AIC.mlds {MLDS}R Documentation

Extract AIC from Object of Class 'mlds'

Description

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.

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.

Value

Returns a numeric value with the corresponding AIC (or BIC, or ..., depending on k).

Author(s)

Kenneth Knoblauch

References

Sakamoto, Y., Ishiguro, M., and Kitagawa G. (1986). Akaike Information Criterion Statistics. D. Reidel Publishing Company.

See Also

logLik.mlds

Examples

data(AutumnLab)
AIC(mlds(AutumnLab, method = "optim", opt.init = c(seq(0, 1, len = 10), 0.2)))


[Package MLDS version 0.1-7 Index]