Get the Bayesian information criterion (BIC) for a fitted `pk` object
# S3 method for pk
BIC(object, newdata = NULL, model = NULL, method = NULL, exclude = TRUE, ...)A data.frame with log-likelihood values and calculated BIC using `newdata`. There is one row for each model in `obj`'s [stat_model()] element and each [optimx::optimx()] method (specified in [settings_optimx()]).
A `pk` object
Optional: A `data.frame` with new data for which to compute log-likelihood. If NULL (the default), then BICs will be computed for the data in `obj$data`. `newdata` is required to contain at least the following variables: `Time`, `Time.Units`, `Dose`, `Route`,`Media`, `Conc`, `Detect`, `N_Subjects`. Before log-likelihood is calculated, `Time` will be transformed according to the transformation in `obj$scales$time` and `Conc` will be transformed according to the transformation in `obj$scales$conc`.
Optional: Specify one or more of the fitted models for which to calculate BIC. If NULL (the default), log-likelihoods will be returned for all of the models in `obj$stat_model`.
Optional: Specify one or more of the [optimx::optimx()] methods for which to calculate BICs. If NULL (the default), log-likelihoods will be returned for all of the methods in `obj$settings_optimx$method`.
Logical: `TRUE` to compute the AIC after removing any observations in the data marked for exclusion (if there is a variable `exclude` in the data, an observation is marked for exclusion when `exclude status. Default `TRUE`.
Additional arguments. Not in use.
Caroline Ring, Gilberto Padilla Mercado
The BIC is calculated from the log-likelihood (LL) as follows: $$\textrm{BIC} = -2\textrm{LL} + \log(n_{obs}) n_{par}$$
where \(n_{par}\) is the number of parameters in the fitted model.
Note that the BIC is just the AIC with \(k = \log(n_{obs})\).
Other fit evaluation metrics:
AAFE.pk(),
AFE.pk(),
AIC.pk(),
logLik.pk(),
rmse.pk(),
rsq.pk()
Other log likelihood functions:
AIC.pk(),
logLik.pk()
Other methods for fitted pk objects:
AAFE.pk(),
AFE.pk(),
AIC.pk(),
coef.pk(),
coef_sd.pk(),
eval_tkstats.pk(),
get_fit.pk(),
get_hessian.pk(),
get_tkstats.pk(),
logLik.pk(),
predict.pk(),
residuals.pk(),
rmse.pk(),
rsq.pk()