ergm (version 3.6.1)

ergm.bridge.dindstart.llk: Bridge sampling to estiamte log-likelihood of an ERGM, using a dyad-independent ERGM as a staring point.

Description

This function is a wrapper around ergm.bridge.llr that uses a dyad-independent ERGM as a starting point for bridge sampling to estimate the log-likelihood for a given dyad-dependent model and parameter configuration. The dyad-independent model may be specified or can be chosen adaptively.

Usage

ergm.bridge.dindstart.llk(object,  response=NULL, constraints=~., coef,  dind=NULL,  coef.dind=NULL,   basis=NULL,  ...,  llkonly=TRUE,  control=control.ergm.bridge())

Arguments

object

A model formula. See ergm for details.

response

The name of the edge attribute that is the response. Note that it's included solely for consistency, since ergm.bridge.dindstart.llk can only handle binary ERGMs.

constraints

A model constraints formula. See ergm for details. Note that only constraints that do not induce dyadic dependence can be handled by ergm.bridge.dindstart.llk.

coef

A vector of coefficients for the configuration of interest.

dind

A one-sided formula with the dyad-independent model to use as a starting point. Defaults to the dyad-independent terms found in the formula object with an overal density term (edges) added if not redundant.

coef.dind

Parameter configuration for the dyad-independent starting point. Defaults to the MLE of dind.

basis

An optional network object to start the Markov chain. If omitted, the default is the left-hand-side of the object.

...

Further arguments to ergm.bridge.llr and simulate.formula.ergm.

llkonly

Whether only the estiamted log-likelihood should be returned. (Defaults to TRUE.)

control

Control parameters. See control.ergm.bridge.

Value

If llkonly=TRUE, returns the scalar log-likelihood. Otherwise, returns a copy of the list returned by ergm.bridge.llr in addition to the following components:

References

Hunter, D. R. and Handcock, M. S. (2006) Inference in curved exponential family models for networks, Journal of Computational and Graphical Statistics.

See Also

ergm.bridge.llr, simulate.formula.ergm