This function extract the log-likelihood from the output of a
estimate call.
The extracted log-likelihood correspond to the value in the last
iteration of the estimate call, users should check convergence of
the Gauss/Fisher scoring method before using the log-likelihood statistic
to compare models.
# S3 method for result.goldfish
logLik(object, ..., avgPerEvent = FALSE)Returns an object of class logLik when avgPerEvent = FALSE.
This is a number with the extracted log-likelihood from the fitted model,
and with the following attributes:
degrees of freedom with the number of estimated parameters in the model
the number of observations used in estimation.
In general, it corresponds to the number of dependent events used in
estimation. For a subModel = "rate" or model = "REM" with intercept,
it corresponds to the number of dependent events plus right-censored
events due to exogenous or endogenous changes.
When avgPerEvent = TRUE, the function returns a number with the average
log-likelihood per event. The total number of events depends on the presence
of right-censored events in a similar way that the attribute nobs
is computed when avgPerEvent = FALSE.
an object of class result.goldfish output from an
estimate call with a fitted model.
additional arguments to be passed.
a logical value indicating whether the average likelihood per event should be calculated.
Users might use stats::AIC() and stats::BIC() to compute the Information
Criteria from one or several fitted model objects.
An information criterion could be used to compare models
with respect to their predictive power.
Alternatively, lmtest::lrtest() can be used to compare models via
asymptotic likelihood ratio tests. The test is designed to compare nested
models. i.e., models where the model specification of one contains a subset
of the predictor variables that define the other.