logLik.ergm | R Documentation |
logLik
method for ergm
fits.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
.
## 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, ...)
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. |
verbose |
A logical or an integer to control the amount of
progress and diagnostic information to be printed. |
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 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.)
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
# 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)
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