waic: Watanabe Information Criterion

Description

Calculates two versions of the Watanabe information criteria from MCMC draws.

Usage

waic(logliks, print = TRUE)

Value

Returns the two version of the WAIC

Arguments

logliks: a matrix of observation level log-likelihood values, the columns are MCMC iterations and the rows are observations in the data
print: logical whether to print the results

References

Gelman, A., Hwang, J., and Vehtari, A. (2014). Understanding predictive information criterion for Bayesian models. Stat Comput, 24, 997-1016.

Examples

Run this code

# Plasma data analysis

# recoding variables
data(plasma)
plasma$Sex <- as.factor(plasma$Sex)
plasma$SmokStat <- as.factor(plasma$SmokStat)
plasma$VitUse <- 3 - plasma$VitUse
plasma$VitUse <- as.factor(plasma$VitUse)

# Model fitting with 40 posterior samples from 80 iterations (thin = 2) is for
# illustration only. For practical model fitting, increase iterations, 
# e.g. nsamp = 500, thin = 20
fit.qrj <- qrjoint(BetaPlasma ~ Age + Sex + SmokStat + Quetelet + VitUse + Calories + 
        Fat + Fiber + Alcohol + Cholesterol + BetaDiet, plasma, nsamp = 40, thin = 2)
summary(fit.qrj, more = TRUE)

# the call to summary already shows the waic for the fitted model, it also returns 
# the observation level log-likelihood vales. To calculate waic from last 20 draws 
# we can use:

if (FALSE) {
summary(fit.qrj, more = TRUE)
ll <- sm$ll
waic(ll[,21:40])
}