ergm (version 4.7.1)

logLik.ergm: A logLik() method for ergm fits.

Description

A function to return the log-likelihood associated with an ergm fit, evaluating it if necessary. If the log-likelihood was not computed for object, produces an error unless eval.loglik=TRUE.

Usage

# S3 method for ergm
logLik(
  object,
  add = FALSE,
  force.reeval = FALSE,
  eval.loglik = add || force.reeval,
  control = control.logLik.ergm(),
  ...,
  verbose = FALSE
)
# S3 method for ergm
deviance(object, ...)
# S3 method for ergm
AIC(object, ..., k = 2)
# S3 method for ergm
BIC(object, ...)

Value

The form of the output of logLik.ergm depends on add: add=FALSE (the default), a logLik object. If add=TRUE (the default), an ergm object with the log-likelihood set.

As of version 3.1, all likelihoods for which logLikNull is not implemented are computed relative to the reference measure. (I.e., a null model, with no terms, is defined to have likelihood of 0, and all other models are defined relative to that.)

Arguments

object

An ergm fit, returned by ergm().

add

Logical: If TRUE, instead of returning the log-likelihood, return object with log-likelihood value (and the null likelihood value) set.

force.reeval

Logical: If TRUE, reestimate the log-likelihood even if object already has an estiamte.

eval.loglik

Logical: If TRUE, evaluate the log-likelihood if not set on object.

control

A list of control parameters for algorithm tuning, typically constructed with control.logLik.ergm(). Its documentation gives the the list of recognized control parameters and their meaning. The more generic utility snctrl() (StatNet ConTRoL) also provides argument completion for the available control functions and limited argument name checking.

...

Other arguments to the likelihood functions.

verbose

A logical or an integer to control the amount of progress and diagnostic information to be printed. FALSE/0 produces minimal output, with higher values producing more detail. Note that very high values (5+) may significantly slow down processing.

k

see help for AIC().

Functions

• deviance(ergm): A deviance() method.

• AIC(ergm): An AIC() method.

• BIC(ergm): A BIC() method.

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

logLik(), logLikNull(), ergm.bridge.llr(), ergm.bridge.dindstart.llk()

Examples

# See help(ergm) for a description of this model. The likelihood will
# not be evaluated.
data(florentine)
if (FALSE) {
# The default maximum number of iterations is currently 20. We'll only
# use 2 here for speed's sake.
gest <- ergm(flomarriage ~ kstar(1:2) + absdiff("wealth") + triangle, eval.loglik=FALSE)

gest <- ergm(flomarriage ~ kstar(1:2) + absdiff("wealth") + triangle, eval.loglik=FALSE,
             control=control.ergm(MCMLE.maxit=2))
# Log-likelihood is not evaluated, so no deviance, AIC, or BIC:
summary(gest)
# Evaluate the log-likelihood and attach it to the object.

# The default number of bridges is currently 20. We'll only use 3 here
# for speed's sake.
gest.logLik <- logLik(gest, add=TRUE)

gest.logLik <- logLik(gest, add=TRUE, control=control.logLik.ergm(bridge.nsteps=3))
# Deviances, AIC, and BIC are now shown:
summary(gest.logLik)
# Null model likelihood can also be evaluated, but not for all constraints:
logLikNull(gest) # == network.dyadcount(flomarriage)*log(1/2)
}