CRAN Package Check Results for Package tergm

Last updated on 2026-06-07 02:50:38 CEST.

Flavor	Version	T_install	T_check	T_total	Status	Flags
r-devel-linux-x86_64-debian-clang	4.2.2	21.05	456.61	477.66	OK
r-devel-linux-x86_64-debian-gcc	4.2.2	18.66	314.06	332.72	ERROR
r-devel-linux-x86_64-fedora-clang	4.2.2	32.00	725.83	757.83	OK
r-devel-linux-x86_64-fedora-gcc	4.2.2	42.00	743.32	785.32	OK
r-devel-windows-x86_64	4.2.2	32.00	282.00	314.00	OK	--no-vignettes
r-patched-linux-x86_64	4.2.2	27.36	432.86	460.22	OK
r-release-linux-x86_64	4.2.2	24.61	437.24	461.85	OK
r-release-macos-arm64	4.2.2	5.00	77.00	82.00	OK
r-release-macos-x86_64	4.2.2	16.00	411.00	427.00	OK
r-release-windows-x86_64	4.2.2	33.00	281.00	314.00	OK	--no-vignettes
r-oldrel-macos-arm64	4.2.2				OK
r-oldrel-macos-x86_64	4.2.2	12.00	233.00	245.00	OK
r-oldrel-windows-x86_64	4.2.2	40.00	311.00	351.00	OK	--no-vignettes

Check Details

Version: 4.2.2
Check: Rd files
Result: WARN cannot open the connection cannot open the connection cannot open the connection cannot open the connection cannot open the connection cannot open the connection cannot open the connection cannot open the connection cannot open the connection problems found in ‘staticDiscordTNT-ergmProposal-b73a241d.Rd’, ‘stergm.Rd’, ‘stergm.utils.Rd’, ‘summary_formula.networkDynamic.Rd’, ‘tergm-deprecated.Rd’, ‘tergm-package.Rd’, ‘tergm.Rd’, ‘tergm.godfather.Rd’, ‘tergm_MCMC_sample.Rd’ Flavor: r-devel-linux-x86_64-debian-gcc

Version: 4.2.2
Check: tests
Result: ERROR Running ‘degree.mean.age.R’ [6s/7s] Running ‘dynamic_EGMME.R’ [0s/1s] Running ‘dynamic_MLE_blockdiag.R’ [0s/0s] Running ‘dynamic_MLE_blockdiag.bipartite.R’ [0s/0s] Running ‘sim_gf_sum.R’ [6s/7s] Running ‘simulate_networkDynamic.R’ [5s/6s] Running ‘tergm_offset_tests.R’ [0s/0s] Running ‘tergm_parallel.R’ [0s/0s] Running ‘testthat.R’ [202s/117s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # File tests/testthat.R in package tergm, part of the Statnet suite of > # packages for network analysis, https://statnet.org . > # > # This software is distributed under the GPL-3 license. It is free, open > # source, and has the attribution requirements (GPL Section 7) at > # https://statnet.org/attribution . > # > # Copyright 2008-2025 Statnet Commons > ################################################################################ > > require(testthat) Loading required package: testthat > require(tergm) Loading required package: tergm Loading required package: ergm Loading required package: network 'network' 1.20.0 (2026-02-06), 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' 4.12.0 (2026-02-17), 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. Loading required package: networkDynamic 'networkDynamic' 0.12.0 (2026-04-08), part of the Statnet Project * 'news(package="networkDynamic")' for changes since last version * 'citation("networkDynamic")' for citation information * 'https://statnet.org' for help, support, and other information Registered S3 method overwritten by 'tergm': method from simulate_formula.network ergm 'tergm' 4.2.2 (2025-06-15), part of the Statnet Project * 'news(package="tergm")' for changes since last version * 'citation("tergm")' for citation information * 'https://statnet.org' for help, support, and other information Attaching package: 'tergm' The following object is masked from 'package:ergm': snctrl > > test_check("tergm") Starting 2 test processes. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0014. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. > test-CMLE-2-bip.R: Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0018. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. > test-CMLE-2-bip.R: Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0002. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-bip.R: 1 > test-CMLE-2-bip.R: Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0005. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0007. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0018. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by < 0.0001. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. > test-CMLE-2-bip.R: Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0031. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0003. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. > test-CMLE-2-bip.R: Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-dir.R: 1 > test-CMLE-2-dir.R: Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0042. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by < 0.0001. > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. > test-CMLE-2-bip.R: Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: 1 > test-CMLE-2-dir.R: Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0002. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0033. > test-CMLE-2-dir.R: Convergence test p-value: 0.0002. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0063. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. > test-CMLE-2-dir.R: Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0026. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0006. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0003. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0001. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: 1 > test-CMLE-2-bip.R: Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0031. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. > test-CMLE-2-bip.R: Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0075. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0001. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0018. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: 1 > test-CMLE-2-dir.R: Optimizing with step length 1.0000. > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0008. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0008. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. > test-CMLE-2-bip.R: Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-bip.R: Obtaining the responsible dyads. > test-CMLE-2-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-bip.R: Finished MPLE. > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-bip.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by < 0.0001. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. > test-CMLE-2-dir.R: Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-bip.R: 1 > test-CMLE-2-bip.R: Optimizing with step length 1.0000. > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-bip.R: The log-likelihood improved by 0.0010. > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-bip.R: Finished MCMLE. > test-CMLE-2-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: 1 > test-CMLE-2-dir.R: Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0013. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0028. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. > test-CMLE-2-dir.R: Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0110. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. > test-CMLE-2-dir.R: Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-dir.R: 1 > test-CMLE-2-dir.R: Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by < 0.0001. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. > test-CMLE-2-dir.R: Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: 1 > test-CMLE-2-und.R: Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0038. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. > test-CMLE-2-und.R: Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0145. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. > test-CMLE-2-dir.R: Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0003. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. > test-CMLE-2-und.R: Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-dir.R: Obtaining the responsible dyads. > test-CMLE-2-dir.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-dir.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-2-dir.R: Finished MPLE. > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-dir.