| logLik.result.goldfish {goldfish} | R Documentation |
Extract log-likelihood from a fitted model object
Description
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.
Usage
## S3 method for class 'result.goldfish'
logLik(object, ..., avgPerEvent = FALSE)
Arguments
object |
an object of class |
... |
additional arguments to be passed. |
avgPerEvent |
a logical value indicating whether the average likelihood per event should be calculated. |
Details
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.
Value
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:
df |
degrees of freedom with the number of estimated parameters in the model |
nobs |
the number of observations used in estimation.
In general, it corresponds to the number of dependent events used in
estimation. For a |
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.