Bergm: bgof – R documentation

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bgof

Bayesian goodness-of-fit diagnostics for ERGMs

Description

Function to calculate summaries for degree, minimum geodesic distances, and edge-wise shared partner distributions to diagnose the Bayesian goodness-of-fit of exponential random graph models.

Usage

bgof(
  x,
  sample.size = 100,
  aux.iters = 10000,
  n.deg = NULL,
  n.dist = NULL,
  n.esp = NULL,
  n.ideg = NULL,
  n.odeg = NULL,
  ...
)

Arguments

`x`	an `R` object of class `bergm`.
`sample.size`	count; number of networks to be simulated and compared to the observed network.
`aux.iters`	count; number of iterations used for network simulation.
`n.deg`	count; used to plot only the first `n.deg`-1 degree distributions. By default no restrictions on the number of degree distributions is applied.
`n.dist`	count; used to plot only the first `n.dist`-1 geodesic distances distributions. By default no restrictions on the number of geodesic distances distributions is applied.
`n.esp`	count; used to plot only the first `n.esp`-1 edge-wise shared partner distributions. By default no restrictions on the number of edge-wise shared partner distributions is applied.
`n.ideg`	count; used to plot only the first `n.ideg`-1 in-degree distributions. By default no restrictions on the number of in-degree distributions is applied.
`n.odeg`	count; used to plot only the first `n.odeg`-1 out-degree distributions. By default no restrictions on the number of out-degree distributions is applied.
`...`	additional arguments, to be passed to lower-level functions.

References

Caimo, A. and Friel, N. (2011), "Bayesian Inference for Exponential Random Graph Models," Social Networks, 33(1), 41-55. https://arxiv.org/abs/1007.5192

Caimo, A. and Friel, N. (2014), "Bergm: Bayesian Exponential Random Graphs in R," Journal of Statistical Software, 61(2), 1-25. https://www.jstatsoft.org/article/view/v061i02

Examples

## Not run: 
# Load the florentine marriage network
data(florentine)

# Posterior parameter estimation:
p.flo <- bergm(flomarriage ~ edges + kstar(2),
               burn.in    = 50,
               aux.iters  = 500,
               main.iters = 1000,
               gamma      = 1.2)

# Bayesian goodness-of-fit test:
bgof(p.flo,
     aux.iters   = 500,
     sample.size = 30,
     n.deg       = 10,
     n.dist      = 9,
     n.esp       = 6)

## End(Not run)

Bergm

Bayesian Exponential Random Graph Models

v5.0.2

GPL (>= 2)

Authors

Alberto Caimo [aut, cre], Lampros Bouranis [aut], Robert Krause [aut] Nial Friel [ctb]

Initial release

2020-11-12