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

View source: R/ergm_estfun.R

ergm.estfunR Documentation

Compute the Sample Estimating Function Values of an ERGM.

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 2, 2022, 1:07 a.m.