The estimating function for an ERGM is the score function: the gradient of the log-likelihood, equalling \(\eta'(\theta)^\top \{g(y)-\mu(\theta)\}\), where \(g(y)\) is a \(p\)-vector of observed network sufficient statistic, \(\mu(\theta)\) is the expected value of the sufficient statistic under the model for parameter value \(\theta\), and \(\eta'(\theta)\) is the \(p\) by \(q\) Jacobian matrix of the mapping from curved parameters to natural parmeters. If the model is linear, all non-offset statistics are passed. If the model is curved, the score estimating equations (3.1) by Hunter and Handcock (2006) are given instead.
ergm.estfun(stats, theta, model, ...)# S3 method for matrix
ergm.estfun(stats, theta, model, ...)
# S3 method for mcmc
ergm.estfun(stats, theta, model, ...)
# S3 method for mcmc.list
ergm.estfun(stats, theta, model, ...)
An object representing sample statistics with observed values subtracted out.
Model parameter \(q\)-vector.
An ergm_model object or its etamap element.
Additional arguments for methods.
An object of the same class as stats containing
\(q\)-vectors of estimating function values.