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: 1 > test-CMLE-2-und.R: Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0024. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. > test-CMLE-2-und.R: Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0010. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-dir.R: The log-likelihood improved by 0.0034. > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-dir.R: Finished MCMLE. > test-CMLE-2-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: 1 > test-CMLE-2-und.R: Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0004. > test-CMLE-2-und.R: Convergence test p-value: 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: 1 > test-CMLE-2-und.R: Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0001. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. > test-CMLE-2-und.R: Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: 1 > test-CMLE-2-und.R: Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0021. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. > test-CMLE-2-und.R: Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0007. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0015. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: 1 > test-CMLE-bip.R: Optimizing with step length 1.0000. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: The log-likelihood improved by 0.0107. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0051. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: 1 > test-CMLE-bip.R: Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0020. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: 1 > test-CMLE-2-und.R: Optimizing with step length 1.0000. > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-und.R: The log-likelihood improved by 0.0025. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. > test-CMLE-2-und.R: Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: 1 > test-CMLE-bip.R: Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0029. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. > test-CMLE-bip.R: Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: 1 > test-CMLE-2-und.R: Optimizing with step length 1.0000. > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-und.R: The log-likelihood improved by 0.0005. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0004. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. > test-CMLE-bip.R: Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0005. > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. > test-CMLE-2-und.R: Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-bip.R: 1 > test-CMLE-bip.R: Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0049. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-2-und.R: 1 > test-CMLE-2-und.R: Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0005. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0018. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. > test-CMLE-bip.R: Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0016. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. > test-CMLE-2-und.R: Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: 1 > test-CMLE-2-und.R: Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0005. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: 1 > test-CMLE-2-und.R: Optimizing with step length 1.0000. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-2-und.R: The log-likelihood improved by 0.0008. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-2-und.R: Obtaining the responsible dyads. > test-CMLE-2-und.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: Finished MPLE. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-2-und.R: Iteration 1 of at most 60: > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-2-und.R: 1 > test-CMLE-2-und.R: Optimizing with step length 1.0000. > test-CMLE-2-und.R: The log-likelihood improved by 0.0012. > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-2-und.R: Finished MCMLE. > test-CMLE-2-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0033. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-bip.R: Convergence test p-value: 0.0001. > test-CMLE-bip.R: Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: The log-likelihood improved by 0.0108. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-bip.R: 1 > test-CMLE-bip.R: Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0004. > test-CMLE-dir.R: 1 > test-CMLE-dir.R: Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by < 0.0001. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-dir.R: 1 > test-CMLE-dir.R: Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by 0.0003. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-dir.R: 1 > test-CMLE-dir.R: Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by 0.0044. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. > test-CMLE-dir.R: Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0095. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. > test-CMLE-bip.R: Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by 0.0001. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. > test-CMLE-dir.R: Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-bip.R: The log-likelihood improved by 0.0002. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-dir.R: The log-likelihood improved by 0.0002. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. > test-CMLE-dir.R: Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-dir.R: 1 > test-CMLE-dir.R: Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by 0.0005. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-bip.R: 1 > test-CMLE-bip.R: Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0081. > test-CMLE-dir.R: 1 > test-CMLE-dir.R: Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by 0.0021. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. > test-CMLE-dir.R: Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Convergence test p-value: 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-bip.R: 1 > test-CMLE-bip.R: Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0014. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-bip.R: 1 Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0005. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. > test-CMLE-bip.R: Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-bip.R: Obtaining the responsible dyads. > test-CMLE-bip.R: Evaluating the predictor and response matrix. > test-CMLE-bip.R: Maximizing the pseudolikelihood. > test-CMLE-bip.R: Finished MPLE. > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-bip.R: Iteration 1 of at most 60: > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-bip.R: 1 > test-CMLE-bip.R: Optimizing with step length 1.0000. > test-CMLE-bip.R: The log-likelihood improved by 0.0004. > test-CMLE-bip.R: Convergence test p-value: < 0.0001. > test-CMLE-bip.R: Converged with 99% confidence. > test-CMLE-bip.R: Finished MCMLE. > test-CMLE-bip.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function. Saving _problems/test-CMLE-bip-12.R > test-CMLE-dir.R: 1 > test-CMLE-dir.R: Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by 0.0173. > test-CMLE-dir.R: Convergence test p-value: 0.0002. > test-CMLE-dir.R: Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: 1 > test-CMLE-dir.R: Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by 0.0002. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. > test-CMLE-dir.R: Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-und.R: Finished MPLE. > test-CMLE-dir.R: 1 > test-CMLE-dir.R: Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by < 0.0001. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. > test-CMLE-dir.R: Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-und.R: Finished MPLE. > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-dir.R: 1 > test-CMLE-dir.R: Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by < 0.0001. