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:2.2e-296
The log-likelihood improved by 1.705.
Iteration 2 of at most 60:
Convergence test P-value:1.3e-150
The log-likelihood improved by 0.9516.
Iteration 3 of at most 60:
Convergence test P-value:1.3e-37
The log-likelihood improved by 0.1636.
Iteration 4 of at most 60:
Convergence test P-value:4.6e-10
The log-likelihood improved by 0.04245.
Iteration 5 of at most 60:
Convergence test P-value:2.6e-02
The log-likelihood improved by 0.01016.
Iteration 6 of at most 60:
Convergence test P-value:3.6e-01
The log-likelihood improved by 0.004287.
Iteration 7 of at most 60:
Convergence test P-value:1.1e-01
The log-likelihood improved by 0.00703.
Iteration 8 of at most 60:
Convergence test P-value:6.5e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.002588.
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 0.3363.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0224.
Convergence test p-value: 0.0322. 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 0.0552.
Convergence test p-value: 0.5196. 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 0.0508.
Convergence test p-value: 0.0005. 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:7.8e-108
The log-likelihood improved by 1.688.
Iteration 2 of at most 60:
Convergence test P-value:6e-35
The log-likelihood improved by 0.3844.
Iteration 3 of at most 60:
Convergence test P-value:8e-22
The log-likelihood improved by 0.1935.
Iteration 4 of at most 60:
Convergence test P-value:6.5e-12
The log-likelihood improved by 0.1033.
Iteration 5 of at most 60:
Convergence test P-value:7.3e-09
The log-likelihood improved by 0.07488.
Iteration 6 of at most 60:
Convergence test P-value:7e-04
The log-likelihood improved by 0.02806.
Iteration 7 of at most 60:
Convergence test P-value:5.3e-04
The log-likelihood improved by 0.03024.
Iteration 8 of at most 60:
Convergence test P-value:1.8e-01
The log-likelihood improved by 0.006593.
Iteration 9 of at most 60:
Convergence test P-value:1.3e-01
The log-likelihood improved by 0.008216.
Iteration 10 of at most 60:
Convergence test P-value:3.3e-02
The log-likelihood improved by 0.01304.
Iteration 11 of at most 60:
Convergence test P-value:6.4e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.001716.
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 2.8310.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 1.4537.
Estimating equations are not within tolerance region.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0386.
Convergence test p-value: 0.1256. 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 0.0240.
Convergence test p-value: 0.0485. 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 0.0002.
Convergence test p-value: 0.0047. 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 1.588.
Iteration 2 of at most 60:
Convergence test P-value:6.4e-218
The log-likelihood improved by 1.524.
Iteration 3 of at most 60:
Convergence test P-value:1e-40
The log-likelihood improved by 0.1551.
Iteration 4 of at most 60:
Convergence test P-value:2.3e-08
The log-likelihood improved by 0.03108.
Iteration 5 of at most 60:
Convergence test P-value:2.1e-01
The log-likelihood improved by 0.005487.
Iteration 6 of at most 60:
Convergence test P-value:1.3e-02
The log-likelihood improved by 0.01082.
Iteration 7 of at most 60:
Convergence test P-value:4.4e-01
The log-likelihood improved by 0.00386.
Iteration 8 of at most 60:
Convergence test P-value:1.6e-01
The log-likelihood improved by 0.006011.
Iteration 9 of at most 60:
Convergence test P-value:1.6e-01
The log-likelihood improved by 0.006212.
Iteration 10 of at most 60:
Convergence test P-value:3.7e-02
The log-likelihood improved by 0.008843.
Iteration 11 of at most 60:
Convergence test P-value:5.2e-02
The log-likelihood improved by 0.008267.
Iteration 12 of at most 60:
Convergence test P-value:9.5e-03
The log-likelihood improved by 0.01142.
Iteration 13 of at most 60:
Convergence test P-value:1.5e-02
The log-likelihood improved by 0.01058.
Iteration 14 of at most 60:
Convergence test P-value:2.6e-01
The log-likelihood improved by 0.00502.
Iteration 15 of at most 60:
Convergence test P-value:5e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.003545.
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 1.6512.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.1790.
Estimating equations are not within tolerance region.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.2298.
Estimating equations are not within tolerance region.
Iteration 4 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.2301.
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 5 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0824.
Convergence test p-value: 0.1132. 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 0.0112.
Convergence test p-value: 0.0509. 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 0.0375.
