loo_compare | R Documentation |
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.
loo_compare(x, ...)
## Default S3 method:
loo_compare(x, ...)
## S3 method for class 'compare.loo'
print(x, ..., digits = 1, simplify = TRUE)
## S3 method for class 'compare.loo_ss'
print(x, ..., digits = 1, simplify = TRUE)
x |
An object of class |
... |
Additional objects of class |
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. |
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.
A matrix with class "compare.loo"
that has its own
print method. See the Details section.
Vehtari, A., Gelman, A., and Gabry, J. (2017). 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. (2019). Pareto smoothed importance sampling. preprint arXiv:1507.02646
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, r_eff = NA) # should be worst model when compared
loo2 <- loo(LL + 1, r_eff = NA) # should be second best model when compared
loo3 <- loo(LL + 2, r_eff = NA) # should be best model when compared
comp <- loo_compare(loo1, loo2, loo3)
print(comp, digits = 2)
# show more details with simplify=FALSE
# (will be the same for all models in this artificial example)
print(comp, simplify = FALSE, digits = 3)
# can use a list of objects with custom names
# will use apple, banana, and cherry, as the names in the output
loo_compare(list("apple" = loo1, "banana" = loo2, "cherry" = loo3))
## Not run:
# works for waic (and kfold) too
loo_compare(waic(LL), waic(LL - 10))
## End(Not run)
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