Widely Applicable Information Criteria

Source: R/methods.R

Widely Application Information Criteria (WAIC) for model comparison

waic(fit, pointwise = FALSE, digits = 2)

Source

Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely application information criterion in singular learning theory. Journal of Machine Learning Research 11, 3571-3594.

Arguments

fit

An surveil object

pointwise

Logical (defaults to FALSE); if pointwise = TRUE, a vector of values for each observation will be returned.

digits

Round results to this many digits.

Value

A vector of length 3 with WAIC, a rough measure of the effective number of parameters estimated by the model Eff_pars, and log predictive density Lpd. If pointwise = TRUE, results are returned in a data.frame.

Examples


data(msa)
austin <- msa[grep("Austin", msa$MSA), ]
austin.w <- austin[grep("White", austin$Race),]
fit <- stan_rw(austin.w, time = Year,
               chains = 2, iter = 1200) # for speed only
waic(fit)