InformationCriteria: Corrected Akaike's Information Criterion and Bayesian...

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

This function extracts AICc / BICc from models. It can be applied to wide variety of models that use logLik() and nobs() methods (including the popular lm, forecast, smooth classes).

Usage

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AICc(object, ...)

BICc(object, ...)

Arguments

object

Time series model.

...

Some stuff.

Details

AICc was proposed by Nariaki Sugiura in 1978 and is used on small samples for the models with normally distributed residuals. BICc was derived in McQuarrie (1999) and is used in similar circumstances.

IMPORTANT NOTE: both of the criteria can only be used for univariate models (regression models, ARIMA, ETS etc) with normally distributed residuals!

Value

This function returns numeric value.

Author(s)

Ivan Svetunkov, [email protected]

References

See Also

AIC, BIC

Examples

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xreg <- cbind(rnorm(100,10,3),rnorm(100,50,5))
xreg <- cbind(100+0.5*xreg[,1]-0.75*xreg[,2]+rnorm(100,0,3),xreg,rnorm(100,300,10))
colnames(xreg) <- c("y","x1","x2","Noise")

ourModel <- stepwise(xreg)

AICc(ourModel)
BICc(ourModel)

greybox documentation built on Dec. 11, 2019, 1:08 a.m.