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-dir.R: The log-likelihood improved by 0.0044. > test-CMLE-dir.R: Convergence test p-value: 0.0001. > test-CMLE-dir.R: Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by 0.0008. > test-CMLE-und.R: 1 > test-CMLE-und.R: Optimizing with step length 1.0000. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: The log-likelihood improved by 0.0062. > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-dir.R: 1 > test-CMLE-dir.R: Optimizing with step length 1.0000. > test-CMLE-dir.R: The log-likelihood improved by 0.0042. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. > test-CMLE-dir.R: Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: 1 > test-CMLE-und.R: Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0020. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-dir.R: Obtaining the responsible dyads. > test-CMLE-dir.R: Evaluating the predictor and response matrix. > test-CMLE-dir.R: Maximizing the pseudolikelihood. > test-CMLE-dir.R: Finished MPLE. > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-dir.R: Iteration 1 of at most 60: > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0001. > test-CMLE-dir.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-dir.R: The log-likelihood improved by 0.0002. > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-dir.R: Finished MCMLE. > test-CMLE-dir.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-und.R: 1 > test-CMLE-und.R: Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0006. > test-CMLE-und.R: Convergence test p-value: < 0.0001. > test-CMLE-und.R: Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-EGMME-errors.R: Targets contains offset statistics; they will only be used during the SAN run, and removal of the offset statistics will be atte > test-EGMME-errors.R: mpted for the EGMME targets. > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-EGMME-errors.R: Targets contains offset statistics; they will only be used during the SAN run, and removal of the offset statistics will be attempted for the EGMME targets. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-und.R: 1 > test-CMLE-und.R: Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0016. > test-EGMME-errors.R: Starting simulated annealing (SAN) > test-EGMME-errors.R: Iteration 1 of at most 4 > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-EGMME-errors.R: Iteration 2 of at most 4 > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-EGMME-errors.R: Iteration 3 of at most 4 > test-EGMME-errors.R: Iteration 4 of at most 4 > test-EGMME-errors.R: Finished simulated annealing > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-EGMME-errors.R: Starting simulated annealing (SAN) > test-EGMME-errors.R: Iteration 1 of at most 4 > test-EGMME-errors.R: Iteration 2 of at most 4 > test-EGMME-errors.R: Iteration 3 of at most 4 > test-EGMME-errors.R: Iteration 4 of at most 4 > test-EGMME-errors.R: Finished simulated annealing > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0003. > test-CMLE-und.R: Convergence test p-value: < 0.0001. > test-CMLE-und.R: Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-EGMME-initialfit.R: Starting simulated annealing (SAN) > test-EGMME-initialfit.R: Iteration 1 of at most 4 > test-CMLE-und.R: 1 > test-EGMME-initialfit.R: Finished simulated annealing > test-CMLE-und.R: Optimizing with step length 1.0000. > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE): > test-EGMME-initialfit.R: Obtaining the responsible dyads. > test-EGMME-initialfit.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: The log-likelihood improved by 0.0121. > test-EGMME-initialfit.R: Finished MPLE. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-EGMME-initialfit.R: Starting simulated annealing (SAN) > test-EGMME-initialfit.R: Iteration 1 of at most 4 > test-EGMME-initialfit.R: Finished simulated annealing > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE): > test-EGMME-initialfit.R: Obtaining the responsible dyads. > test-EGMME-initialfit.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Maximizing the pseudolikelihood. > test-EGMME-initialfit.R: Finished MPLE. > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-EGMME-initialfit.R: Starting simulated annealing (SAN) > test-EGMME-initialfit.R: Iteration 1 of at most 4 > test-EGMME-initialfit.R: Finished simulated annealing > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE): > test-EGMME-initialfit.R: Obtaining the responsible dyads. > test-EGMME-initialfit.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Maximizing the pseudolikelihood. > test-EGMME-initialfit.R: Finished MPLE. > test-EGMME-initialfit.R: Starting simulated annealing (SAN) > test-EGMME-initialfit.R: Iteration 1 of at most 4 > test-EGMME-initialfit.R: Finished simulated annealing > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE): > test-EGMME-initialfit.R: Obtaining the responsible dyads. > test-EGMME-initialfit.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Maximizing the pseudolikelihood. > test-EGMME-initialfit.R: Finished MPLE. > test-CMLE-und.R: 1 > test-CMLE-und.R: Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0001. > test-CMLE-und.R: Convergence test p-value: < 0.0001. > test-CMLE-und.R: Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-EGMME-initialfit.R: Starting simulated annealing (SAN) > test-EGMME-initialfit.R: Iteration 1 of at most 4 > test-EGMME-initialfit.R: Iteration 2 of at most 4 > test-EGMME-initialfit.R: Iteration 3 of at most 4 > test-EGMME-initialfit.R: Iteration 4 of at most 4 > test-EGMME-initialfit.R: Finished simulated annealing > test-EGMME-initialfit.R: Unable to match target stats. Using MCMLE estimation. > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE): > test-EGMME-initialfit.R: Obtaining the responsible dyads. > test-EGMME-initialfit.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Maximizing the pseudolikelihood. > test-EGMME-initialfit.R: Finished MPLE. > test-EGMME-initialfit.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-EGMME-initialfit.R: Iteration 1 of at most 60: > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-EGMME-initialfit.R: 1 Optimizing with step length 1.0000. > test-EGMME-initialfit.R: The log-likelihood improved by 0.0157. > test-EGMME-initialfit.R: Convergence test p-value: < 0.0001. > test-EGMME-initialfit.R: Converged with 99% confidence. > test-EGMME-initialfit.R: Finished MCMLE. > test-EGMME-initialfit.R: This model was fit using MCMC. To examine model diagnostics and check > test-EGMME-initialfit.R: for degeneracy, use the mcmc.diagnostics() function. > test-EGMME-initialfit.R: Starting simulated annealing (SAN) > test-EGMME-initialfit.R: Iteration 1 of at most 4 > test-EGMME-initialfit.R: Iteration 2 of at most 4 > test-EGMME-initialfit.R: Iteration 3 of at most 4 > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-EGMME-initialfit.R: Iteration 4 of at most 4 > test-EGMME-initialfit.R: Finished simulated annealing > test-EGMME-initialfit.R: Unable to match target stats. Using MCMLE estimation. > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE): > test-EGMME-initialfit.R: Obtaining the responsible dyads. > test-EGMME-initialfit.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Maximizing the pseudolikelihood. > test-EGMME-initialfit.R: Finished MPLE. > test-EGMME-initialfit.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-EGMME-initialfit.R: Iteration 1 of at most 60: > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-EGMME-initialfit.R: The log-likelihood improved by 0.0157. > test-EGMME-initialfit.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-EGMME-initialfit.R: Finished MCMLE. > test-EGMME-initialfit.R: This model was fit using MCMC. To examine model diagnostics and check > test-EGMME-initialfit.