Auxiliary function as user interface for ergmm prior specification. Typically only used when calling ergmm. It is used to supply the parameters of the prior distribution of the model, to overwrite those specified in the model formula, and to supply miscellaneous prior parameters.
simulate
(Package: latentnet) :
Draw from the distribution of an Exponential Random Graph Mixed Model
If passed a ergmm fit object, simulate is used to simulate networks from the posterior of an exponetial random graph mixed model fit. Alternatively, a ergmm.model object can be passed to simulate based on a particular parametr configuration.
The package latentnet is used to fit latent cluster random effect models, where the probability of a network g, on a set of nodes is a product of dyad probabilities, each of which is a GLM with linear component η_{i,j}=∑_{k=1}^p β_k X_{i,j,k}+d(Z_i,Z_j)+δ_i+γ_j, where X is an array of dyad covariates, β is a vector of covariate coefficients, Z_i is the latent space position of node i, d(cdot,cdot) is a function of the two positions: either negative Euclidean (-||Z_i-Z_j||) or bilinear (Z_icdot Z_j), and δ and γ are vectors of sender and receiver effects. (Note that these are different from the eigenmodel of Hoff (2007) “Modeling homophily and stochastic equivalence in symmetric relational data”, fit by package eigenmodel.)
merge.ergmm prodcues a ergmm object containing the combined MCMC output (and derived estimates) of several ergmm objects produced with the same input parameters but different starting values, random seeds, etc..
Auxiliary function as user interface for ergmm fitting. Typically only used when calling ergmm. It is used to set parameters that affect the sampling but do not affect the posterior distribution.
gof
(Package: latentnet) :
Conduct Goodness-of-Fit Diagnostics on a Exponential Family
gof calculates p-values for geodesic distance, degree, and reachability summaries to diagnose the goodness-of-fit of exponential family random graph mixed models. See ergmm for more information on these models.
This function creates simple diagnostic plots for the MCMC sampled statistics produced from a fit. It also prints the Raftery-Lewis diagnostics, indicates if they are sufficient, and suggests the run length required.