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

models using Pareto smoothed importance sampling.

## Arguments

- x
[

`dynamitefit`

]

The model fit object.- L
[

`integer(1)`

]

Positive integer defining how many time points should be used for the initial fit.- verbose
[

`logical(1)`

]

If`TRUE`

(default), print the progress of the LFO computations to the console.- k_threshold
[

`numeric(1)`

]

Threshold for the Pareto k estimate triggering refit. Default is 0.7.- ...
Additional arguments passed to

`rstan::sampling()`

or`cmdstanr::sample()`

, such as`chains`

and`cores`

(`parallel_chains`

in`cmdstanr`

).

## Value

An `lfo`

object which is a `list`

with the following components:

`ELPD`

Expected log predictive density estimate.`ELPD_SE`

Standard error of ELPD. This is a crude approximation which does not take into account potential serial correlations.`pareto_k`

Pareto k values.`refits`

Time points where model was re-estimated.`L`

L value used in the LFO estimation.`k_threshold`

Threshold used in the LFO estimation.

## Details

For multichannel models, the log-likelihoods of all channels are combined. For models with groups, expected log predictive densities (ELPDs) are computed independently for each group, but the re-estimation of the model is triggered if pareto k values of any group exceeds the threshold.

## References

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

## See also

Model diagnostics
`loo.dynamitefit()`

,
`mcmc_diagnostics()`

## Examples

```
# \donttest{
# Please update your rstan and StanHeaders installation before running
# on Windows
if (!identical(.Platform$OS.type, "windows")) {
# this gives warnings due to the small number of iterations
out <- suppressWarnings(lfo(gaussian_example_fit, L = 20))
out$ELPD
out$ELPD_SE
}
#> Estimating model with 20 time points.
#> Estimating model with 22 time points.
#> Estimating model with 24 time points.
#> Estimating model with 27 time points.
#> [1] 17.31789
# }
```