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-EGMME-initialfit.R: Starting simulated annealing (SAN) > test-EGMME-initialfit.R: Iteration 1 of at most 4 > test-EGMME-initialfit.R: Iteration 2 of at most 4 > test-EGMME-initialfit.R: Iteration 3 of at most 4 > test-EGMME-initialfit.R: Iteration 4 of at most 4 > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Finished simulated annealing > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-EGMME-initialfit.R: Unable to match target stats. Using MCMLE estimation. > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE): > test-EGMME-initialfit.R: Obtaining the responsible dyads. > test-EGMME-initialfit.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Finished MPLE. > test-EGMME-initialfit.R: Maximizing the pseudolikelihood. > test-EGMME-initialfit.R: Finished MPLE. > test-EGMME-initialfit.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-EGMME-initialfit.R: Iteration 1 of at most 60: > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-EGMME-initialfit.R: 1 Optimizing with step length 1.0000. > test-EGMME-initialfit.R: The log-likelihood improved by 0.0157. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-EGMME-initialfit.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-EGMME-initialfit.R: Finished MCMLE. > test-EGMME-initialfit.R: This model was fit using MCMC. To examine model diagnostics and check > test-EGMME-initialfit.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Starting simulated annealing (SAN) > test-EGMME-initialfit.R: Iteration 1 of at most 4 > test-EGMME-initialfit.R: Iteration 2 of at most 4 > test-EGMME-initialfit.R: Iteration 3 of at most 4 > test-EGMME-initialfit.R: Iteration 4 of at most 4 > test-EGMME-initialfit.R: Finished simulated annealing > test-EGMME-initialfit.R: Unable to match target stats. Using MCMLE estimation. > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE): > test-EGMME-initialfit.R: Obtaining the responsible dyads. > test-EGMME-initialfit.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-EGMME-initialfit.R: Finished MPLE. > test-EGMME-initialfit.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-EGMME-initialfit.R: Iteration 1 of at most 60: > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-und.R: Model statistics 'Persist(1)~edges' 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. > test-CMLE-und.R: Post-burnin sample is constant; returning. > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0002. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-EGMME-initialfit.R: 1 Optimizing with step length 1.0000. > test-EGMME-initialfit.R: The log-likelihood improved by 0.0157. > test-EGMME-initialfit.R: Convergence test p-value: < 0.0001. > test-EGMME-initialfit.R: Converged with 99% confidence. > test-EGMME-initialfit.R: Finished MCMLE. > test-EGMME-initialfit.R: This model was fit using MCMC. To examine model diagnostics and check > test-EGMME-initialfit.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-EGMME-initialfit.R: Starting simulated annealing (SAN) > test-EGMME-initialfit.R: Iteration 1 of at most 4 > test-EGMME-initialfit.R: Iteration 2 of at most 4 > test-EGMME-initialfit.R: Iteration 3 of at most 4 > test-EGMME-initialfit.R: Iteration 4 of at most 4 > test-EGMME-initialfit.R: Finished simulated annealing > test-EGMME-initialfit.R: Unable to match target stats. Using MCMLE estimation. > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE): > test-EGMME-initialfit.R: Obtaining the responsible dyads. > test-EGMME-initialfit.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Maximizing the pseudolikelihood. > test-EGMME-initialfit.R: Finished MPLE. > test-EGMME-initialfit.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-EGMME-initialfit.R: Iteration 1 of at most 60: > test-CMLE-und.R: 1 > test-CMLE-und.R: Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0013. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-EGMME-initialfit.R: 1 Optimizing with step length 1.0000. > test-EGMME-initialfit.R: The log-likelihood improved by 0.0157. > test-EGMME-initialfit.R: Convergence test p-value: < 0.0001. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Converged with 99% confidence. > test-EGMME-initialfit.R: Finished MCMLE. > test-EGMME-initialfit.R: This model was fit using MCMC. To examine model diagnostics and check > test-EGMME-initialfit.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-EGMME-initialfit.R: Starting simulated annealing (SAN) > test-EGMME-initialfit.R: Iteration 1 of at most 4 > test-EGMME-initialfit.R: Iteration 2 of at most 4 > test-EGMME-initialfit.R: Iteration 3 of at most 4 > test-EGMME-initialfit.R: Iteration 4 of at most 4 > test-EGMME-initialfit.R: Finished simulated annealing > test-EGMME-initialfit.R: Unable to match target stats. Using MCMLE estimation. > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE): > test-EGMME-initialfit.R: Obtaining the responsible dyads. > test-EGMME-initialfit.R: Evaluating the predictor and response matrix. > test-EGMME-initialfit.R: Maximizing the pseudolikelihood. > test-EGMME-initialfit.R: Finished MPLE. > test-EGMME-initialfit.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-EGMME-initialfit.R: Iteration 1 of at most 60: > test-EGMME-initialfit.R: 1 Optimizing with step length 1.0000. > test-EGMME-initialfit.R: The log-likelihood improved by 0.0157. > test-EGMME-initialfit.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-EGMME-initialfit.R: Finished MCMLE. > test-EGMME-initialfit.R: This model was fit using MCMC. To examine model diagnostics and check > test-EGMME-initialfit.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0041. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-und.R: 1 > test-CMLE-und.R: Optimizing with step length 1.0000. > test-EGMME-simple.R: Initializing unconstrained Metropolis-Hastings proposal: > test-EGMME-simple.R: 'ergm:MH_SPDyad'. > test-EGMME-simple.R: Initializing model... > test-CMLE-und.R: The log-likelihood improved by 0.0124. > test-EGMME-simple.R: Model initialized. > test-EGMME-simple.R: Starting 4 SAN iterations of 524288 steps each. > test-EGMME-simple.R: > test-EGMME-simple.R: Iteration 1 of at most 4 > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-EGMME-simple.R: SAN Metropolis-Hastings accepted 75.742% of 32768 proposed steps. > test-EGMME-simple.R: SAN summary statistics: > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: 6.9 > test-EGMME-simple.R: Meanstats Goal: > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: 10 > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats = > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: -3.1 > test-EGMME-simple.R: New statistics scaling = > test-EGMME-simple.R: [1] 1 > test-EGMME-simple.R: Scaled Mahalanobis distance = 9.61000000000013 > test-EGMME-simple.R: > test-EGMME-simple.R: Iteration 2 of at most 4 > test-EGMME-simple.R: SAN Metropolis-Hastings accepted 66.619% of 69632 proposed steps. > test-EGMME-simple.R: SAN summary statistics: > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: 8.4 > test-EGMME-simple.R: Meanstats Goal: > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: 10 > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats = > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: -1.6 > test-EGMME-simple.R: New statistics scaling = > test-EGMME-simple.R: [1] 1 > test-EGMME-simple.R: Scaled Mahalanobis distance = 2.56000000000006 > test-EGMME-simple.R: > test-EGMME-simple.R: Iteration 3 of at most 4 > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-EGMME-simple.R: SAN Metropolis-Hastings accepted 61.490% of 139264 proposed steps. > test-EGMME-simple.R: SAN summary statistics: > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: 9.7 > test-EGMME-simple.R: Meanstats Goal: > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: 10 > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats = > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: -0.3 > test-EGMME-simple.R: New statistics scaling = > test-EGMME-simple.R: [1] 1 > test-EGMME-simple.R: Scaled Mahalanobis distance = 0.0900000000000113 > test-EGMME-simple.R: > test-EGMME-simple.R: Iteration 4 of at most 4 > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-EGMME-simple.R: SAN Metropolis-Hastings accepted 0.000% of 278528 proposed steps. > test-EGMME-simple.R: SAN summary statistics: > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: 10 > test-EGMME-simple.R: Meanstats Goal: > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: 10 > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats = > test-EGMME-simple.R: meandeg > test-EGMME-simple.R: -1.879052e-14 > test-EGMME-simple.R: New statistics scaling = > test-EGMME-simple.R: [1] 1 > test-EGMME-simple.R: Scaled Mahalanobis distance = 3.53083818192423e-28 > test-EGMME-simple.R: Finished simulated annealing > test-EGMME-simple.R: Initializing Metropolis-Hastings proposal. