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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 2003-2024 Statnet Commons
> ################################################################################
> library(latentnet)
Loading required package: network

'network' 1.18.2 (2023-12-04), 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' 4.6.0 (2023-12-17), 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.


'latentnet' 2.11.0 (2024-02-19), 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         1.0000000    NaN -0.3297370  0.3604778  0.7030832
beta.1            NaN    NaN        NaN        NaN        NaN
Z.1.1      -0.3297370    NaN  1.0000000 -0.3838973 -0.3637549
Z.1.2       0.3604778    NaN -0.3838973  1.0000000  0.3879223
receiver.1  0.7030832    NaN -0.3637549  0.3879223  1.0000000

Lag 1 
                  lpY beta.1      Z.1.1      Z.1.2 receiver.1
lpY         0.9249059    NaN -0.3270760  0.3599640  0.6946515
beta.1            NaN    NaN        NaN        NaN        NaN
Z.1.1      -0.3380934    NaN  0.9437811 -0.3842102 -0.3736006
Z.1.2       0.3643801    NaN -0.3823173  0.9424039  0.3837067
receiver.1  0.6847733    NaN -0.3563086  0.3742702  0.9426249

[[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        16       2724  600           4.54     
 beta.1     <NA>     <NA>  600             NA     
 Z.1.1      21       3599  600           6.00     
 Z.1.2      34       5638  600           9.40     
 receiver.1 45       9109  600          15.20     


> 
> proc.time()
   user  system elapsed 
   3.96    0.32    4.26