ergmm-class: Class of Fitted Exponential Random Graph Mixed Models
In statnet/latentnet: Latent Position and Cluster Models for Statistical Networks

ergmm-class

R Documentation

Class of Fitted Exponential Random Graph Mixed Models

A class ergmm to represent a fitted exponential random graph mixed model. The output of ergmm().

There are methods summary.ergmm(), print.ergmm(), plot.ergmm(), predict.ergmm(), and as.mcmc.list.ergmm().

The structure of ergmm is as follows:

sample: An object of class ergmm.par.list containing the MCMC sample from the posterior. If the run had multiple threads, their output is concatenated.
mcmc.mle: A list containing the parameter configuration of the highest-likelihood MCMC iteration.
mcmc.pmode: A list containing the parameter configuration of the highest-joint-density (conditional on cluster assignments) MCMC iteration.
mkl: A list containing the MKL estimate.
model: A list containing the model that was fitted.
prior: A list containing the information about the prior distribution used. It can be passed as parameter prior to ergmm() to reproduce the prior in a new fit.
control: A list containing the information about the model fit settings that do not affect the posterior distribution. It can be passed as parameter control to ergmm() to reproduce control parameters in a new fit.
mle: A list containing the MLE, conditioned on cluster assignments.
pmode: A list containing the posterior mode, conditioned on cluster assignments.
burnin.start: A list containing the starting value for the burnin.
main.start: A list (or a list of lists, for a multithreaded run) containing the starting value for the sampling.