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 |
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.
1 2 3 4 5 6 | # 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)
|
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