lcc (version 3.2.2)

AIC.lcc: Akaike and Bayesian Information Criteria for an lcc Object

Description

Calculates the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC) for a fitted longitudinal concordance correlation model represented by an lcc object.

Calculates the Bayesian Information Criterion (BIC) for a fitted longitudinal concordance correlation model represented by an lcc object. BIC is used for model selection, with lower values indicating a better model.

Usage

# S3 method for lcc
AIC(object, ..., k = 2)
# S3 method for lcc
BIC(object, ...)

Arguments

object

An object of class lcc, representing a fitted longitudinal concordance correlation function.

...

Optional arguments passed to the underlying BIC function from the stats package.

k

Numeric value used as a penalty coefficient for the number of parameters in the fitted model; the default k = 2 corresponds to the classical AIC.

Details

The function computes AIC or BIC values as a measure of the relative quality of statistical models for a given set of data. Lower AIC or BIC values indicate a better model fit with fewer parameters. For more information, refer to the methods for AIC objects.

The function computes BIC as a measure of the trade-off between model fit and complexity. It is particularly useful for comparing models with different numbers of parameters. For more information, refer to the documentation for BIC.

See Also

lcc, summary.lcc, coef.lcc, vcov.lcc

lcc, summary.lcc, coef.lcc, vcov.lcc, AIC.lcc

Examples

if (FALSE) {
fm1 <- lcc(data = hue, subject = "Fruit", resp = "H_mean",
           method = "Method", time = "Time", qf = 2, qr = 2)
AIC(fm1)
}

if (FALSE) {
attach(simulated_hue)
fm6 <- lcc(data = simulated_hue, subject = "Fruit",
           resp = "Hue", method = "Method", time = "Time",
           qf = 2, qr = 1, components = TRUE,
           time_lcc = list(n=50, from=min(Time), to=max(Time)))
AIC(fm6)
BIC(fm6)
}