Convergence test p-value: 0.0001. 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 1.651.
Iteration 2 of at most 60:
Convergence test P-value:1.7e-172
The log-likelihood improved by 1.355.
Iteration 3 of at most 60:
Convergence test P-value:1.2e-43
The log-likelihood improved by 0.21.
Iteration 4 of at most 60:
Convergence test P-value:4.5e-07
The log-likelihood improved by 0.03106.
Iteration 5 of at most 60:
Convergence test P-value:7.8e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.001952.
Finished CD.
Starting Monte Carlo maximum likelihood estimation (MCMLE):
Iteration 1 of at most 60:
Optimizing with step length 0.6554.
The log-likelihood improved by 2.2259.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.3777.
Estimating equations are not within tolerance region.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0351.
Convergence test p-value: 0.5744. 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 0.1073.
Estimating equations are not within tolerance region.
Iteration 5 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0475.
Convergence test p-value: 0.5716. 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 0.0053.
Convergence test p-value: 0.1250. 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 0.0221.
Convergence test p-value: 0.0085. 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 1.502.
Iteration 2 of at most 60:
Convergence test P-value:0e+00
The log-likelihood improved by 1.261.
Iteration 3 of at most 60:
Convergence test P-value:9.8e-161
The log-likelihood improved by 0.6369.
Iteration 4 of at most 60:
Convergence test P-value:1.5e-14
The log-likelihood improved by 0.04298.
Iteration 5 of at most 60:
Convergence test P-value:8e-06
The log-likelihood improved by 0.01901.
Iteration 6 of at most 60:
Convergence test P-value:1.1e-01
The log-likelihood improved by 0.006422.
Iteration 7 of at most 60:
Convergence test P-value:1.7e-02
The log-likelihood improved by 0.009305.
Iteration 8 of at most 60:
Convergence test P-value:2.7e-02
The log-likelihood improved by 0.008695.
Iteration 9 of at most 60:
Convergence test P-value:2.5e-02
The log-likelihood improved by 0.008771.
Iteration 10 of at most 60:
Convergence test P-value:8.2e-02
The log-likelihood improved by 0.006979.
Iteration 11 of at most 60:
Convergence test P-value:2.3e-01
The log-likelihood improved by 0.005265.
Iteration 12 of at most 60:
Convergence test P-value:3.5e-02
The log-likelihood improved by 0.00832.
Iteration 13 of at most 60:
Convergence test P-value:4.2e-03
The log-likelihood improved by 0.01119.
Iteration 14 of at most 60:
Convergence test P-value:3.2e-01
The log-likelihood improved by 0.004595.
Iteration 15 of at most 60:
Convergence test P-value:9.4e-02
The log-likelihood improved by 0.006823.
Iteration 16 of at most 60:
Convergence test P-value:9.6e-02
The log-likelihood improved by 0.006702.
Iteration 17 of at most 60:
Convergence test P-value:3.4e-01
The log-likelihood improved by 0.004543.
Iteration 18 of at most 60:
Convergence test P-value:2.9e-01
The log-likelihood improved by 0.004741.
Iteration 19 of at most 60:
Convergence test P-value:6.1e-02
The log-likelihood improved by 0.007566.
Iteration 20 of at most 60:
Convergence test P-value:1.6e-01
The log-likelihood improved by 0.005807.
Iteration 21 of at most 60:
Convergence test P-value:3e-02
The log-likelihood improved by 0.008387.
Iteration 22 of at most 60:
Convergence test P-value:3.5e-01
The log-likelihood improved by 0.004398.
Iteration 23 of at most 60:
Convergence test P-value:8.9e-02
The log-likelihood improved by 0.006794.
Iteration 24 of at most 60:
Convergence test P-value:4.3e-01
The log-likelihood improved by 0.003934.
Iteration 25 of at most 60:
Convergence test P-value:5.6e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.003398.
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 
                1.22284420                -4.69051890 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.14378155                 0.07665351 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.21116593                -0.92015714 
  absdiff.sum.faction.id.4                  nodecovar 
               -1.10530063                 3.23761286 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                837.668342                  51.597990 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -36.567839                 514.959799 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                152.221106                 190.432161 
  absdiff.sum.faction.id.4                  nodecovar 
                 -9.718593                 350.072723 
Starting MCMLE Optimization...
Optimizing with step length 0.0301.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 3.3254.
Estimating equations are not within tolerance region.

