Akaike's Information Criterion

A function calculating Akaike's Information Criterion (AIC) based on the log-likelihood value extracted from logLik.izip, according to the formula -2log-likelihood + knpar, where npar represents the number of parameters in the fitted model, and k=2 for the usual AIC or k=log(n) (n being the number of observations) for the so-called BIC (Bayesian Information Criterion).

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

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

Arguments

object	an object class 'izip' or 'tsizip' object, obtained from a call to glm.izip or tsglm.izip.
...	other arguments passed to or from other methods (currently unused).
k	numeric: the penalty per parameter to be used; the default k = 2 is the classical AIC.

Value

A numeric value with the corresponding AIC (or BIC, or ..., depends on k).

Details

When comparing models fitted by maximum likelihood to the same data, the smaller the AIC or BIC, the better the fit.

See also

logLik.izip, nobs.izip, glm.izip, logLik.tsizip, nobs.tsizip, tsglm.izip

Examples

data(bioChemists)
M_bioChem <- glm.izip(art ~ ., data = bioChemists)
# AIC
AIC(M_bioChem)
#> [1] 3240.321

data(arson)
M_arson <- tsglm.izip(arson ~ 1, past_mean_lags = 1, past_obs_lags = c(1, 2))
# BIC
AIC(M_arson, k = log(nobs(M_arson)))
#> [1] 419.4006