AICc: Akaike information criterion

In emkayoh/Dark: The Analysis of Dark Adaptation Data

Akaike information criterion

Description

The Akaike information criterion corrected for small sample size is a measure of the relative quality of a model. The AICc is calculated from a 'dark' object.

Usage

AICc(obj)

Arguments

obj	A dark object This object must have at least the following elements: obj$time to calculate the number of observations obj$Pn the number of parameters in the model obj$val the sum of squared residual error

Value

The value returned is an indication of the information lost by fitting a particular model to the data, and is only of merit when compared to the value from another model.

Author(s)

Jeremiah MF Kelly

Mumac Ltd, SK7 6NR, GB

References

See https://en.wikipedia.org/wiki/Akaike_information_criterion.

K. Burnham and D. Anderson. Model selection and multi-model inference: a practical information- theoretic approach. Springer, 2002.

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

See Also

AIC

Examples

AICc(dark)

emkayoh/Dark documentation built on Jan. 22, 2025, 3:17 a.m.