R: Compute the Sample Estimating Function Values of an ERGM.

ergm.estfun {ergm} | R 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 '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)

- `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.

[Package ergm version 3.10.4 Index]