1d:T6d0,# Watanabe-Akaike or Widely Available Information Criterion (WAIC) for objects of class `mtar` This function computes the Watanabe-Akaike or Widely Available Information Criterion (WAIC), for objects of class `mtar`. ```r ## S3 method for class 'mtar' WAIC(...) ``` ## Arguments - `...`: one or several objects of the class **mtar**. ## Returns A numeric matrix containing the WAIC values corresponding to each **mtar** object in the input. ## Examples ```r ###### Example 1: Returns of the closing prices of three financial indexes data(returns) fit1a <- mtar(~ COLCAP + BOVESPA | SP500, data=returns, row.names=Date, subset={Date<="2016-03-14"}, dist="Student-t", ars=ars(nregim=3,p=c(1,1,2)), n.burnin=2000, n.sim=3000, n.thin=2) fit1b <- update(fit1a,dist="Slash") fit1c <- update(fit1a,dist="Laplace") WAIC(fit1a,fit1b,fit1c) ###### Example 2: Rainfall and two river flows in Colombia data(riverflows) fit2a <- mtar(~ Bedon + LaPlata | Rainfall, data=riverflows, row.names=Date, subset={Date<="2009-04-04"}, dist="Laplace", ars=ars(nregim=3,p=5), n.burnin=2000, n.sim=3000, n.thin=2) fit2b <- update(fit2a,dist="Slash") fit2c <- update(fit2a,dist="Student-t") WAIC(fit2a,fit2b,fit2c) ###### Example 3: Temperature, precipitation, and two river flows in Iceland data(iceland.rf) fit3a <- mtar(~ Jokulsa + Vatnsdalsa | Temperature | Precipitation, data=iceland.rf, subset={Date<="1974-12-21"}, row.names=Date, ars=ars(nregim=2,p=15,q=4,d=2), n.burnin=2000, n.sim=3000, n.thin=2, dist="Slash") fit3b <- update(fit3a,dist="Laplace") fit3c <- update(fit3a,dist="Student-t") WAIC(fit3a,fit3b,fit3c) ``` ## See Also DIC1e:T49a,M7.49933 0.25C3.49635 0.25 0.25 3.49593 0.25 7.50024C0.25 10.703 2.32715 13.4206 5.2081 14.3797C5.57084 14.446 5.70302 14.2222 5.70302 14.0299C5.70302 13.8576 5.69679 13.4019 5.69323 12.797C3.67661 13.235 3.25112 11.825 3.25112 11.825C2.92132 10.9874 2.44599 10.7644 2.44599 10.7644C1.787

WAIC.mtar function