Iteration 2 of at most 50 with parameter:
                       sum                    nonzero 
                1.70662908                -6.75418891 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.06268379                -0.18203553 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.42277441                -1.06415731 
  absdiff.sum.faction.id.4                  nodecovar 
                0.30382675                 2.35156213 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                1504.85847                   83.56613 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                  34.52900                  121.12065 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 108.67285                   91.54524 
  absdiff.sum.faction.id.4                  nodecovar 
                 718.62413                  505.39029 
Starting MCMLE Optimization...
Optimizing with step length 0.0572.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.5913.
Distance from origin on tolerance region scale: 15798.6521481114 (previously 19421.6723587942).
Estimating equations are not within tolerance region.

Iteration 3 of at most 50 with parameter:
                       sum                    nonzero 
                1.77615022                -7.20199179 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.03228741                -0.16812340 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.43754436                -0.97752236 
  absdiff.sum.faction.id.4                  nodecovar 
                0.25054664                 2.09717942 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                1501.68151                   90.67808 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                  48.33904                  131.11644 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 104.27740                   35.15068 
  absdiff.sum.faction.id.4                  nodecovar 
                 747.27397                  445.90829 
Starting MCMLE Optimization...
Optimizing with step length 0.0342.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.3532.
Distance from origin on tolerance region scale: 23084.8119025768 (previously 21313.0611206386).
Estimating equations are not within tolerance region.

Iteration 4 of at most 50 with parameter:
                       sum                    nonzero 
               1.773655155               -7.587682521 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
              -0.004762258               -0.155008392 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
              -0.410416319               -0.832264933 
  absdiff.sum.faction.id.4                  nodecovar 
               0.251435572                1.980327682 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                1442.58413                   88.92063 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                  53.42857                  122.31111 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  96.20000                   36.64127 
  absdiff.sum.faction.id.4                  nodecovar 
                 730.20317                  424.16567 
Starting MCMLE Optimization...
Optimizing with step length 0.0450.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.1217.
Distance from origin on tolerance region scale: 20919.4735113536 (previously 22667.2044492343).
Estimating equations are not within tolerance region.

