logLik.result.goldfish function

Extract log-likelihood from a fitted model object

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 class 'result.goldfish'
logLik(object, ..., avgPerEvent = FALSE)

Arguments

• object: an object of class result.goldfish output from an estimate call with a fitted model.
• ...: additional arguments to be passed.
• avgPerEvent: a logical value indicating whether the average likelihood per event should be calculated.

Returns

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 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.

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.