AICc: Akaike information criterion
In Dark: The Analysis of Dark Adaptation Data
AICc R Documentation
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)
Dark documentation built on April 4, 2025, 3:52 a.m.
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| AICc | R Documentation |
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:
|
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)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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