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> #  File tests/nomixing.R in package latentnet, 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 2003-2025 Statnet Commons
> ################################################################################
> library(latentnet)
Loading required package: network

'network' 1.19.0 (2024-12-08), 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

Loading required package: ergm

'ergm' 4.10.1 (2025-08-26), part of the Statnet Project
* 'news(package="ergm")' for changes since last version
* 'citation("ergm")' for citation information
* 'https://statnet.org' for help, support, and other information

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


'latentnet' 2.12.0 (2025-09-11), part of the Statnet Project
* 'news(package="latentnet")' for changes since last version
* 'citation("latentnet")' for citation information
* 'https://statnet.org' for help, support, and other information
NOTE: BIC calculation prior to latentnet 2.7.0 had a bug in the calculation of the effective number of parameters. See help(summary.ergmm) for details.
NOTE: Prior to version 2.8.0, handling of fixed effects for directed networks had a bug: the covariate matrix was transposed.
> 
> data(sampson)
> 
> badfit<-ergmm(samplike~euclidean(d=2,G=3)+rreceiver,control=ergmm.control(mle.maxit=3,burnin=0,interval=1,sample.size=1000,group.deltas=0,pilot.runs=0))
> 
> plot(badfit)
Warning message:
In cov2cor(beta.cov) :
  diag(V) had non-positive or NA entries; the non-finite result may be dubious
> 
> mcmc.diagnostics(badfit)
Chain 1 
WARNING: Variables beta.1 did not mix AT ALL. MCMC should be rerun with different proposal parameters!
Lag 0 
                   lpY beta.1      Z.1.1       Z.1.2 receiver.1
lpY         1.00000000    NaN 0.08341349 -0.01396633  0.4838438
beta.1             NaN    NaN        NaN         NaN        NaN
Z.1.1       0.08341349    NaN 1.00000000  0.03973312  0.0897795
Z.1.2      -0.01396633    NaN 0.03973312  1.00000000  0.2634467
receiver.1  0.48384380    NaN 0.08977950  0.26344669  1.0000000

Lag 1 
                  lpY beta.1      Z.1.1       Z.1.2 receiver.1
lpY        0.86875077    NaN 0.07363922 -0.02318558  0.4706296
beta.1            NaN    NaN        NaN         NaN        NaN
Z.1.1      0.08442509    NaN 0.94225096  0.04523029  0.1033282
Z.1.2      0.01191396    NaN 0.03547586  0.92652553  0.2633846
receiver.1 0.46597874    NaN 0.08141510  0.24379376  0.9016258

[[1]]

Quantile (q) = 0.025
Accuracy (r) = +/- 0.0125
Probability (s) = 0.95 
                                                  
            Burn-in  Total Lower bound  Dependence
            (M)      (N)   (Nmin)       factor (I)
 lpY        8        1358  600           2.26     
 beta.1     <NA>     <NA>  600             NA     
 Z.1.1      37       7614  600          12.70     
 Z.1.2      18       3074  600           5.12     
 receiver.1 21       3123  600           5.20     


> 
> proc.time()
   user  system elapsed 
   3.32    0.21    3.53