Iteration 5 of at most 50 with parameter:
                       sum                    nonzero 
               1.805223494               -8.124432878 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
              -0.005110872               -0.158867498 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
              -0.354085529               -0.639982853 
  absdiff.sum.faction.id.4                  nodecovar 
               0.230182551                1.771366998 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 2048.
Average estimating function values:
                       sum                    nonzero 
                2069.95909                  152.06591 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 326.85909                  -70.50682 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 -11.18636                  -10.49091 
  absdiff.sum.faction.id.4                  nodecovar 
                1299.75455                  456.74582 
Starting MCMLE Optimization...
Optimizing with step length 0.0117.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.5808.
Distance from origin on tolerance region scale: 377041.998168358 (previously 168923.511805201).
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 6 of at most 50 with parameter:
                       sum                    nonzero 
                1.54454228               -10.55399517 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.01341625                 0.78509934 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                0.68110962                 0.85065551 
  absdiff.sum.faction.id.4                  nodecovar 
                0.23624415                 2.98870133 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 2048.
Average estimating function values:
                       sum                    nonzero 
                2299.04684                   48.16159 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                  34.35831                  399.61124 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 264.69555                  432.88759 
  absdiff.sum.faction.id.4                  nodecovar 
                 768.84309                  862.75288 
Starting MCMLE Optimization...
Optimizing with step length 0.0031.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 0.1205.
Distance from origin on tolerance region scale: 245839.912550477 (previously 1562128.44977367).
Estimating equations are not within tolerance region.

Iteration 7 of at most 50 with parameter:
                       sum                    nonzero 
                1.59151190               -11.88714551 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.01524644                 0.77069566 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                0.66472477                 0.83565665 
  absdiff.sum.faction.id.4                  nodecovar 
                0.22898863                 2.87441498 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                2295.44613                   48.08498 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                  34.38695                  401.74507 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 264.72382                  432.60395 
  absdiff.sum.faction.id.4                  nodecovar 
                 765.18665                  860.68000 
Starting MCMLE Optimization...
Optimizing with step length 0.0016.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 0.0478.
Distance from origin on tolerance region scale: 382989.639649976 (previously 384641.157176673).
Estimating equations are not within tolerance region.

Iteration 8 of at most 50 with parameter:
                       sum                    nonzero 
                1.61332964               -13.02142254 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.01403757                 0.76727245 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                0.66062103                 0.83161329 
  absdiff.sum.faction.id.4                  nodecovar 
                0.22722218                 2.81562908 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                2284.59699                   48.04181 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                  34.29933                  397.16722 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 265.30602                  428.04348 
  absdiff.sum.faction.id.4                  nodecovar 
                 766.00502                  857.30708 
Starting MCMLE Optimization...
Optimizing with step length 0.0007.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 0.0206.
Distance from origin on tolerance region scale: 742930.660468637 (previously 744114.696237215).
Estimating equations are not within tolerance region.

Iteration 9 of at most 50 with parameter:
                       sum                    nonzero 
                1.62792878               -14.08996417 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.01218923                 0.76363372 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                0.65553587                 0.82546429 
  absdiff.sum.faction.id.4                  nodecovar 
                0.22425445                 2.78088191 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                2293.22622                   48.00634 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                  35.51374                  400.03383 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 262.65539                  431.37421 
  absdiff.sum.faction.id.4                  nodecovar 
                 765.94292                  860.20410 
Starting MCMLE Optimization...
Optimizing with step length 0.0001.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 0.0029.
Distance from origin on tolerance region scale: 3892260.17866567 (previously 3897659.4889971).
Estimating equations are not within tolerance region.

Iteration 10 of at most 50 with parameter:
                       sum                    nonzero 
                1.62947165               -15.06885285 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.01230503                 0.76342954 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                0.65515261                 0.82487246 
  absdiff.sum.faction.id.4                  nodecovar 
                0.22402920                 2.77635403 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                2296.23200                   48.01143 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                  35.43314                  398.69257 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 263.70629                  432.10286 
  absdiff.sum.faction.id.4                  nodecovar 
                 768.04800                  861.44889 
Starting MCMLE Optimization...
Optimizing with step length 0.0002.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 0.0042.
Distance from origin on tolerance region scale: 2274904.89045586 (previously 2274215.53723709).
Estimating equations are not within tolerance region.

