BIC.gkwreg: Bayesian Information Criterion for GKw Regression Models

In gkwreg: Generalized Kumaraswamy Regression Models for Bounded Data

Bayesian Information Criterion for GKw Regression Models

Description

Calculates the Bayesian Information Criterion (BIC), also known as Schwarz's Bayesian Criterion (SBC), for one or more fitted Generalized Kumaraswamy (GKw) regression model objects (class "gkwreg"). BIC is used for model selection and tends to penalize model complexity more heavily than AIC, especially for larger datasets.

Usage

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

Arguments

object	An object of class "gkwreg", typically the result of a call to gkwreg.
...	Optionally, one or more additional fitted model objects of class "gkwreg", for which BIC should also be calculated.

Details

The BIC is calculated based on the maximized log-likelihood (L), the number of estimated parameters (p) in the model, and the number of observations (n):

BIC = -2 \log(L) + p \times \log(n)

This function retrieves the log-likelihood, the number of parameters (df), and the number of observations (nobs) using the logLik.gkwreg method for the fitted gkwreg object(s).

Models with lower BIC values are generally preferred. The penalty term p \log(n) increases more rapidly with sample size n compared to AIC's penalty 2p, meaning BIC favors simpler models more strongly in larger samples. BIC can be motivated from a Bayesian perspective as an approximation related to Bayes factors.

When comparing multiple models passed via ..., the function relies on BIC's default method for creating a comparison table, which in turn calls logLik for each provided object.

Value

If just one object is provided, returns a single numeric BIC value. If multiple objects are provided via ..., returns a data.frame with rows corresponding to the models and columns for the degrees of freedom (df) and the BIC values, sorted by BIC.

Author(s)

Lopes, J. E.

References

Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461-464.

See Also

gkwreg, logLik.gkwreg, AIC.gkwreg, BIC

Examples

# Assume 'df' exists with response 'y' and predictors 'x1', 'x2', 'x3'
# and that rkw() is available and data is appropriate (0 < y < 1).
set.seed(123)
n <- 100
x1 <- runif(n)
x2 <- rnorm(n)
x3 <- factor(rbinom(n, 1, 0.4))
alpha <- exp(0.5 + 0.2 * x1)
beta <- exp(1.0 - 0.1 * x2 + 0.3 * (x3 == "1"))
y <- rkw(n, alpha = alpha, beta = beta) # Placeholder if rkw not available
y <- pmax(pmin(y, 1 - 1e-7), 1e-7)
df <- data.frame(y = y, x1 = x1, x2 = x2, x3 = x3)

# Fit two competing models
kw_reg1 <- gkwreg(y ~ x1 | x2, data = df, family = "kw")
kw_reg2 <- gkwreg(y ~ x1 | x2 + x3, data = df, family = "kw") # More complex beta model
kw_reg3 <- gkwreg(y ~ 1 | x2 + x3, data = df, family = "kw") # Simpler alpha model

# Calculate BIC for a single model
bic1 <- BIC(kw_reg1)
print(bic1)

# Compare models using BIC (lower is better)
model_comparison_bic <- c(BIC(kw_reg1), BIC(kw_reg2), BIC(kw_reg3))
print(model_comparison_bic)

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