R: A 'logLik' method for 'ergm' fits.

logLik.ergm {ergm}

R Documentation

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 class 'ergm'
logLik(
  object,
  add = FALSE,
  force.reeval = FALSE,
  eval.loglik = add || force.reeval,
  control = control.logLik.ergm(),
  ...,
  verbose = FALSE
)

## S3 method for class 'ergm'
deviance(object, ...)

## S3 method for class 'ergm'
AIC(object, ..., k = 2)

## S3 method for class 'ergm'
BIC(object, ...)

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()`.

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.)

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.

Examples


# See help(ergm) for a description of this model. The likelihood will
# not be evaluated.
data(florentine)
## Not run: 
# 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)

## End(Not run)

[Package ergm version 4.6.0 Index]

A logLik method for ergm fits.