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

Description Usage Arguments Value Methods (by class)

View source: R/ergm_estfun.R

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

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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)


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