# 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```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.