loo (version 0.1.6)

compare: Model comparison

Description

Compare fitted models on LOO or WAIC

Usage

compare(...)

Arguments

...

At least two objects returned by loo or waic.

Value

A vector or matrix with class 'compare.loo'. If ... contains more than two objects then a matrix of summary information is returned. If ... contains exactly two objects then the difference in expected predictive accuracy and the standard error of the difference are returned (see Details). The difference will be positive if the expected predictive accuracy for the second model is higher.

Details

When comparing two fitted models, we can estimate the difference in their expected predictive accuracy by the difference in elpd_waic or elpd_loo (multiplied by $-2$, if desired, to be on the deviance scale). To compute the standard error of this difference we can use a paired estimate to take advantage of the fact that the same set of $N$ data points was used to fit both models. We think these calculations will be most useful when $N$ is large, because then non-normality of the distribution is not such an issue when estimating the uncertainty in these sums. These standard errors, for all their flaws, should give a better sense of uncertainty than what is obtained using the current standard approach of comparing differences of deviances to a Chi-squared distribution, a practice derived for Gaussian linear models or asymptotically, and which only applies to nested models in any case.

References

Vehtari, A., Gelman, A., and Gabry, J. (2016). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. http://arxiv.org/abs/1507.04544/ (preprint)

See Also

print.compare.loo

Examples

## Not run: 
# loo1 <- loo(log_lik1)
# loo2 <- loo(log_lik2)
# print(compare(loo1, loo2), digits = 3)
# 
# waic1 <- waic(log_lik1)
# waic2 <- waic(log_lik2)
# compare(waic1, waic2)
# ## End(Not run)