Description Usage Arguments Value References See Also
ergm.bridge.llr
uses bridge sampling with geometric spacing to
estimate the difference between the loglikelihoods 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 loglikelihood of configuration θ
relative to the reference measure. That is, the
configuration with θ=0 is defined as having loglikelihood of
0.
ergm.bridge.dindstart.llk
is a wrapper that uses a
dyadindependent ERGM as a starting point for bridge sampling to
estimate the loglikelihood for a given dyaddependent model and
parameter configuration. Note that it only handles binary ERGMs
(response=NULL
) and with constraints (constraints=
) that that
do not induce dyadic dependence.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42  ergm.bridge.llr(
object,
response = NULL,
reference = ~Bernoulli,
constraints = ~.,
from,
to,
obs.constraints = ~.  observed,
target.stats = NULL,
basis = ergm.getnetwork(object),
verbose = FALSE,
...,
llronly = FALSE,
control = control.ergm.bridge()
)
ergm.bridge.0.llk(
object,
response = NULL,
reference = ~Bernoulli,
coef,
...,
llkonly = TRUE,
control = control.ergm.bridge(),
basis = ergm.getnetwork(object)
)
ergm.bridge.dindstart.llk(
object,
response = NULL,
constraints = ~.,
coef,
obs.constraints = ~.  observed,
target.stats = NULL,
dind = NULL,
coef.dind = NULL,
basis = ergm.getnetwork(object),
...,
llkonly = TRUE,
control = control.ergm.bridge(),
verbose = FALSE
)

object 
A model formula. See 
response 
Either a character string, a formula, or

reference 
A onesided formula specifying the reference
measure (h(y)) to be used. (Defaults to

constraints, obs.constraints 
Onesided formulas specifying
one or more constraints on the support of the distribution of the
networks being simulated and on the observation process
respectively. See the documentation for a similar argument for

from, to 
The initial and final parameter vectors. 
target.stats 
A vector of sufficient statistics to be used in place of those of the network in the formula. 
basis 
An optional 
verbose 
A logical or an integer to control the amount of
progress and diagnostic information to be printed. 
... 
Further arguments to 
llronly 
Logical: If TRUE, only the estiamted logratio will
be returned by 
control 
A list of control parameters for algorithm tuning,
typically constructed with 
coef 
A vector of coefficients for the configuration of interest. 
llkonly 
Whether only the estiamted loglikelihood should be
returned by the 
dind 
A onesided formula with the dyadindependent model to use as a
starting point. Defaults to the dyadindependent terms found in the formula

coef.dind 
Parameter configuration for the dyadindependent starting
point. Defaults to the MLE of 
If llronly=TRUE
or llkonly=TRUE
, these functions return
the scalar loglikelihoodratio or the loglikelihood.
Otherwise, they return a list with the following components:
llr 
The estimated logratio. 
llr.vcov 
The estimated variance of the logratio due to MCMC approximation. 
llrs 
A list of lists (1 per attempt) of the estimated
logratios for each of the 
llrs.vcov 
A list of lists (1 per attempt) of the estimated
variances of the estimated logratios for each of the

paths 
A list of lists (1 per attempt) with two elements:

Dtheta.Du 
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 loglikelihood itself (with the reference
distribution assumed to have likelihood 0).
ergm.bridge.dindstart.llk
result list also includes
an llk
element, with the loglikelihood itself and an
llk.dind
element, with the loglikelihood of the nearest
dyadindependent model.
Hunter, D. R. and Handcock, M. S. (2006) Inference in curved exponential family models for networks, Journal of Computational and Graphical Statistics.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.