bgof function

Bayesian goodness-of-fit diagnostics for ERGMs

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.

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.

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)

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/v61/i02