DIC: Computes the Deviance Information Criterion (DIC) for a...
In solvsa/BayesPLS: Bayesian estimation in PLS regression

Usage Arguments Details Value Author(s) References Examples

View source: R/DIC.R

1	DIC(obj, start = 2, stop = length(obj$theta$solu))

`obj`	A BayesPLS object
`start`	Compute DIC starting with this iteration. Note, the value refers to iteration number after thinning.
`stop`	Compute DIC ending with this iteration. Note, the value refers to iteration number after thinning.

DIC accoring to Spiegelhalter et al. (2002)

`dic`	The DIC value
`pd`	The estimated effective number of parameters estimated in the model.

Solve S<c3><a6>b<c3><b8>

Spiegelhalter, David J.; Best, Nicola G.; Carlin, Bradley P.; van der Linde, Angelika (2002). "Bayesian measures of model complexity and fit (with discussion)". Journal of the Royal Statistical Society, Series B 64 (4)

library(simrel)
set.seed(1430)
sim <- simrel(n=30, p=50, q=50, relpos=c(1,3), gamma=0.5, R2=0.8, muY=3, muX=rnorm(50,5,1), ntest=1000)
X <- sim$X
Y <- sim$Y

test <- BayesPLS(Y,X,3,scale=TRUE, totiter=500, start=20, dotrace=TRUE, adaptint=50, plotint=10)
est <- estimate(test, start=20)
pred <- predict(est, sim$TESTX)
windows()
plot(sim$TESTY, pred, xlab="Observed", ylab="Predicted", main="Prediction plot for test data")
curve(1*x, add=TRUE, col=2)

print(DIC(est,start=20))