AICc

A dark objectThis object must have at least the following elements:
<tabular>ll</tabular>{
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 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.

~kwd1

~kwd2

The recovery of visual sensitivity in a dark environment is known as dark 
 adaptation.  In a clinical or research setting the recovery is typically 
 measured after a dazzling flash of light and can be described by the Mahroo, 
 Lamb and Pugh (MLP) model of dark adaptation. The functions in this package 
 take dark adaptation data and use nonlinear regression to find the parameters  
 of the model that 'best' describe the data. They do this by firstly, 
 generating rapid initial objective estimates of data adaptation parameters, 
 then a multi-start algorithm is used to reduce the possibility of a local 
 minimum. There is also a bootstrap method to calculate parameter confidence 
 intervals. The functions rely upon a 'dark' list or object. This object is 
 created as the first step in the workflow and parts of the object are 
 updated as it is processed.

AICc: Akaike information criterion

Description

Usage

Arguments

Value

References

See Also

Examples