AICc: Akaike's An Information Criterion with Correction
In USGS-R/rloadest: River Load Estimation

Description Usage Arguments Value Note References See Also Examples

Compute Akaike's An Information Criterion with Correction (AICc) for for finite sample sizes.

1	AICc(object)

object

the output from loadReg, or any object that has a logLik method.

A numeric value corresponding to the AICc of object.

The penalty that AIC applies for adding explanatory variables is biased low when the number of samples is small. As a result, models with small smaple sizes can be overfitted. AICc can be used to identify more parsimonious models.

Hurvitch, C.M. and Tsai, C.L., 1989, Regression and time series model selection in small samples: Biometrika, v. 76, no. 2, p. 297–307.

loadReg,

# From application 1 in the vignettes
data(app1.calib)
app1.lr <- loadReg(Phosphorus ~ model(1), data = app1.calib, 
 flow = "FLOW", dates = "DATES", conc.units="mg/L",
 station="Illinois River at Marseilles, Ill.")
AICc(app1.lr)