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-EGMME-simple.R: Constructing an approximate response network. > test-EGMME-simple.R: Starting 4 SAN iterations of 80000 steps each. > test-EGMME-simple.R: SAN Metropolis-Hastings accepted 59.619% of 4096 proposed steps. > test-EGMME-simple.R: > test-EGMME-simple.R: Iteration 1 of at most 4 > test-EGMME-simple.R: SAN summary statistics: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 11 1073741824 > test-EGMME-simple.R: Meanstats Goal: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 10 10 > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats = > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 1 1073741814 > test-EGMME-simple.R: New statistics scaling = > test-EGMME-simple.R: [1] 0.5 0.5 > test-EGMME-simple.R: Scaled Mahalanobis distance = 576460741566005312 > test-EGMME-simple.R: > test-EGMME-simple.R: Iteration 2 of at most 4 > test-EGMME-simple.R: SAN Metropolis-Hastings accepted 31.152% of 8192 proposed steps. > test-EGMME-simple.R: SAN summary statistics: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 10 1073741824 > test-EGMME-simple.R: Meanstats Goal: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 10 10 > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats = > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 0 1073741814 > test-EGMME-simple.R: New statistics scaling = > test-EGMME-simple.R: [1] 0.5 0.5 > test-EGMME-simple.R: Scaled Mahalanobis distance = 576460741566005312 > test-EGMME-simple.R: > test-EGMME-simple.R: Iteration 3 of at most 4 > test-EGMME-simple.R: SAN Metropolis-Hastings accepted 2.061% of 20480 proposed steps. > test-EGMME-simple.R: SAN summary statistics: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 10 1073741824 > test-EGMME-simple.R: Meanstats Goal: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 10 10 > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats = > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 0 1073741814 > test-EGMME-simple.R: New statistics scaling = > test-EGMME-simple.R: [1] 0.5 0.5 > test-EGMME-simple.R: Scaled Mahalanobis distance = 576460741566005312 > test-EGMME-simple.R: > test-EGMME-simple.R: Iteration 4 of at most 4 > test-EGMME-simple.R: SAN Metropolis-Hastings accepted 0.000% of 40960 proposed steps. > test-EGMME-simple.R: SAN summary statistics: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 10 1073741824 > test-EGMME-simple.R: Meanstats Goal: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 10 10 > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats = > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 0 1073741814 > test-EGMME-simple.R: New statistics scaling = > test-EGMME-simple.R: [1] 0.5 0.5 > test-EGMME-simple.R: Scaled Mahalanobis distance = 576460741566005312 > test-EGMME-simple.R: Finished simulated annealing > test-EGMME-simple.R: SAN summary statistics: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 10 1073741824 > test-EGMME-simple.R: Meanstats Goal: > test-EGMME-simple.R: [1] 10 10 > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats = > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 0 1073741814 > test-EGMME-simple.R: Fitting TERGM Equilibrium GMME. > test-EGMME-simple.R: Starting optimization with with coef_0 = ( -2.94443897916644 1 ). > test-EGMME-simple.R: ======== Phase 1: Burn in, get initial gradient values, and find a configuration under which all targets vary. ======== > test-EGMME-simple.R: Burning in... > test-CMLE-und.R: Model statistics 'Persist(1)~edges' 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. > test-CMLE-und.R: Post-burnin sample is constant; returning. > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0007. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0124. > test-CMLE-und.R: Convergence test p-value: < 0.0001. > test-CMLE-und.R: Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-CMLE-und.R: The log-likelihood improved by 0.0040. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-EGMME-simple.R: Returned from STERGM burnin > test-EGMME-simple.R: Done. > test-EGMME-simple.R: ======== Attempt 1 ======== > test-EGMME-simple.R: Running stochastic optimization... > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'. > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE): > test-CMLE-und.R: Obtaining the responsible dyads. > test-CMLE-und.R: Evaluating the predictor and response matrix. > test-CMLE-und.R: Maximizing the pseudolikelihood. > test-CMLE-und.R: Finished MPLE. > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-CMLE-und.R: Iteration 1 of at most 60: > test-CMLE-und.R: 1 Optimizing with step length 1.0000. > test-EGMME-simple.R: Finished. Extracting. > test-CMLE-und.R: The log-likelihood improved by 0.0039. > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-CMLE-und.R: Finished MCMLE. > test-CMLE-und.R: This model was fit using MCMC. To examine model diagnostics and check > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: Starting maximum pseudolikelihood estimation (MPLE): > test-basis.R: Obtaining the responsible dyads. > test-basis.R: Evaluating the predictor and response matrix. > test-basis.R: Maximizing the pseudolikelihood. > test-basis.R: Finished MPLE. > test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-basis.R: Iteration 1 of at most 60: > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 1.2158. > test-basis.R: Estimating equations are not within tolerance region. > test-basis.R: Iteration 2 of at most 60: > test-EGMME-simple.R: Finished. Extracting. > test-basis.R: 1 Optimizing with step length 1.0000. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: The log-likelihood improved by 0.0687. > test-basis.R: Convergence test p-value: 0.0280. Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 3 of at most 60: > test-EGMME-simple.R: Finished. Extracting. > test-basis.R: 1 Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0837. > test-basis.R: Convergence test p-value: 0.6094. Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 4 of at most 60: > test-EGMME-simple.R: All parameters have some effect and all statistics are moving. Proceeding to Phase 2. > test-EGMME-simple.R: ======== Phase 2: Find and refine the estimate. ======== > test-EGMME-simple.R: ======== Subphase 1 ======== > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0656. > test-basis.R: Convergence test p-value: 0.5885. Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 5 of at most 60: > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -3.535745 1.475307 > test-EGMME-simple.R: Estimating equations = 0 p-value: 1.3914799890544e-110 , trending: 3.27131906336549e-08 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0914. > test-EGMME-simple.R: Finished. Extracting. > test-basis.R: Convergence test p-value: 0.2077. > test-basis.R: Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 6 of at most 60: > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -4.118841 1.802270 > test-EGMME-simple.R: Estimating equations = 0 p-value: 3.01245591281223e-42 , trending: 3.81048849286777e-19 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -4.930926 2.325456 > test-EGMME-simple.R: Estimating equations = 0 p-value: 5.01298005644045e-24 , trending: 5.10804229077213e-13 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.280278 2.102755 > test-EGMME-simple.R: Estimating equations = 0 p-value: 3.60431227021322e-07 , trending: 4.19606113727738e-13 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0096. > test-EGMME-simple.R: Finished. Extracting. > test-basis.R: Convergence test p-value: 0.0031. > test-basis.R: Converged with 99% confidence. > test-basis.R: Finished MCMLE. > test-basis.R: Evaluating log-likelihood at the estimate. > test-basis.R: Fitting the dyad-independent submodel... > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.068490 2.059625 > test-basis.R: Bridging between the dyad-independent submodel and the full model... > test-basis.R: Setting up bridge sampling... > test-basis.R: Using 16 bridges: 1 > test-basis.R: 2 > test-basis.R: 3 > test-basis.R: 4 > test-basis.R: 5 > test-basis.R: 6 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.00114190507638694 , trending: 5.57325010969205e-19 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: 7 > test-basis.R: 8 > test-basis.R: 9 > test-basis.R: 10 > test-basis.R: 11 > test-basis.R: 12 > test-basis.R: 13 > test-basis.R: 14 > test-basis.R: 15 > test-basis.R: 16 > test-basis.R: . > test-basis.R: Bridging finished. > test-basis.R: > test-basis.R: This model was fit using MCMC. To examine model diagnostics and check > test-basis.R: for degeneracy, use the mcmc.diagnostics() function. > test-basis.R: Starting maximum pseudolikelihood estimation (MPLE): > test-basis.R: Obtaining the responsible dyads. > test-basis.R: Evaluating the predictor and response matrix. > test-basis.R: Maximizing the pseudolikelihood. > test-basis.R: Finished MPLE. > test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-EGMME-simple.R: Finished. Extracting. > test-basis.R: Iteration 1 of at most 60: > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.226398 2.400553 > test-EGMME-simple.R: Estimating equations = 0 p-value: 7.02401736927543e-05 , trending: 7.5737719187006e-11 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 1.2158. > test-basis.R: Estimating equations are not within tolerance region. > test-basis.R: Iteration 2 of at most 60: > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.106173 2.441485 > test-basis.R: 1 Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0687. > test-basis.R: Convergence test p-value: 0.0280. Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 3 of at most 60: > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.00583560731513221 , trending: 3.11134253124785e-11 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0837. > test-basis.R: Convergence test p-value: 0.6094. > test-EGMME-simple.R: Finished. Extracting. > test-basis.R: Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 4 of at most 60: > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.633203 2.130450 > test-basis.R: 1 Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0656. > test-basis.R: Convergence test p-value: 0.5885. Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 5 of at most 60: > test-EGMME-simple.R: Estimating equations = 0 p-value: 6.22153092853623e-05 , trending: 6.06750743355267e-07 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.330221 2.182673 > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0914. > test-basis.R: Convergence test p-value: 0.2077. Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 6 of at most 60: > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.00496616862578934 , trending: 2.60795493515843e-16 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.268428 2.343567 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.963218136606521 , trending: 0.0671395053447981 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 4 / 5 to go. > test-EGMME-simple.R: ======== Subphase 2 ======== > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.224301 2.272558 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.878641316766649 , trending: 0.0564044565523399 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 4 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0096. > test-basis.R: Convergence test p-value: 0.0031. Converged with 99% confidence. > test-basis.R: Finished MCMLE. > test-basis.R: Evaluating log-likelihood at the estimate. > test-EGMME-simple.R: Finished. Extracting. > test-basis.R: Fitting the dyad-independent submodel... > test-basis.R: Bridging between the dyad-independent submodel and the full model... > test-basis.R: Setting up bridge sampling... > test-basis.R: Using 16 bridges: > test-basis.R: 1 > test-basis.R: 2 > test-basis.R: 3 > test-basis.R: 4 > test-basis.R: 5 > test-basis.R: 6 > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.157947 2.304233 > test-basis.R: 7 > test-basis.R: 8 > test-basis.R: 9 > test-basis.R: 10 > test-basis.R: 11 > test-basis.R: 12 > test-basis.R: 13 > test-basis.R: 14 > test-basis.R: 15 > test-basis.R: 16 > test-basis.R: . > test-basis.R: Bridging finished. > test-basis.R: > test-basis.R: This model was fit using MCMC. To examine model diagnostics and check > test-basis.R: for degeneracy, use the mcmc.diagnostics() function. > test-basis.R: Starting maximum pseudolikelihood estimation (MPLE): > test-basis.R: Obtaining the responsible dyads. > test-basis.R: Evaluating the predictor and response matrix. > test-basis.R: Maximizing the pseudolikelihood. > test-basis.R: Finished MPLE. > test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-basis.R: Iteration 1 of at most 60: > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.828767985605079 , trending: 0.038176878635704 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 3 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.097439 2.306586 > test-basis.R: 1 Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 1.2158. > test-basis.R: Estimating equations are not within tolerance region. > test-basis.R: Iteration 2 of at most 60: > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.726377956354663 , trending: 0.00550122868318379 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0687. > test-basis.R: Convergence test p-value: 0.0280. Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 3 of at most 60: > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.18524 2.07143 > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0837. > test-basis.R: Convergence test p-value: 0.6094. Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 4 of at most 60: > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.688236715199918 , trending: 0.000244622114012557 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0656. > test-basis.R: Convergence test p-value: 0.5885. Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 5 of at most 60: > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.165517 2.377492 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.887135556274084 , trending: 0.00435733865560731 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0914. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.082794 2.227639 > test-basis.R: Convergence test p-value: 0.2077. > test-basis.R: Not converged with 99% confidence; increasing sample size. > test-basis.R: Iteration 6 of at most 60: > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.746906892213617 , trending: 0.00984507486438503 . > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.332418 2.184492 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.72903979754815 , trending: 0.143800594218699 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 4 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0096. