average estimating function values:
                       sum                    nonzero 
                371.924603                   2.583333 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -56.317460                  27.341270 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 33.908730                  51.746032 
  absdiff.sum.faction.id.4                  nodecovar 
                130.250000                 187.818894 
Starting MCMLE Optimization...
Optimizing with step length 0.0937.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.2803.
Distance from origin on tolerance region scale: 5177.61455189374 (previously 34992.6510671288).
Estimating equations are not within tolerance region.

Iteration 21 of at most 50 with parameter:
                       sum                    nonzero 
               1.668056591               -6.830320517 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               0.007988225               -0.070955788 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
              -0.477859494               -0.642198986 
  absdiff.sum.faction.id.4                  nodecovar 
              -0.535745483                1.652368202 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 8192.
Average estimating function values:
                       sum                    nonzero 
                333.809129                   4.004149 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -47.128631                  26.688797 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 28.294606                  44.614108 
  absdiff.sum.faction.id.4                  nodecovar 
                112.701245                 160.664011 
Starting MCMLE Optimization...
Optimizing with step length 0.1479.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.2078.
Distance from origin on tolerance region scale: 2011.28300856509 (previously 2631.79750083008).
Estimating equations are not within tolerance region.

Iteration 22 of at most 50 with parameter:
                       sum                    nonzero 
                1.59087985                -6.61214365 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.09820149                -0.05965515 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.49240639                -0.59588810 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.54206599                 1.64012649 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 16384.
Average estimating function values:
                       sum                    nonzero 
                335.365909                   2.411364 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                420.934091                  32.875000 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 29.052273                  45.402273 
  absdiff.sum.faction.id.4                  nodecovar 
                129.279545                 170.933213 
Starting MCMLE Optimization...
Optimizing with step length 0.1025.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.8513.
Distance from origin on tolerance region scale: 3516.29069854559 (previously 95850.9772205403).
Estimating equations are not within tolerance region.

Iteration 23 of at most 50 with parameter:
                       sum                    nonzero 
                1.69342040                -6.91910761 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.15836521                -0.07449628 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.51195602                -0.61631279 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.58154984                 1.90522314 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 32768.
Average estimating function values:
                       sum                    nonzero 
                383.095238                   0.515873 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -56.361111                 124.277778 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 41.087302                  98.166667 
  absdiff.sum.faction.id.4                  nodecovar 
                 73.480159                 196.339477 
Starting MCMLE Optimization...
Optimizing with step length 0.0912.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.5801.
Distance from origin on tolerance region scale: 6181.15993736098 (previously 45925.7795867525).
Estimating equations are not within tolerance region.

Iteration 24 of at most 50 with parameter:
                       sum                    nonzero 
                1.72168539                -7.29101499 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.03185234                -0.07021139 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.48271364                -0.62898322 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.58274158                 1.61538214 
Starting unconstrained MCMC...