Description Usage Arguments Details Value References Examples

View source: R/compare_models.R

Compare fitted models on LOO or WAIC.

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`...` |
At least two objects returned by |

`x` |
A list of at least two objects returned by |

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

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

or
`elpd_waic`

(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. 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 vector or matrix with class `'compare.loo'`

that has its own
print method. If exactly two objects are provided in `...`

or
`x`

, 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.* If more than two objects are provided then a
matrix of summary information is returned.

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.
(published
version, arXiv preprint).

Vehtari, A., Gelman, A., and Gabry, J. (2017b). Pareto smoothed importance sampling. arXiv preprint: http://arxiv.org/abs/1507.02646/

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loo documentation built on April 11, 2018, 5:04 p.m.

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