BIC.gkwfit: Calculate Bayesian Information Criterion (BIC) for gkwfit...

In gkwreg: Generalized Kumaraswamy Regression Models for Bounded Data

Calculate Bayesian Information Criterion (BIC) for gkwfit Objects

Description

Computes the Bayesian Information Criterion (BIC), sometimes called the Schwarz criterion (SIC), for one or more fitted model objects of class "gkwfit".

Usage

## S3 method for class 'gkwfit'
BIC(object, ...)

Arguments

object	An object of class "gkwfit", typically the result of a call to gkwfit.
...	Optionally, more fitted model objects of class "gkwfit".

Details

This function calculates the BIC based on the formula -2 \times \log Likelihood + \log(n) \times df, where n is the number of observations and df represents the number of estimated parameters in the model (degrees of freedom).

It relies on the logLik.gkwfit method to extract the log-likelihood, the degrees of freedom (df), and the number of observations (nobs) for each model. Ensure that logLik.gkwfit is defined and returns a valid "logLik" object with appropriate attributes.

When comparing multiple models fitted to the same data, the model with the lower BIC value is generally preferred, as BIC tends to penalize model complexity more heavily than AIC for larger sample sizes. The function returns a sorted data frame to facilitate this comparison when multiple objects are provided. A warning is issued if models were fitted to different numbers of observations.

Value

• If only one object is provided: A single numeric value, the calculated BIC.

• If multiple objects are provided: A data.frame with rows corresponding to the models and columns for the degrees of freedom (df) and the calculated BIC value (named BIC). The data frame is sorted in ascending order based on the BIC values. Row names are generated from the deparsed calls or the names of the arguments passed to BIC.

Author(s)

Lopes, J. E. (with refinements)

See Also

gkwfit, BIC, logLik.gkwfit, AIC.gkwfit

Examples

set.seed(2203)
y <- rkw(1000, alpha = 2.5, beta = 1.5)

# Fit different models to the same data
fit1_kw <- gkwfit(y, family = "kw", silent = TRUE)
fit2_bkw <- gkwfit(y, family = "bkw", silent = TRUE)
fit3_gkw <- gkwfit(y, family = "gkw", silent = TRUE)

# Calculate BIC for a single model
bic1 <- BIC(fit1_kw)
print(bic1)

# Compare BIC values for multiple models
bic_comparison <- c(BIC(fit1_kw), BIC(fit2_bkw), BIC(fit3_gkw))
print(bic_comparison)

gkwreg documentation built on April 16, 2025, 1:10 a.m.