Description Usage Arguments Details Value References Examples

Estimates the leave-future-out (LFO) information criterion for `walker`

and `walker_glm`

models.

1 |

`object` |
Output of |

`L` |
Positive integer defining how many observations should be used for the initial fit. |

`exact` |
If |

`verbose` |
If |

`k_thres` |
Threshold for the pareto k estimate triggering refit. Default is 0.7. |

The LFO for non-Gaussian models is (currently) based on the corresponding Gaussian approximation and not the importance sampling corrected true posterior.

List with components `ELPD`

(Expected log predictive density), `ELPDs`

(observation-specific ELPDs),
`ks`

(Pareto k values in case of approximation was used), and `refits`

(time points where model was re-estimated)

Paul-Christian Bürkner, Jonah Gabry & Aki Vehtari (2020). Approximate leave-future-out cross-validation for Bayesian time series models, Journal of Statistical Computation and Simulation, 90:14, 2499-2523, DOI: 10.1080/00949655.2020.1783262.

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