AIC.gkwfit {gkwreg} | R Documentation |
Calculate AIC or BIC for gkwfit Objects
Description
Computes the Akaike Information Criterion (AIC) or variants like the Bayesian
Information Criterion (BIC) for one or more fitted model objects of class "gkwfit"
.
Usage
## S3 method for class 'gkwfit'
AIC(object, ..., k = 2)
Arguments
object |
An object of class |
... |
Optionally, more fitted model objects of class |
k |
Numeric scalar specifying the penalty per parameter. The default |
Details
This function calculates an information criterion based on the formula
-2 \times \log Likelihood + k \times df
, where 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
and the degrees of freedom for each model.
When comparing multiple models fitted to the same data, the model with the lower AIC or BIC value is generally preferred. The function returns a sorted data frame to facilitate this comparison when multiple objects are provided.
Value
If only one
object
is provided: A single numeric value representing the calculated criterion (AIC or 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 criterion value (namedAIC
, regardless of the value ofk
). The data frame is sorted in ascending order based on the criterion values. Row names are derived from the deparsed calls of the fitted models.
Author(s)
Lopes, J. E.
See Also
gkwfit
, AIC
, logLik.gkwfit
, BIC.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 AIC for a single model
aic1 <- AIC(fit1_kw)
print(aic1)
# Compare AIC values for multiple models
aic_comparison <- c(AIC(fit1_kw), AIC(fit2_bkw), AIC(fit3_gkw))
print(aic_comparison)