AICc: Akaike's second-order corrected Information Criterion

In qpcR: Modelling and Analysis of Real-Time PCR Data

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.

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.

Value

The second-order corrected AIC value.

Author(s)

Andrej-Nikolai Spiess

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

m1 <- pcrfit(reps, 1, 2, l5)
AICc(m1)

Example output

Loading required package: MASS
Loading required package: minpack.lm
Loading required package: rgl
Loading required package: robustbase
Loading required package: Matrix
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE 
3: .onUnload failed in unloadNamespace() for 'rgl', details:
  call: fun(...)
  error: object 'rgl_quit' not found 
[1] -103.189

qpcR documentation built on May 2, 2019, 5:17 a.m.