Description Usage Arguments Value Functions References See Also Examples
A function to return the loglikelihood associated with an
ergm
fit, evaluating it if
necessary. If the loglikelihood was not computed for
object
, produces an error unless eval.loglik=TRUE
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  ## S3 method for class 'ergm'
logLik(
object,
add = FALSE,
force.reeval = FALSE,
eval.loglik = add  force.reeval,
control = control.logLik.ergm(),
...
)
## S3 method for class 'ergm'
deviance(object, ...)
## S3 method for class 'ergm'
AIC(object, ..., k = 2)
## S3 method for class 'ergm'
BIC(object, ...)

object 
An 
add 
Logical: If 
force.reeval 
Logical: If 
eval.loglik 
Logical: If 
control 
A list of control parameters for algorithm tuning,
typically constructed with 
... 
Other arguments to the likelihood functions. 
k 
see help for 
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 loglikelihood
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.)
deviance.ergm
: A deviance()
method.
AIC.ergm
: An AIC()
method.
BIC.ergm
: A BIC()
method.
Hunter, D. R. and Handcock, M. S. (2006) Inference in curved exponential family models for networks, Journal of Computational and Graphical Statistics.
logLik
, logLikNull
, ergm.bridge.llr
,
ergm.bridge.dindstart.llk
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  # 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))
# Loglikelihood is not evaluated, so no deviance, AIC, or BIC:
summary(gest)
# Evaluate the loglikelihood 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)

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