ergm.bridge.llr uses bridge sampling with geometric spacing to
estimate the difference between the log-likelihoods of two parameter vectors
for an ERGM via repeated calls to simulate.formula.ergm.
ergm.bridge.0.llk is a convenience wrapper that
returns the log-likelihood of configuration \(\theta\)
relative to the reference measure. That is, the
configuration with \(\theta=0\) is defined as having log-likelihood of
0.
ergm.bridge.dindstart.llk is a wrapper 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. Note that it only handles binary ERGMs
(response=NULL) and with constraints (constraints=) that that
do not induce dyadic dependence.
ergm.bridge.llr(object, response = NULL, constraints = ~., from, to,
basis = NULL, verbose = FALSE, ..., llronly = FALSE,
control = control.ergm.bridge())ergm.bridge.0.llk(object, response = response, constraints = ~., coef,
..., llkonly = TRUE, control = control.ergm.bridge())
ergm.bridge.dindstart.llk(object, response = NULL, constraints = ~.,
coef, dind = NULL, coef.dind = NULL, basis = NULL, ...,
llkonly = TRUE, control = control.ergm.bridge())
A model formula. See ergm for details.
Name of the edge attribute whose value is to be
modeled in the valued ERGM framework. Defaults to NULL for
simple presence or absence, modeled via a binary ERGM.
A one-sided formula specifying one or more
constraints on the support of the distribution of the networks
being simulated. See the documentation for a similar argument for
ergm for more information.
The initial and final parameter vectors.
An optional network object to
start the Markov chain. If omitted, the default is the
left-hand-side of the object.
Logical: If TRUE, print detailed information.
Further arguments to ergm.bridge.llr and
simulate.formula.ergm.
Logical: If TRUE, only the estiamted log-ratio will
be returned by ergm.bridge.llr.
Control arguments. See
control.ergm.bridge for details.
A vector of coefficients for the configuration of interest.
Whether only the estiamted log-likelihood should be
returned by the ergm.bridge.0.llk and
ergm.bridge.dindstart.llk. (Defaults to TRUE.)
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.
Parameter configuration for the dyad-independent starting
point. Defaults to the MLE of dind.
If llronly=TRUE or llkonly=TRUE, these functions return
the scalar log-likelihood-ratio or the log-likelihood.
Otherwise, they return a list with the following components:
The estimated log-ratio.
The estimated
log-ratios for each of the nsteps bridges.
A numeric matrix with nsteps rows, with each row being the respective bridge's parameter configuration.
A numeric matrix with nsteps rows, with each row being the respective bridge's vector of simulated statistics.
The gradient vector of the parameter values with respect to position of the bridge.
ergm.bridge.0.llk result list also includes an llk element, with the log-likelihood itself (with the reference distribution assumed to have likelihood 0).
ergm.bridge.dindstart.llk result list also includes an llk element, with the log-likelihood itself and an llk.dind element, with the log-likelihood of the nearest dyad-independent model.
Hunter, D. R. and Handcock, M. S. (2006) Inference in curved exponential family models for networks, Journal of Computational and Graphical Statistics.