Compute Bayesian information criterion (BIC) or Schwarz's Bayesian criterion (SBC) for possibly one or several objects.
# S3 method for cox_cure
BIC(object, ..., method = c("obs", "effective"))# S3 method for cox_cure_uncer
BIC(object, ..., method = c("obs", "certain-event"))
An object for a fitted model.
Other objects of the same class.
A character string specifying the method for computing the BIC
values. Notice that this argument is placed after ... and thus
must be specified as a named argument. The available options for
cox_cure objects are "obs" for regular BIC based on the
number of observations, and "effective" for using BIC based on
the number of effective sample size for censored data (number of
uncensored events) proposed by Volinsky and Raftery (2000). The
available options for cox_cure_uncer objects are "obs" for
regular BIC based on the number of observations, and
"certain-event" for a variant of BIC based on the number of
certain uncensored events. For objects of either class, the former
method is used by default.
Volinsky, C. T., & Raftery, A. E. (2000). Bayesian information criterion for censored survival models. Biometrics, 56(1), 256--262.
# NOT RUN {
## See examples of function 'cox_cure'.
# }
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