Iteration 11 of at most 50 with parameter:
                       sum                    nonzero 
                1.63226710               -15.94955769 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.01273347                 0.76285707 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                0.65489281                 0.82400865 
  absdiff.sum.faction.id.4                  nodecovar 
                0.22386764                 2.76823896 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Model statistics 'nonzero' are not varying. This may indicate that the observed data occupies an extreme point in the sample space or that the estimation has reached a dead-end configuration.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                2297.81833                   48.00000 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                  33.23077                  402.16858 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 264.80196                  429.91326 
  absdiff.sum.faction.id.4                  nodecovar 
                 768.24877                  861.57644 
Starting MCMLE Optimization...
Optimizing with step length 0.0354.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.8808.
Distance from origin on tolerance region scale: 29953.1352374241 (previously 29946.1552393754).
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 12 of at most 50 with parameter:
                       sum                    nonzero 
                1.73974405               -15.94955769 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.01819493                 0.73785533 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                0.62578067                 0.80067601 
  absdiff.sum.faction.id.4                  nodecovar 
                0.20971922                 2.40198104 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 2048.
Average estimating function values:
                       sum                    nonzero 
                2208.72557                   48.00832 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                  34.39917                  385.88150 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 255.16216                  421.49688 
  absdiff.sum.faction.id.4                  nodecovar 
                 736.35343                  827.17406 
Starting MCMLE Optimization...
Optimizing with step length 0.0002.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 0.0033.
Distance from origin on tolerance region scale: 2911422.92427743 (previously 2913561.57155014).
Estimating equations are not within tolerance region.

Iteration 13 of at most 50 with parameter:
                       sum                    nonzero 
                1.74142520               -16.85489107 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.01867931                 0.73779406 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                0.62587854                 0.80058651 
  absdiff.sum.faction.id.4                  nodecovar 
                0.20992572                 2.39679938 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                2207.91197                   48.01513 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                  33.69188                  388.24347 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 254.28336                  421.04539 
  absdiff.sum.faction.id.4                  nodecovar 
                 735.20495                  826.59945 
Starting MCMLE Optimization...
Optimizing with step length 0.0003.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 0.0069.
Distance from origin on tolerance region scale: 1715756.93857302 (previously 1715382.36391785).
Estimating equations are not within tolerance region.

Iteration 14 of at most 50 with parameter:
                       sum                    nonzero 
                1.74502650               -17.84277519 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.01891332                 0.73681494 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                0.62463789                 0.79968170 
  absdiff.sum.faction.id.4                  nodecovar 
                0.21046763                 2.38645684 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                2195.48677                   48.00176 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                  33.15168                  383.49383 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 253.23280                  418.79718 
  absdiff.sum.faction.id.4                  nodecovar 
                 731.34744                  822.53445 
Starting MCMLE Optimization...
Optimizing with step length < 0.0001.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by < 0.0001.
Distance from origin on tolerance region scale: 28858.4418662141 (previously 29133.2889280174).
Estimating equations are not within tolerance region.

Iteration 15 of at most 50 with parameter:
                       sum                    nonzero 
                1.74502650               -17.84277519 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.01891332                 0.73681494 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                0.62463789                 0.79968170 
  absdiff.sum.faction.id.4                  nodecovar 
                0.21046763                 2.38645684 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 4096.
Average estimating function values:
                       sum                    nonzero 
                2203.18058                   48.00194 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                  33.57476                  385.58835 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 254.24272                  418.72039 
  absdiff.sum.faction.id.4                  nodecovar 
                 735.08738                  825.31016 
Starting MCMLE Optimization...
Optimizing with step length < 0.0001.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by < 0.0001.
Distance from origin on tolerance region scale: 24991.8807065473 (previously 24849.2239572591).
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.
Quitting from lines 765-771 (valued.Rmd) 
Error: processing vignette 'valued.Rmd' failed with diagnostics:
MCMLE estimation stuck. There may be excessive correlation between model terms, suggesting a poor model for the observed data. If target.stats are specified, try increasing SAN parameters.
--- failed re-building 'valued.Rmd'

SUMMARY: processing the following file failed:
  'valued.Rmd'

Error: Vignette re-building failed.
Execution halted