Akaike's second-order corrected Information Criterion
Calculates the second-order corrected Akaike Information Criterion for objects of class pcrfit, nls, lm, glm or any other models from which coefficients and residuals can be extracted. This is a modified version of the original AIC which compensates for bias with small n. As qPCR data usually has \frac{n}{k} < 40 (see original reference), AICc was implemented to correct for this.
AICc(object)
object |
a fitted model. |
Extends the AIC such that
AICc = AIC+\frac{2k(k + 1)}{n - k - 1}
with k = number of parameters, and n = number of observations. For large n, AICc converges to AIC.
The second-order corrected AIC value.
Andrej-Nikolai Spiess
Akaike Information Criterion Statistics.
Sakamoto Y, Ishiguro M and Kitagawa G.
D. Reidel Publishing Company (1986).
Regression and Time Series Model Selection in Small Samples.
Hurvich CM & Tsai CL.
Biometrika (1989), 76: 297-307.
m1 <- pcrfit(reps, 1, 2, l5) AICc(m1)
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