waic.icfit.RdImplementation of waic() methods from loo to compute WAIC from the
pointwise log-likelihood.
# S3 method for icfit waic(X, ...) # S3 method for iclist waic(X, ...)
| X | Object of type |
|---|---|
| ... | Additional arguments. See |
A named list (of class c("waic", "loo")) with components:
estimatesA matrix with two columns ("Estimate", "SE") and three
rows ("elpd_waic", "p_waic", "waic"). This contains
point estimates and standard errors of the expected log pointwise predictive
density (elpd_waic), the effective number of parameters
(p_waic) and the information criterion waic (which is just
-2 * elpd_waic, i.e., converted to deviance scale).
pointwiseA matrix with three columns (and number of rows equal to the number of
observations) containing the pointwise contributions of each of the above
measures (elpd_waic, p_waic, waic).
For more details see loo. The developers of stan
recommend LOO-CV using PSIS (as implemented by the loo::loo()
function) because PSIS provides useful diagnostics as well as effective
sample size and Monte Carlo estimates.
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.