evidenceCJ {Bergm} | R Documentation |

Function to estimate the evidence (marginal likelihood) with Chib and Jeliazkov's method, based on the adjusted pseudolikelihood function.

```
evidenceCJ(
formula,
prior.mean = NULL,
prior.sigma = NULL,
aux.iters = 1000,
n.aux.draws = 5,
aux.thin = 50,
ladder = 30,
main.iters = 30000,
burn.in = 5000,
thin = 1,
V.proposal = 1.5,
num.samples = 25000,
seed = 1,
estimate = c("MLE", "CD"),
...
)
```

`formula` |
formula; an |

`prior.mean` |
vector; mean vector of the multivariate Normal prior. By default set to a vector of 0's. |

`prior.sigma` |
square matrix; variance/covariance matrix for the multivariate Normal prior. By default set to a diagonal matrix with every diagonal entry equal to 100. |

`aux.iters` |
count;
number of auxiliary iterations used for drawing the first network from the ERGM likelihood.
See |

`n.aux.draws` |
count;
number of auxiliary networks drawn from the ERGM likelihood.
See |

`aux.thin` |
count;
number of auxiliary iterations between network draws after the first network is drawn.
See |

`ladder` |
count; length of temperature ladder (>=3).
See |

`main.iters` |
count; number of MCMC iterations after burn-in for the adjusted pseudo-posterior estimation. |

`burn.in` |
count; number of burn-in iterations at the beginning of an MCMC run for the adjusted pseudo-posterior estimation. |

`thin` |
count; thinning interval used in the simulation for the adjusted pseudo-posterior estimation. The number of MCMC iterations must be divisible by this value. |

`V.proposal` |
count; diagonal entry for the multivariate Normal proposal. By default set to 1.5. |

`num.samples` |
integer;
number of samples used in the marginal likelihood estimate.
Must be lower than |

`seed` |
integer; seed for the random number generator.
See |

`estimate` |
If "MLE" (the default), then an approximate maximum likelihood estimator is returned. If "CD" , the Monte-Carlo contrastive divergence estimate is returned. See |

`...` |
additional arguments, to be passed to the ergm function.
See |

Caimo, A., & Friel, N. (2013). Bayesian model selection for exponential random graph models. Social Networks, 35(1), 11-24. https://arxiv.org/abs/1201.2337

Bouranis, L., Friel, N., & Maire, F. (2018). Bayesian model selection for exponential random graph models via adjusted pseudolikelihoods. Journal of Computational and Graphical Statistics, 27(3), 516-528. https://arxiv.org/abs/1706.06344

```
## Not run:
# Load the florentine marriage network:
data(florentine)
# MCMC sampling and evidence estimation:
CJE <- evidenceCJ(flomarriage ~ edges + kstar(2),
main.iters = 2000,
burn.in = 200,
aux.iters = 500,
num.samples = 25000,
V.proposal = 2.5)
# Posterior summaries:
summary(CJE)
# MCMC diagnostics plots:
plot(CJE)
# Log-evidence (marginal likelihood) estimate:
CJE$log.evidence
## End(Not run)
```

[Package *Bergm* version 5.0.7 Index]