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.204873 2.165226 > test-basis.R: Convergence test p-value: 0.0031. > test-basis.R: Converged with 99% confidence. > test-basis.R: Finished MCMLE. > test-basis.R: Evaluating log-likelihood at the estimate. > test-basis.R: Fitting the dyad-independent submodel... > test-basis.R: Bridging between the dyad-independent submodel and the full model... > test-basis.R: Setting up bridge sampling... > test-basis.R: Using 16 bridges: 1 > test-basis.R: 2 > test-basis.R: 3 > test-basis.R: 4 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.915517851686919 , trending: 0.0308422566828079 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 3 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-basis.R: 5 > test-basis.R: 6 > test-basis.R: 7 > test-basis.R: 8 > test-basis.R: 9 > test-basis.R: 10 > test-basis.R: 11 > test-basis.R: 12 > test-basis.R: 13 14 > test-basis.R: 15 > test-basis.R: 16 > test-basis.R: . > test-basis.R: Bridging finished. > test-basis.R: > test-basis.R: This model was fit using MCMC. To examine model diagnostics and check > test-basis.R: for degeneracy, use the mcmc.diagnostics() function. > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.251966 1.944937 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.849842619342716 , trending: 0.156756643900556 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 2 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.108031 2.223334 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.898419792326419 , trending: 0.0787005201225524 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 1 / 5 to go. > test-EGMME-simple.R: Approximate standard error of the estimate: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: 0.4922024 0.5499035 > test-EGMME-simple.R: Approximate standard error of window means: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: 0.01975644 0.01576257 > test-EGMME-simple.R: par. var. / (std. var. + par. var.): > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: 0.0016085363 0.0008209645 > test-EGMME-simple.R: Local nonlinearity p-value: 0.0948548593932981 > test-EGMME-simple.R: There is evidence of local nonlinearity. Continuing. > test-EGMME-simple.R: ======== Subphase 3 ======== > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.104021 2.255011 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.852201823894968 , trending: 0.0481180630546847 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 4 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.143689 2.200477 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.865672941020689 , trending: 0.219231451535762 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 3 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.151976 2.173453 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.811071100224124 , trending: 0.528424223198149 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 2 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.139175 2.102897 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.947368135956696 , trending: 0.535566962490517 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 1 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.175996 2.148141 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.974056708436821 , trending: 0.744214575272452 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and neither they nor the parameters exhibit a significant trend. Reducin > test-EGMME-simple.R: g gain. > test-EGMME-simple.R: Approximate standard error of the estimate: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: 0.3104041 0.5135670 > test-EGMME-simple.R: Approximate standard error of window means: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: 0.01014973 0.01687378 > test-EGMME-simple.R: par. var. / (std. var. + par. var.): > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: 0.001068046 0.001078356 > test-EGMME-simple.R: Local nonlinearity p-value: 0.720167289327714 > test-EGMME-simple.R: EGMME does not appear to be estimated with sufficient prescision. Continuing. > test-EGMME-simple.R: ======== Subphase 4 ======== > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.231970 2.259602 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.982532758325356 , trending: 0.605756452659413 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 4 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.185316 2.237948 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.974448108052989 , trending: 0.545326240721661 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 3 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.244654 2.173530 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.936352885655037 , trending: 0.968030111947793 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 2 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.138225 2.248962 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.995431953517572 , trending: 0.433599476477221 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend. 1 / 5 to go. > test-EGMME-simple.R: Running stochastic optimization... > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: New parameters: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.153525 2.245627 > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.986594407698035 , trending: 0.478569355659096 . > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and neither they nor the parameters exhibit a significant trend. Reducing gain. > test-EGMME-simple.R: Approximate standard error of the estimate: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: 0.3338714 0.4219504 > test-EGMME-simple.R: Approximate standard error of window means: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: 0.01006127 0.01301498 > test-EGMME-simple.R: par. var. / (std. var. + par. var.): > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: 0.0009073040 0.0009504983 > test-EGMME-simple.R: Local nonlinearity p-value: 0.778429922252107 > test-EGMME-simple.R: Maximum number of gain levels exceeded. Stopping.Refining the estimate using the mean method. New estimate: > test-EGMME-simple.R: Form~edges Persist~edges > test-EGMME-simple.R: -5.171135 2.188753 > test-EGMME-simple.R: ======== Phase 3: Simulate from the fit and estimate standard errors. ======== > test-EGMME-simple.R: Evaluating target statistics at the estimate. > test-EGMME-simple.R: Running stochastic optimization... > test-durational-terms.R: Starting simulated annealing (SAN) > test-durational-terms.R: Iteration 1 of at most 4 > test-durational-terms.R: Finished simulated annealing > test-EGMME-simple.R: Finished. Extracting. > test-EGMME-simple.R: Finished. > test-EGMME-simple.R: Estimating equation = 0 p-value: 0.798393159978643 > test-EGMME-simple.R: Maximum number of gain levels exceeded. Stopping. > test-EGMME-simple.R: Call: > test-EGMME-simple.R: tergm(formula = g1 ~ Form(~edges) + Persist(~edges), constraints = ~., > test-EGMME-simple.R: target.stats = target.stats[-3], estimate = "EGMME", control = control.tergm(SA.plot.progress = do.plot, > test-EGMME-simple.R: SA.phase2.levels.min = 2, SA.phase2.levels.max = 4, SA.phase2.repeats = 10, > test-EGMME-simple.R: SA.restart.on.err = FALSE, init = c(-log(0.95/0.05), > test-EGMME-simple.R: 1)), verbose = TRUE, targets = ~edges + mean.age) > test-EGMME-simple.R: > test-EGMME-simple.R: Gradient Descent Equilibrium Generalized Method of Moments Results Results: > test-EGMME-simple.R: > test-EGMME-simple.R: Estimate Std. Error MCMC % z value Pr(>|z|) > test-EGMME-simple.R: Form~edges -5.1711 0.3348 0 -15.447 <1e-04 *** > test-EGMME-simple.R: Persist~edges 2.1888 0.4104 0 5.333 <1e-04 *** > test-EGMME-simple.R: --- > test-EGMME-simple.R: Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > test-EGMME-simple.R: > test-EGMME-simple.R: Sample statistics summary: > test-EGMME-simple.R: > test-EGMME-simple.R: Iterations = 21:5270 > test-EGMME-simple.R: Thinning interval = 1 > test-EGMME-simple.R: Number of chains = 1 > test-EGMME-simple.R: Sample size per chain = 5250 > test-EGMME-simple.R: > test-EGMME-simple.R: 1. Empirical mean and standard deviation for each variable, > test-EGMME-simple.R: plus standard