AICc {qpcR}R Documentation

Akaike's second-order Information Criterion for small sample numbers

Description

Calculates the second-order (corrected) Akaike information criterion for 'drc' objects. This is a modified version of the original AIC which compensates for bias with small n. As qPCR data usually has n/par < 40 (see original reference), AICc was implemented to correct for this.

Usage

  AICc(object)

Arguments

object an object of class 'drc'.

Details

Extends the AIC such that AICc = AIC+frac{2k(k + 1)}{n - k - 1}, with k = number of parameters + 1, and n = number of observations. For large n, AICc converges to AIC.

Value

The second-order AIC value.

Author(s)

Andrej-Nikolai Spiess

References

Sakamoto, Y., Ishiguro, M., and Kitagawa G. (1986). Akaike Information Criterion Statistics. D. Reidel Publishing Company.

Hurvich CM & Tsai CL (1989) Regression and Time Series Model Selection in Small Samples Biometrika 76, 297-307.

See Also

AIC, logLik.

Examples

m <- multdrc(F1.1 ~ Cycles, data = reps, fct = l5())
AICc(m)

[Package qpcR version 1.0-6 Index]