This function calculates the Akaike information criterion from the fitted model object generated by mlds from the formula $-2*log(likelihood) + k*npar$, where $npar$ represents the number of parameters in the fitted model and $k = 2$ for the usual AIC or $k = log(n)$ ($n$ the number of observations for the so-called BIC or SBC (Schwarz's Bayesian criterion).
Usage
## S3 method for class 'mlds':
AIC(object, ..., k = 2)
Arguments
object
an object of class mlds.
...
not used for the moment
k
numeric, the penalty per parameter to be used, the default k = 2 is the classical AIC.
Value
Returns a numeric value with the corresponding AIC (or BIC, or ..., depending on k).
Details
The method depends on the logLik.mlds method computing the log-likelihood for the mlds class. The smaller the AIC, the better the fit. The log-likelihood and hence the AIC is only defined up to an additive constant.
References
Sakamoto, Y., Ishiguro, M., and Kitagawa G. (1986). Akaike Information Criterion Statistics. D. Reidel Publishing Company.