arviz.reloo(wrapper, loo_orig=None, k_thresh=0.7, scale=None, verbose=True)[source]#

Recalculate exact Leave-One-Out cross validation refitting where the approximation fails.

az.loo estimates the values of Leave-One-Out (LOO) cross validation using Pareto Smoothed Importance Sampling (PSIS) to approximate its value. PSIS works well when the posterior and the posterior_i (excluding observation i from the data used to fit) are similar. In some cases, there are highly influential observations for which PSIS cannot approximate the LOO-CV, and a warning of a large Pareto shape is sent by ArviZ. This cases typically have a handful of bad or very bad Pareto shapes and a majority of good or ok shapes.

Therefore, this may not indicate that the model is not robust enough nor that these observations are inherently bad, only that PSIS cannot approximate LOO-CV correctly. Thus, we can use PSIS for all observations where the Pareto shape is below a threshold and refit the model to perform exact cross validation for the handful of observations where PSIS cannot be used. This approach allows to properly approximate LOO-CV with only a handful of refits, which in most cases is still much less computationally expensive than exact LOO-CV, which needs one refit per observation.

wrapper: SamplingWrapper-like

Class (preferably a subclass of az.SamplingWrapper, see Wrappers for details) implementing the methods described in the SamplingWrapper docs. This allows ArviZ to call any sampling backend (like PyStan or emcee) using always the same syntax.

loo_origELPDData, optional

ELPDData instance with pointwise loo results. The pareto_k attribute will be checked for values above the threshold.

k_threshfloat, optional

Pareto shape threshold. Each pareto shape value above k_thresh will trigger a refit excluding that observation.

scalestr, optional

Only taken into account when loo_orig is None. See az.loo for valid options.


ELPDData instance containing the PSIS approximation where possible and the exact LOO-CV result where PSIS failed. The Pareto shape of the observations where exact LOO-CV was performed is artificially set to 0, but as PSIS is not performed, it should be ignored.


Sampling wrappers are an experimental feature in a very early stage. Please use them with caution.


It is strongly recommended to first compute az.loo on the inference results to confirm that the number of values above the threshold is small enough. Otherwise, prohibitive computation time may be needed to perform all required refits.

As an extreme case, artificially assigning all pareto_k values to something larger than the threshold would make reloo perform the whole exact LOO-CV. This is not generally recommended nor intended, however, if needed, this function can be used to achieve the result.