ergm.estfun: Compute the Sample Estimating Function Values of an ERGM.

In ergm: Fit, Simulate and Diagnose Exponential-Family Models for Networks

Description

The estimating function for an ERGM is the score function: the gradient of the log-likelihood, equalling η'(θ)^\top \{g(y)-μ(θ)\}, where g(y) is a p-vector of observed network sufficient statistic, μ(θ) is the expected value of the sufficient statistic under the model for parameter value θ, and η'(θ) 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.

Usage

ergm.estfun(stats, theta, model, ...)

## S3 method for class 'numeric'
ergm.estfun(stats, theta, model, ...)

## S3 method for class 'matrix'
ergm.estfun(stats, theta, model, ...)

## S3 method for class 'mcmc'
ergm.estfun(stats, theta, model, ...)

## S3 method for class 'mcmc.list'
ergm.estfun(stats, theta, model, ...)

Arguments

stats	An object representing sample statistics with observed values subtracted out.
theta	Model parameter q-vector.
model	An ergm_model object or its etamap element.
...	Additional arguments for methods.

Value

An object of the same class as stats containing q-vectors of estimating function values.

Methods (by class)

• numeric: Method for numeric vectors of length p.

• matrix: Method for matrices with p columns.

• mcmc: Method for mcmc objects with p variables.

• mcmc.list: Method for mcmc.list objects with p variables.

ergm documentation built on June 21, 2021, 9:07 a.m.