Running 'valued_fit.R' [5s]
Running the tests in 'tests/valued_fit.R' failed.
Complete output:
  > #  File tests/valued_fit.R in package ergm.count, part of the Statnet suite
  > #  of packages for network analysis, http://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
  > #  http://statnet.org/attribution
  > #
  > #  Copyright 2008-2018 Statnet Commons
  > #######################################################################
  > library(ergm.count)
  Loading required package: statnet.common
  
  Attaching package: 'statnet.common'
  
  The following object is masked from 'package:base':
  
      order
  
  Loading required package: ergm
  Loading required package: network
  network: Classes for Relational Data
  Version 1.15 created on 2019-04-01.
  copyright (c) 2005, Carter T. Butts, University of California-Irvine
                      Mark S. Handcock, University of California -- Los Angeles
                      David R. Hunter, Penn State University
                      Martina Morris, University of Washington
                      Skye Bender-deMoll, University of Washington
   For citation information, type citation("network").
   Type help("network-package") to get started.
  
  
  ergm: version 3.9.4, created on 2018-08-15
  Copyright (c) 2018, Mark S. Handcock, University of California -- Los Angeles
                      David R. Hunter, Penn State University
                      Carter T. Butts, University of California -- Irvine
                      Steven M. Goodreau, University of Washington
                      Pavel N. Krivitsky, University of Wollongong
                      Martina Morris, University of Washington
                      with contributions from
                      Li Wang
                      Kirk Li, University of Washington
                      Skye Bender-deMoll, University of Washington
  Based on "statnet" project software (statnet.org).
  For license and citation information see statnet.org/attribution
  or type citation("ergm").
  
  NOTE: Versions before 3.6.1 had a bug in the implementation of the bd()
  constriant which distorted the sampled distribution somewhat. In
  addition, Sampson's Monks datasets had mislabeled vertices. See the
  NEWS and the documentation for more details.
  
  
  Attaching package: 'ergm'
  
  The following objects are masked from 'package:statnet.common':
  
      colMeans.mcmc.list, sweep.mcmc.list
  
  
  ergm.count: version 3.3.0, created on 2018-08-25
  Copyright (c) 2018, Pavel N. Krivitsky, University of Wollongong
                      with contributions from
                      Mark S. Handcock, University of California -- Los Angeles
                      David R. Hunter, Penn State University
  Based on "statnet" project software (statnet.org).
  For license and citation information see statnet.org/attribution
  or type citation("ergm.count").
  
  NOTE: The form of the term 'CMP' has been changed in version 3.2 of
  'ergm.count'. See the news or help('CMP') for more information.
  
