AIC.mlds {MLDS} | R Documentation |
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).
## S3 method for class 'mlds': AIC(object, ..., k = 2)
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. |
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.
Returns a numeric value with the corresponding AIC (or BIC, or ..., depending on k)
.
Kenneth Knoblauch
Sakamoto, Y., Ishiguro, M., and Kitagawa G. (1986). Akaike Information Criterion Statistics. D. Reidel Publishing Company.
data(AutumnLab) AIC(mlds(AutumnLab, method = "optim", opt.init = c(seq(0, 1, len = 10), 0.2)))