qpcR (version 1.4-1)

AICc: Akaike's second-order corrected Information Criterion

Description

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.

Usage

AICc(object)

Arguments

object

a fitted model.

Value

The second-order corrected AIC value.

Details

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.

References

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.

See Also

AIC, logLik.

Examples

# NOT RUN {
m1 <- pcrfit(reps, 1, 2, l5)
AICc(m1)
# }