bergmC {Bergm} | R Documentation |

Function to transform a sample from the pseudo-posterior to one that is approximately sampled from the intractable posterior distribution.

```
bergmC(
formula,
prior.mean = NULL,
prior.sigma = NULL,
burn.in = 10000,
main.iters = 40000,
aux.iters = 3000,
V.proposal = 1.5,
thin = 1,
rm.iters = 500,
rm.a = 0.001,
rm.alpha = 0,
n.aux.draws = 400,
aux.thin = 50,
estimate = c("MLE", "CD"),
seed = 1,
...
)
```

`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. |

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

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

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

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

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

`rm.iters` |
count; number of iterations for the Robbins-Monro stochastic approximation algorithm. |

`rm.a` |
scalar; constant for sequence alpha_n (Robbins-Monro). |

`rm.alpha` |
scalar; noise added to gradient (Robbins-Monro). |

`n.aux.draws` |
count; number of auxiliary networks drawn from the ERGM likelihood (Robbins-Monro). See |

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

`estimate` |
If "MLE" (the default), then an approximate maximum likelihood estimator is used as a starting point in the Robbins-Monro algorithm. If "CD" , the Monte-Carlo contrastive divergence estimate is returned. See |

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

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

Bouranis, L., Friel, N., & Maire, F. (2017). Efficient Bayesian inference for exponential random graph models by correcting the pseudo-posterior distribution. Social Networks, 50, 98-108. https://arxiv.org/abs/1510.00934

```
## Not run:
# Load the florentine marriage network
data(florentine)
# Calibrated pseudo-posterior:
cpp.flo <- bergmC(flomarriage ~ edges + kstar(2),
aux.iters = 500,
burn.in = 500,
main.iters = 10000,
V.proposal = 2.5)
# Posterior summaries:
summary(cpp.flo)
## End(Not run)
```

[Package *Bergm* version 5.0.7 Index]