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> #  File tests/binomial.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)
> 
> fit.1 <- ergmm(samplike ~ euclidean(2) + rreceiver, tofit = "mle")
> eta <- function(par){
+   o <- par$beta - as.matrix(dist(par$Z)) + outer(rep(1, 18), par$receiver)
+   diag(o) <- -Inf
+   o
+ }
> 
> trials <- matrix(1000 + rgeom(18*18, 1/1000), 18, 18)
> diag(trials) <- 0
> 
> Ym <- matrix(rbinom(18*18, trials, plogis(eta(fit.1$mle))), 18, 18)
> 
> Y <- as.network(Ym, ignore.eval = FALSE, names.eval = "a")
> 
> wrong.trials <- matrix(floor(runif(18*18, pmin(trials, Ym), trials*2 - pmin(trials, Ym) + 1)), 18, 18)
> 
> fit.mle <- ergmm(Y ~ euclidean(2) + rreceiver, response = "a", tofit = "mle", family = "binomial", fam.par = list(trials = trials))
> fit.mle.wrong <- ergmm(Y ~ euclidean(2) + rreceiver, response = "a", tofit = "mle", family = "binomial", fam.par = list(trials = wrong.trials))
> 
> stopifnot(sqrt(mean(c((eta(fit.1$mle) - eta(fit.mle$mle))^2), na.rm=TRUE)) < sqrt(mean(c((eta(fit.1$mle) - eta(fit.mle.wrong$mle))^2), na.rm=TRUE)))
> stopifnot(sqrt(mean(c((plogis(eta(fit.1$mle)) - plogis(eta(fit.mle$mle)))^2), na.rm=TRUE)) < sqrt(mean(c((plogis(eta(fit.1$mle)) - plogis(eta(fit.mle.wrong$mle)))^2), na.rm=TRUE)))
> 
> fit.mkl <- ergmm(Y ~ euclidean(2) + rreceiver, response = "a", tofit = "mkl", family = "binomial", fam.par = list(trials = trials))
Warning messages:
1: In backoff.check(model, burnin.sample, burnin.control) :
  Backing off: too few acceptances. If you see this message several times in a row, use a longer burnin.
2: In backoff.check(model, burnin.sample, burnin.control) :
  Backing off: too few acceptances. If you see this message several times in a row, use a longer burnin.
> fit.mkl.wrong <- ergmm(Y ~ euclidean(2) + rreceiver, response = "a", tofit = "mkl", family = "binomial", fam.par = list(trials = wrong.trials))
Warning messages:
1: In backoff.check(model, burnin.sample, burnin.control) :
  Backing off: too few acceptances. If you see this message several times in a row, use a longer burnin.
2: In backoff.check(model, burnin.sample, burnin.control) :
  Backing off: too few acceptances. If you see this message several times in a row, use a longer burnin.
3: In backoff.check(model, burnin.sample, burnin.control) :
  Backing off: too few acceptances. If you see this message several times in a row, use a longer burnin.
> 
> stopifnot(sqrt(mean(c((eta(fit.1$mle) - eta(fit.mkl$mkl))^2), na.rm=TRUE)) < sqrt(mean(c((eta(fit.1$mle) - eta(fit.mkl.wrong$mkl))^2), na.rm=TRUE)))
> stopifnot(sqrt(mean(c((plogis(eta(fit.1$mle)) - plogis(eta(fit.mkl$mkl)))^2), na.rm=TRUE)) < sqrt(mean(c((plogis(eta(fit.1$mle)) - plogis(eta(fit.mkl.wrong$mkl)))^2), na.rm=TRUE)))
> 
> proc.time()
   user  system elapsed 
  35.20    0.34   35.53