BIC.pk {invivoPKfit}R Documentation

Bayesian information criterion

Description

Get the Bayesian information criterion (BIC) for a fitted 'pk' object

Usage

## S3 method for class 'pk'
BIC(object, newdata = NULL, model = NULL, method = NULL, exclude = TRUE, ...)

Arguments

object

A 'pk' object

newdata

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'.

model

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'.

method

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'.

exclude

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.

Details

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}).

Value

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()]).

Author(s)

Caroline Ring, Gilberto Padilla Mercado

See Also

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()


[Package invivoPKfit version 2.0.1 Index]