akaike
fun akaikeInfoCriterionCorrected(sampleSize: Int, numParameters: Int, lnMax: Double): Double(source)
Computes the small-sample corrected form of Akaike's information criterion (AICc) based on the sample size sampleSize, the number of parameters estimated for the model numParameters, and the maximized value lnMax of the log-likelihood function of the model. That is, AIC + 2p(p + 1)/(n - p - 1), for n observations and p parameters. Smaller is better.
The correction is meaningful only when the sample size exceeds the number of parameters by more than one, and it vanishes as the sample size grows, so AICc approaches akaikeInfoCriterion for large samples.