AICc: Akaike information criterion
In emkayoh/Dark: The Analysis of Dark Adaptation Data

Description Usage Arguments Value Author(s) References See Also Examples

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.

AICc(obj)

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

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.

Jeremiah MF Kelly

Faculty of Life Sciences, The University of Manchester, M13 9PL, UK

See http://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.

AIC