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, directed = FALSE, sample.size = 100, aux.iters = 10000,
n.deg = NULL, n.dist = NULL, n.esp = NULL, n.ideg = NULL,
n.odeg = NULL, ...)an R object of class bergm or calibrate.bergm.
logical; TRUE if the observed graph is directed.
count; number of networks to be simulated and compared to the observed network.
count; number of iterations used for network simulation.
count; used to plot only the first
n.deg-1 degree distributions.
By default no restrictions on the number of degree
distributions is applied.
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.
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.
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.
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.
Caimo, A. and Friel, N. (2011), "Bayesian Inference for Exponential Random Graph Models," Social Networks, 33(1), 41-55. http://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. jstatsoft.org/v61/i02