  > set.seed(0)
  > 
  > ## Poisson-reference ERGM
  > 
  > n <- 5
  > 
  > m <- matrix(rpois(n^2,2),n,n)
  > diag(m) <- 0
  > y <- as.network(m, matrix.type="a", directed=TRUE, ignore.eval=FALSE, names.eval="w")
  > 
  > truth <- log(sum(m)/n/(n-1))
  > diag(m) <- NA
  > 
  > efit <- ergm(y ~ sum, response="w", reference=~Poisson, verbose=TRUE, control=control.ergm(MCMLE.effectiveSize=128))
  Evaluating network in model.
  Initializing Metropolis-Hastings proposal(s): ergm.count:MH_PoissonTNT
  Initializing model.
  Using initial method 'CD'.
  Fitting initial model.
  Starting contrastive divergence estimation via CD-MCMLE:
  Iteration 1 of at most 60 with parameter: 
  sum 
    0 
  Sampler accepted  68.518% of 8192 proposed steps.
  Sample size = 1024 by 1024.
  Back from unconstrained CD.
  Average estimating function values:
         sum 
  -0.2275391 
  Convergence test P-value:2e-02
  Calling CD-MCMLE Optimization...
  Using Newton-Raphson Step with step length  1  ...
  Using naive metric (see control.ergm function).
  Optimizing loglikelihood
  The log-likelihood improved by 0.002669
  Iteration 2 of at most 60 with parameter: 
         sum 
  0.02348984 
  Sampler accepted  68.982% of 8192 proposed steps.
  Sample size = 1024 by 1024.
  Back from unconstrained CD.
  Average estimating function values:
        sum 
  0.3369141 
  Convergence test P-value:4.3e-04
  Calling CD-MCMLE Optimization...
  Using Newton-Raphson Step with step length  1  ...
  Using naive metric (see control.ergm function).
  Optimizing loglikelihood
  The log-likelihood improved by 0.006127
  Iteration 3 of at most 60 with parameter: 
          sum 
  -0.01296802 
  Sampler accepted  70.129% of 8192 proposed steps.
  Sample size = 1024 by 1024.
  Back from unconstrained CD.
  Average estimating function values:
      sum 
  0.53125 
  Convergence test P-value:5e-08
  Calling CD-MCMLE Optimization...
  Using Newton-Raphson Step with step length  1  ...
  Using naive metric (see control.ergm function).
  Optimizing loglikelihood
  The log-likelihood improved by 0.01473
  Iteration 4 of at most 60 with parameter: 
          sum 
  -0.06840481 
  Sampler accepted  70.020% of 8192 proposed steps.
  Sample size = 1024 by 1024.
  Back from unconstrained CD.
  Average estimating function values:
        sum 
  0.2861328 
  Convergence test P-value:4.1e-03
  Calling CD-MCMLE Optimization...
  Using Newton-Raphson Step with step length  1  ...
  Using naive metric (see control.ergm function).
  Optimizing loglikelihood
  The log-likelihood improved by 0.004036
  Iteration 5 of at most 60 with parameter: 
          sum 
  -0.09654827 
  Sampler accepted  70.593% of 8192 proposed steps.
  Sample size = 1024 by 1024.
  Back from unconstrained CD.
  Average estimating function values:
        sum 
  0.3779297 
  Convergence test P-value:1.1e-04
  Calling CD-MCMLE Optimization...
  Using Newton-Raphson Step with step length  1  ...
  Using naive metric (see control.ergm function).
  Optimizing loglikelihood
  The log-likelihood improved by 0.007285
  Iteration 6 of at most 60 with parameter: 
         sum 
  -0.1349567 
  Sampler accepted  67.407% of 8192 proposed steps.
  Sample size = 1024 by 1024.
  Back from unconstrained CD.
  Average estimating function values:
        sum 
  -0.546875 
  Convergence test P-value:3.3e-08
  Calling CD-MCMLE Optimization...
  Using Newton-Raphson Step with step length  1  ...
  Using naive metric (see control.ergm function).
  Optimizing loglikelihood
  The log-likelihood improved by 0.01517
  Iteration 7 of at most 60 with parameter: 
          sum 
  -0.07941636 
  Sampler accepted  70.728% of 8192 proposed steps.
  Sample size = 1024 by 1024.
  Back from unconstrained CD.
  Average estimating function values:
        sum 
  0.1630859 
  Convergence test P-value:9.2e-02
  Calling CD-MCMLE Optimization...
  Using Newton-Raphson Step with step length  1  ...
  Using naive metric (see control.ergm function).
  Optimizing loglikelihood
  The log-likelihood improved by 0.001389
  Iteration 8 of at most 60 with parameter: 
          sum 
  -0.09643703 
  Sampler accepted  69.470% of 8192 proposed steps.
  Sample size = 1024 by 1024.
  Back from unconstrained CD.
  Average estimating function values:
         sum 
  0.07421875 
  Convergence test P-value:4.6e-01
  Calling CD-MCMLE Optimization...
  Using Newton-Raphson Step with step length  1  ...
  Using naive metric (see control.ergm function).
  Optimizing loglikelihood
  The log-likelihood improved by 0.0002628
  Iteration 9 of at most 60 with parameter: 
         sum 
  -0.1035182 
  Sampler accepted  70.703% of 8192 proposed steps.
  Sample size = 1024 by 1024.
  Back from unconstrained CD.
  Average estimating function values:
          sum 
  -0.02441406 
  Convergence test P-value:8e-01
  Convergence detected. Stopping.
  Calling CD-MCMLE Optimization...
  Using Newton-Raphson Step with step length  1  ...
  Using naive metric (see control.ergm function).
  Optimizing loglikelihood
  The log-likelihood improved by < 0.0001
  Starting Monte Carlo maximum likelihood estimation (MCMLE):
  Density guard set to 10000 from an initial count of 19 edges.
  
  Iteration 1 of at most 20 with parameter:
         sum 
  -0.1010662 
  Starting unconstrained MCMC...
  Error in NextMethod("[") : argument is missing, with no default
  Calls: ergm ... FUN -> lapply -> lapply -> FUN -> [.mcmc -> NextMethod
  Execution halted