Compare fitted models based on ELPD.

By default the print method shows only the most important information. Use
`print(..., simplify=FALSE)`

to print a more detailed summary.

- x
An object of class

`"loo"`

or a list of such objects.- ...
Additional objects of class

`"loo"`

.- digits
For the print method only, the number of digits to use when printing.

- simplify
For the print method only, should only the essential columns of the summary matrix be printed? The entire matrix is always returned, but by default only the most important columns are printed.

A matrix with class `"compare.loo"`

that has its own
print method. See the **Details** section.

When comparing two fitted models, we can estimate the difference in their
expected predictive accuracy by the difference in
`elpd_loo`

or `elpd_waic`

(or multiplied by \(-2\), if
desired, to be on the deviance scale).

When using `loo_compare()`

, the returned matrix will have one row per model
and several columns of estimates. The values in the
`elpd_diff`

and `se_diff`

columns of the
returned matrix are computed by making pairwise comparisons between each
model and the model with the largest ELPD (the model in the first row). For
this reason the `elpd_diff`

column will always have the value `0`

in the
first row (i.e., the difference between the preferred model and itself) and
negative values in subsequent rows for the remaining models.

To compute the standard error of the difference in ELPD ---
which should not be expected to equal the difference of the standard errors
--- we use a paired estimate to take advantage of the fact that the same
set of \(N\) data points was used to fit both models. These calculations
should 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.
Sivula et al. (2022) discuss the conditions when the normal
approximation used for SE and `se_diff`

is good.

If more than \(11\) models are compared, we internally recompute the model differences using the median model by ELPD as the baseline model. We then estimate whether the differences in predictive performance are potentially due to chance as described by McLatchie and Vehtari (2023). This will flag a warning if it is deemed that there is a risk of over-fitting due to the selection process. In that case users are recommended to avoid model selection based on LOO-CV, and instead to favor model averaging/stacking or projection predictive inference.

Vehtari, A., Gelman, A., and Gabry, J. (2017a). Practical Bayesian model
evaluation using leave-one-out cross-validation and WAIC.
*Statistics and Computing*. 27(5), 1413--1432. doi:10.1007/s11222-016-9696-4
(journal version,
preprint arXiv:1507.04544).

Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2022). Pareto smoothed importance sampling. preprint arXiv:1507.02646

Sivula, T, Magnusson, M., Matamoros A. A., and Vehtari, A. (2022). Uncertainty in Bayesian leave-one-out cross-validation based model comparison. preprint arXiv:2008.10296v3..

McLatchie, Y., and Vehtari, A. (2023). Efficient estimation and correction of selection-induced bias with order statistics. preprint arXiv:2309.03742

The FAQ page on the

**loo**website for answers to frequently asked questions.

```
# very artificial example, just for demonstration!
LL <- example_loglik_array()
loo1 <- loo(LL) # should be worst model when compared
loo2 <- loo(LL + 1) # should be second best model when compared
loo3 <- loo(LL + 2) # should be best model when compared
comp <- loo_compare(loo1, loo2, loo3)
print(comp, digits = 2)
#> elpd_diff se_diff
#> model3 0.00 0.00
#> model2 -32.00 0.00
#> model1 -64.00 0.00
# show more details with simplify=FALSE
# (will be the same for all models in this artificial example)
print(comp, simplify = FALSE, digits = 3)
#> elpd_diff se_diff elpd_loo se_elpd_loo p_loo se_p_loo looic se_looic
#> model3 0.000 0.000 -19.589 4.284 3.329 1.152 39.178 8.568
#> model2 -32.000 0.000 -51.589 4.284 3.329 1.152 103.178 8.568
#> model1 -64.000 0.000 -83.589 4.284 3.329 1.152 167.178 8.568
# can use a list of objects
loo_compare(x = list(loo1, loo2, loo3))
#> elpd_diff se_diff
#> model3 0.0 0.0
#> model2 -32.0 0.0
#> model1 -64.0 0.0
# \dontrun{
# works for waic (and kfold) too
loo_compare(waic(LL), waic(LL - 10))
#> Warning:
#> 3 (9.4%) p_waic estimates greater than 0.4. We recommend trying loo instead.
#> Warning:
#> 3 (9.4%) p_waic estimates greater than 0.4. We recommend trying loo instead.
#> elpd_diff se_diff
#> model1 0.0 0.0
#> model2 -320.0 0.0
# }
```