error of the mean: > test-EGMME-simple.R: > test-EGMME-simple.R: Mean SD Naive SE Time-series SE > test-EGMME-simple.R: edges 0.04171 3.150 0.04348 0.1888 > test-EGMME-simple.R: mean.age -0.08878 3.178 0.04385 0.1920 > test-EGMME-simple.R: > test-EGMME-simple.R: 2. Quantiles for each variable: > test-EGMME-simple.R: > test-EGMME-simple.R: 2.5% 25% 50% 75% 97.5% > test-EGMME-simple.R: edges -6.00 -2.0 0.0000 2.000 7 > test-EGMME-simple.R: mean.age -5.49 -2.2 -0.4286 1.778 7 > test-EGMME-simple.R: > test-EGMME-simple.R: > test-EGMME-simple.R: Are sample statistics significantly different from observed? > test-EGMME-simple.R: edges mean.age (Omni) > test-EGMME-simple.R: diff. 0.04171429 -0.08877985 NA > test-EGMME-simple.R: test stat. 0.22090947 -0.46228617 0.4520099 > test-EGMME-simple.R: P-val. 0.82516293 0.64387611 0.7983932 > test-EGMME-simple.R: > test-EGMME-simple.R: Sample statistics cross-correlations: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: edges 1.00000000 -0.03095688 > test-EGMME-simple.R: mean.age -0.03095688 1.00000000 > test-EGMME-simple.R: > test-EGMME-simple.R: Sample statistics auto-correlation: > test-EGMME-simple.R: Chain 1 > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: Lag 0 1.0000000 1.0000000 > test-EGMME-simple.R: Lag 1 0.8992875 0.8840140 > test-EGMME-simple.R: Lag 2 0.8095137 0.7907080 > test-EGMME-simple.R: Lag 3 0.7277822 0.7070889 > test-EGMME-simple.R: Lag 4 0.6590848 0.6408322 > test-EGMME-simple.R: Lag 5 0.5975276 0.5821078 > test-EGMME-simple.R: > test-EGMME-simple.R: Sample statistics burn-in diagnostic (Geweke): > test-EGMME-simple.R: Chain 1 > test-EGMME-simple.R: > test-EGMME-simple.R: Fraction in 1st window = 0.1 > test-EGMME-simple.R: Fraction in 2nd window = 0.5 > test-EGMME-simple.R: > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: -0.3064371 -0.3217347 > test-EGMME-simple.R: > test-EGMME-simple.R: Individual P-values (lower = worse): > test-EGMME-simple.R: edges mean.age > test-EGMME-simple.R: 0.7592719 0.7476537 > test-EGMME-simple.R: Joint P-value (lower = worse): 0.943036 > test-EGMME-simple.R: > test-EGMME-simple.R: Note: MCMC diagnostics shown here are from the last round of > test-EGMME-simple.R: simulation, prior to computation of final parameter estimates. > test-EGMME-simple.R: Because the final estimates are refinements of those used for this > test-EGMME-simple.R: simulation run, these diagnostics may understate model performance. > test-EGMME-simple.R: To directly assess the performance of the final model on in-model > test-EGMME-simple.R: statistics, please use the GOF command: gof(ergmFitObject, > test-EGMME-simple.R: GOF=~model). > test-EGMME-simple.R: > test-edgelist_with_lasttoggle.R: Starting simulated annealing (SAN) > test-edgelist_with_lasttoggle.R: Iteration 1 of at most 4 > test-edgelist_with_lasttoggle.R: Finished simulated annealing > test-edgelist_with_lasttoggle.R: Starting simulated annealing (SAN) > test-edgelist_with_lasttoggle.R: Iteration 1 of at most 4 > test-edgelist_with_lasttoggle.R: Finished simulated annealing > test-edgelist_with_lasttoggle.R: Starting simulated annealing (SAN) > test-edgelist_with_lasttoggle.R: Iteration 1 of at most 4 > test-edgelist_with_lasttoggle.R: Finished simulated annealing > test-edgelist_with_lasttoggle.R: Starting simulated annealing (SAN) > test-edgelist_with_lasttoggle.R: Iteration 1 of at most 4 > test-edgelist_with_lasttoggle.R: Finished simulated annealing > test-edgelist_with_lasttoggle.R: Starting simulated annealing (SAN) > test-edgelist_with_lasttoggle.R: Iteration 1 of at most 4 > test-edgelist_with_lasttoggle.R: Finished simulated annealing > test-edgelist_with_lasttoggle.R: Starting simulated annealing (SAN) > test-edgelist_with_lasttoggle.R: Iteration 1 of at most 4 > test-edgelist_with_lasttoggle.R: Finished simulated annealing > test-durational-terms.R: Starting simulated annealing (SAN) > test-durational-terms.R: Iteration 1 of at most 4 > test-durational-terms.R: Finished simulated annealing > test-lasttoggle.R: Starting simulated annealing (SAN) > test-lasttoggle.R: Iteration 1 of at most 4 > test-lasttoggle.R: Finished simulated annealing > test-lasttoggle.R: Starting simulated annealing (SAN) > test-lasttoggle.R: Iteration 1 of at most 4 > test-lasttoggle.R: Finished simulated annealing > test-networkLite.R: Loading required package: networkLite > test-durational-terms.R: Starting simulated annealing (SAN) > test-durational-terms.R: Iteration 1 of at most 4 > test-durational-terms.R: Finished simulated annealing > test-networkLite.R: Starting simulated annealing (SAN) > test-networkLite.R: Iteration 1 of at most 4 > test-networkLite.R: Finished simulated annealing > test-nwelt.R: Edge activity in base.net was ignored > test-nwelt.R: Created net.obs.period to describe network > test-nwelt.R: Network observation period info: > test-nwelt.R: Number of observation spells: 1 > test-nwelt.R: Maximal time range observed: -Inf until Inf > test-nwelt.R: Temporal mode: discrete > test-nwelt.R: Time unit: step > test-nwelt.R: Suggested time increment: 1 > test-nwelt.R: Edge activity in base.net was ignored > test-nwelt.R: Created net.obs.period to describe network > test-nwelt.R: Network observation period info: > test-nwelt.R: Number of observation spells: 1 > test-nwelt.R: Maximal time range observed: -Inf until Inf > test-nwelt.R: Temporal mode: discrete > test-nwelt.R: Time unit: step > test-nwelt.R: Suggested time increment: 1 > test-nwelt.R: Edge activity in base.net was ignored > test-nwelt.R: Created net.obs.period to describe network > test-nwelt.R: Network observation period info: > test-nwelt.R: Number of observation spells: 1 > test-nwelt.R: Maximal time range observed: -Inf until Inf > test-nwelt.R: Temporal mode: discrete > test-nwelt.R: Time unit: step > test-nwelt.R: Suggested time increment: 1 > test-nwelt.R: Created net.obs.period to describe network > test-nwelt.R: Network observation period info: > test-nwelt.R: Number of observation spells: 1 > test-nwelt.R: Maximal time range observed: 1 until Inf > test-nwelt.R: Temporal mode: discrete > test-nwelt.R: Time unit: step > test-nwelt.R: Suggested time increment: 1 > test-simulate.R: simulate.tergm test(s) skipped. Set ENABLE_statnet_TESTS environment variable to run. > test-term-EdgeAges.R: Starting simulated annealing (SAN) > test-term-EdgeAges.R: Iteration 1 of at most 4 > test-term-EdgeAges.R: Finished simulated annealing > test-term-EdgeAges.R: Starting simulated annealing (SAN) > test-term-EdgeAges.R: Iteration 1 of at most 4 > test-term-EdgeAges.R: Finished simulated annealing > test-term-EdgeAges.R: Starting simulated annealing (SAN) > test-term-EdgeAges.R: Iteration 1 of at most 4 > test-term-EdgeAges.R: Finished simulated annealing > test-term-EdgeAges.R: Starting simulated annealing (SAN) > test-term-EdgeAges.R: Iteration 1 of at most 4 > test-term-EdgeAges.R: Finished simulated annealing > test-term-EdgeAges.R: Starting simulated annealing (SAN) > test-term-EdgeAges.R: Iteration 1 of at most 4 > test-term-EdgeAges.R: Finished simulated annealing > test-term-EdgeAges.R: Starting simulated annealing (SAN) > test-term-EdgeAges.R: Iteration 1 of at most 4 > test-term-EdgeAges.R: Finished simulated annealing [ FAIL 1 | WARN 11 | SKIP 0 | PASS 4189 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-CMLE-bip.R:12:1'): Autodetect CMPLE on partially observed bipartite undirected network ── Error in `file()`: cannot open the connection Backtrace: ▆ 1. └─tergm::tergm(...) at ./helper-CMLE.R:408:5 2. └─tergm:::tergm.CMLE(...) 3. └─ergm::ergm(formula, ..., control = control$CMLE.ergm, basis = nw) 4. └─ergm:::ergm.fit(...) 5. └─ergm:::ergm.mple(...) 6. └─quietly(function() glm(pl$zy ~ . - 1 + offset(pl$foffset), data = data.frame(pl$xmat), ... 7. └─purrr:::capture_output(.f(...)) 8. └─base::file() [ FAIL 1 | WARN 11 | SKIP 0 | PASS 4189 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-debian-gcc